CN107219457B

CN107219457B - Frame-type circuit breaker fault diagnosis and degree assessment method based on operation attachment electric current

Info

Publication number: CN107219457B
Application number: CN201710450109.9A
Authority: CN
Inventors: 孙曙光; 张强; 杜太行; 王佳兴; 齐玲; 王岩
Original assignee: Hebei University of Technology
Current assignee: Hebei University of Technology
Priority date: 2017-06-15
Filing date: 2017-06-15
Publication date: 2019-05-31
Anticipated expiration: 2037-06-15
Also published as: CN107219457A

Abstract

The present invention relates to frame-type circuit breaker fault diagnosises and degree assessment method based on operation attachment electric current, this method first determines whether the working stage wait diagnose and assess breaker, the working stage includes energy storage stage, combined floodgate stage and separating brake stage, then carries out fault diagnosis to energy storage stage using energy storage motor electric current, carries out fault diagnosis and scale evaluation to the stage of combined floodgate using closing coil electric current and carries out fault diagnosis and scale evaluation to the separating brake stage using opening coil electric current；Energy storage motor current signal of the detection block pantograph disconnecting switch in energy storage stage, the closing coil current signal in the combined floodgate stage and the opening coil current signal in the separating brake stage respectively, fault diagnosis is carried out in combination with multi-kernel support vector machine, when diagnosis be out of order need to carry out fault degree assessment when, the judgement of fault degree can be accurately carried out by fault degree characteristic curve.

Description

Frame-type circuit breaker fault diagnosis and degree assessment method based on operation attachment electric current

Technical field

Technical solution of the present invention is related to the fault diagnosis and scale evaluation of breaker, specifically a kind of based on operation The frame-type circuit breaker fault diagnosis and degree assessment method of attachment electric current.

Background technique

With the rapid development of the national economy, ordinary users also start to put forward higher requirements power quality, low pressure Power distribution network is the end power grid of power transmission network, is connected directly with users, is particularly significant between connection power transmission network and user Tie, it is the key that electric system distributes to vast power consumer, adjusts electric energy, the O&M level of low-voltage network and strong Kang Chengdu often directly determines that the power quality of power consumer is horizontal.Key equipment frame-type circuit breaker in low-voltage network It is widely used at distribution system total input-wire or at important equipment, protection and control dual role is born, in power station equipment Quantity is more, and investment is big, and operational reliability is related to the safe operation of electric system, so to key equipment in low-voltage network One of frame-type circuit breaker study it is most important, carry out frame-type circuit breaker monitoring running state research, can send out in time Existing breaker mechanical failure, prevents accident, while the research for carrying out frame-type circuit breaker fault diagnosis technology can be very big Ground mitigates the labor intensity of service personnel, improves power supply reliability, automation, the level of IT application.

It is limited to detect the vibration signal and sound during breaker actuation currently, the research of circuit breaker failure diagnostic techniques Signal completes the fault diagnosis of breaker with this, such as (Sun Laijun, Hu Xiaoguang, Ji Yanchao one kind are based on vibration to Sun Laijun Fault Diagnosis for HV Circuit Breakers new method [J] Proceedings of the CSEE of signal, 2006,26 (6): 157-161.) with high pressure Vibration signal under breaker is non-loaded is that fingerprint signal carries out Fault Diagnosis for HV Circuit Breakers.And breaker operator attachment electric current Signal has many advantages, such as high stability, is easy to detect, and breaker energy storage, combined floodgate, separating brake action process are contained in synchronous signal In bulk information, be able to reflect many fault types of attachment, become one of important detection parameters of breaker.It is existing Research on Fault Diagnosis Technology focuses on fault location more, and, fault degree quantitative analysis energy less for the research of fault degree More accurate maintenance mode is provided for equipment.(Chen Bin, Yan Zhaoli, Cheng Xiaobin are based on SVDD and relative distance by such as Chen Bin Equipment fault degree predicts [J] Chinese journal of scientific instrument, 2011,32 (7): 1558-1563.) find out the vibration of rotor oscillation simulation table The statistics variations rule of signal spectrum curve, extracts the frequency domain character that can describe signal under faulty equipment different faults degree Relative distance is constructed, the ordered categorization of fault degree size is then carried out.Current most of researchs for fault degree are only It is limited to the ordered categorization of fault degree, i.e., identifies the failure of fixation degree using intelligent recognition algorithm, this qualitative analysis mode The not high constraint of recognizer accuracy cannot still be got rid of, it is easy to generate the misinterpretation to fault degree, gently then influence pair The assurance of working state of circuit breaker, it is heavy then cause power outage and heavy economic losses.

Summary of the invention

In view of the deficiencies of the prior art, the technical problems to be solved by the present invention are: providing a kind of based on operation attachment electricity The frame-type circuit breaker fault diagnosis and degree assessment method of stream.This method distinguishes detection block pantograph disconnecting switch in energy storage stage Energy storage motor current signal, the closing coil current signal in the combined floodgate stage and the opening coil electric current in the separating brake stage letter Number, in combination with multi-kernel support vector machine carry out fault diagnosis, when diagnosis be out of order need to carry out fault degree assessment when, lead to The judgement of fault degree can accurately be carried out by crossing fault degree characteristic curve, what this method breaker operator attachment can occur Mechanical breakdown is accurately and reliably diagnosed, while fault degree being rationally and effectively quantitatively evaluated.This method is to open circuit Device energy storage stage carries out fault diagnosis, carries out fault diagnosis and scale evaluation to combined floodgate, separating brake stage.Energy storage motor electric current is believed Number envelope line drawing is carried out, then extracts the material time of envelope, amplitude Characteristics parameter, in this, as local feature vectors, Set empirical mode decomposition (the Ensemble Empirical Mode of energy storage motor current signal is sought simultaneously Decomposition, EEMD) energy square is as global characteristics, the two combined structure fault feature vector, through Principal Component Analysis After dimensionality reduction, it is input to multi-kernel support vector machine and carries out fault diagnosis, when being diagnosed to be breaker and being in normal condition, without appointing Where reason, when the failure being diagnosed to be needs to repair immediately, makes alert process, notifies at maintenance personal immediately Reason.It is special by the material time, the current amplitude that extract divide-shut brake coil current when being monitored to circuit-breaker switching on-off state Sign and EEMD energy moment characteristics, combine and establish fault feature vector, input multicore branch after Principal Component Analysis merges dimensionality reduction It holds vector machine and carries out the diagnosis of divide-shut brake fault type, when the failure being diagnosed to be needs to carry out fault degree qualitative assessment, then count Point counting closing coil electric current EEMD energy square relative entropy, as fault degree evaluation index, in conjunction with the event having had built up Barrier degree characteristic curve, and then complete the qualitative assessment of fault degree.

The present invention solve the technical problem the technical solution adopted is that, provide it is a kind of based on operation attachment electric current frame The diagnosis of formula circuit breaker failure and degree assessment method, this method first determine whether the working stage wait diagnose and assess breaker, institute Stating working stage includes energy storage stage, combined floodgate stage and separating brake stage, is then carried out using energy storage motor electric current to energy storage stage Fault diagnosis carries out fault diagnosis and scale evaluation to the stage of combined floodgate using closing coil electric current and utilizes opening coil electric current pair The separating brake stage carries out fault diagnosis and scale evaluation；Wherein,

One, fault diagnosis is carried out to energy storage stage using energy storage motor electric current, comprising the following steps:

The first step acquires energy storage motor electric current S of the breaker under different working condition_cc(t), energy storage is set in the application 4 kinds of different working conditions that motor is shared normal, transmission gear bite, spring bite and spring fall off, under every kind of working condition Acquire r group energy storage motor current signal；

Second step is carried out noise suppression preprocessing to the energy storage motor current signal of acquisition, is obtained using mean filter Denoising Algorithm To denoising energy storage motor current signal S '_cc(t)；

Third step, by Hilbert envelope method to denoising energy storage motor current signal S '_cc(t) envelope is carried out to seek, The amplitude A (t) of denoising energy storage motor current signal is acquired according to formula (3), A (t) is to denoise energy storage motor current signal S '_cc (t) Hilbert envelope line；

4th step, by Hilbert envelope line to denoising energy storage motor current signal S '_cc(t) material time and electricity are carried out It flows magnitude parameters to extract, by current maxima I when starting in energy storage motor current signal₁, motor stabilizing operation electric current most Big value I₂And maximum value I₂Corresponding moment t₁, energy storage motor electric current continue total time t₂Local fault as energy storage motor electric current Feature obtains the local feature vectors component T that energy storage motor electric current is formed in time domain₁=[I₁, I₂, t₁, t₂]；

5th step, to denoising energy storage motor current signal S '_cc(t) it is special to carry out set empirical mode decomposition (EEMD) energy square Sign is extracted, i.e., global characteristics extract；

EEMD energy Moment Feature Extraction comprises the concrete steps that:

5-1., which is determined, decomposes number M and the noise amplitude to be added, by white noise signal n_m(t) it is added to point The denoising energy storage motor current signal S ' of solution_cc(t) in, new signal x to be decomposed is obtained according to formula (5)_m(t):

x_m(t)=S '_cc(t)+n_m(t) (5)

5-2. determines new signal x to be decomposed_m(t) Local modulus maxima and minimum point, utilize cubic spline interpolation Whole maximum points is attached, coenvelope line is formed；Similarly, cubic spline interpolation is carried out to all minimum points, obtained To lower envelope line；

5-3. finds out the average value of all envelope points of coenvelope line and lower envelope line, is denoted as m1；

5-4. is by new signal x to be decomposed_m(t) m1 is subtracted, obtains one-component h according to formula (6)₁If: h₁Meet IMF Component condition, then h₁It is just new signal x to be decomposed_m(t) first intrinsic mode (IMF) component for meeting IMF component condition, First intrinsic modal components for meeting IMF component condition is denoted as c₁, enter step 5-6；

h₁=x_m(t)-m₁ (6)

If 5-5. h₁It is unsatisfactory for IMF component condition, and includes the odd function of other different scales, then by h₁As original letter Number, step 5-2~5-4 is repeated, h is obtained₁Upper and lower envelope, calculate h₁Coenvelope average value, be denoted as m₁₁；Then sentence Disconnected h₁₁=h₁-m₁₁IMF component condition can be met to continue cycling through if be not met by, until h_1k=h_1(k-1)-m_1kIt is full Until sufficient IMF component condition；By h_1kAs new signal x to be decomposed_m(t) first meets the eigen mode of IMF component condition State component, is denoted as c₁；

5-6. is c₁From new signal x to be decomposed_m(t) it independently goes out in, obtains r according to formula (7)₁:

r₁=x_m(t)-c₁ (7)

5-7. is by r₁Step 5-2~5-5 is repeated as original signal to be decomposed, obtains second IMF component c₂；

5-8. repeats step 5-2~5-7, obtains new signal x to be decomposed according to formula (8)_m(t) m IMF component, when Residual components r_mWhen for monotonic function, then stop repeating step 5-2~5-7, complete it is primary decompose,

At this point, new signal x to be decomposed_m(t) it is indicated with formula (9)；

In formula, r_mIt is survival function, the average tendency of representation signal；c_iFor i-th of IMF component；

The amplitudes noise signal such as new is added to the denoising energy storage motor current signal S ' decomposed by 5-9._cc (t) in, it is repeated in step 5-2~5-8M times, obtains M group IMF vector sequence { c_n,l, c_n,lFirst decomposed for n-th IMF component, n=1,2 ... ..., M, l=1,2 ... ..., m；

5-10. seeks M group IMF vector sequence { c according to formula (10)_n,lFirst of IMF component M times decomposition average valueIt willAs first of IMF component of EEMD, i.e., it is by the IMF component that EEMD is obtained

5-11. is according to formula (11) to the IMF component obtained by EEMDIt carries out that energy square is taken to calculate:

In formula: Δ t is sampling time interval, n₁For sampling number, k₁=1,2 ..., n₁；

5-12. new signal x to be decomposed_m(t) it is decomposed into m IMF component, acquires m energy square E altogether_m, then energy storage motor The global characteristics component of a vector T that electric current is formed in time-frequency domain₂=[E₁, E₂... ..., E_m]；

6th step establishes energy storage motor fault feature vector, constructs energy storage motor sample dimensionality reduction eigenmatrix:

The local feature vectors component T that energy storage motor electric current is formed in time domain is acquired by the 4th step₁=[I₁, I₂, t₁, t₂] and the 5th step acquire the global characteristics component of a vector T that energy storage motor electric current is formed in time-frequency domain₂=[E₁, E₂... ..., E_m], By two feature vector components makes fusion constructs at energy storage motor fault feature vector T, indicated with formula (12):

T=[T₁ T₂] (12)

The dimension of this energy storage motor fault feature vector is 4+m dimension, special to energy storage motor failure using Principal Component Analysis It levies vector and carries out dimension-reduction treatment, the dimension of the feature vector T ' after dimensionality reduction is λ dimension；Energy storage motor dimensionality reduction under all working state Feature vector afterwards constitutes energy storage motor sample dimensionality reduction eigenmatrix, then the dimension of energy storage motor sample dimensionality reduction eigenmatrix is 4r ×λ；

7th step constructs energy storage motor multi-category support vector machines, carries out fault diagnosis:

The total class number of the energy storage motor working condition identified is 4, and the data under some working condition are regarded as just Class, the data under remaining working condition regard negative class as, and the energy storage motor sample dimensionality reduction eigenmatrix obtained using the 6th step is used " one-to-many " method constructs three Sub-SVMs, and three Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function Multi-kernel support vector machine is constituted based on kernel function and multilayer perceptron kernel function, the nuclear parameter of each kernel function is true using empirical method It is fixed, and multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification Support vector machines identifies the working condition of breaker energy storage motor；

When breaker energy storage motor needs to carry out fault diagnosis, energy storage motor current signal is acquired, is mentioned according to the 6th step Energy storage motor fault feature vector is taken, then input quantity will be used as after energy storage motor fault feature vector dimensionality reduction, be input to energy storage The working condition identification to energy storage motor can be completed in motor multi-category support vector machines；

Two, fault diagnosis and scale evaluation are carried out to the stage of combined floodgate using closing coil electric current, comprising the following steps:

The first step acquires closing coil electric current S of the breaker under different working condition_hc(t), closing coil circuit is set Shared normal, iron core bite, mechanical structure bite, the working condition that iron core stroke is insufficient and coil voltage is different less than 5 kinds, often R group current signal is acquired under kind working condition；

Second step, to closing line loop current S_hc(t) denoising is carried out, denoising closing coil electric current S ' is obtained_hc(t), it goes Algorithm of making an uproar is consistent with Denoising Algorithm in the processing of energy storage motor current signal；

Third step, to denoising closing coil electric current S '_hc(t) material time, current amplitude parameter extraction are carried out, is closed a floodgate The local feature vectors component T that coil current signal is formed in time domain_h1；

Closing coil Current wave-shape characteristic is analyzed, the process that iron core acts is divided into four-stage: first stage is iron Core setting in motion, coil magnetization, but do not generate magnetic saturation always；Second stage is coil magnetic saturation, the exciting current of generation To have the characteristics that the peaked wave of 1/4 periodic symmetry；Three phases are electric current approximation steady state stage, coil current waveform and magnetic flux Same-phase, peak valley amplitude are constant；Fourth stage is the current waveform of auxiliary switch separation phase, and electric current shows as being reduced rapidly, Until electric current thoroughly disappears；The time t in aforementioned four stage₁₁、t₁₂、t₁₃、t₁₄Different working conditions can be in due to breaker And corresponding variation occurs, while closing coil electric current continues total time t₁₅Also it can change；Since closing coil is exchange Power voltage supply, the coil current of generation are also AC wave shape, and 3 trough I occurs altogether in closing coil electric current₁₁、I₁₃、I₁₅, 2 waves Peak I₁₂、I₁₄, it is hereby achieved that the local feature vectors component T that closing coil current signal is formed in time domain_h1=[t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅]；

4th step will denoise closing coil electric current S '_hc(t) EEMD energy Moment Feature Extraction, denoising closing coil electricity are carried out Flow S '_hc(t) it is decomposed into m_hA IMF component, acquires m altogether_hA energy square, then closing coil current signal is formed in time-frequency domain Global characteristics component of a vector T_h2=[E_1h, E_2h... ..., E_mh]；Specific steps are the same as EEMD energy Moment Feature Extraction in energy storage stage Method；

5th step establishes closing coil fault feature vector, constructs closing coil sample dimensionality reduction eigenmatrix:

By local feature vectors component T of the closing coil current signal in time domain_h1=[t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅] and global characteristics component of a vector T in time-frequency domain_h2=[E_1h, E_2h... ..., E_mh] fusion constructs combined floodgate Coil fault feature vector T_h=[T_h1, T_h2], the dimension of thus obtained closing coil fault feature vector is 10+m_h, through master The feature vector T ' of the closing coil obtained after componential analysis dimensionality reduction_hDimension be λ_hIt ties up, the closing line under all working state Feature vector after enclosing dimensionality reduction constitutes closing coil sample dimensionality reduction eigenmatrix, the dimension of closing coil sample dimensionality reduction eigenmatrix For 5r × λ_h；

6th step constructs closing coil circuit multi-category support vector machines, carries out fault diagnosis:

The total class number of closing coil loop works state identified is 5, is dropped using above-mentioned closing coil sample Dimensional feature matrix constructs 4 Sub-SVMs, and 4 Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function core letter Multi-kernel support vector machine is constituted based on several and multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, And multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification branch It holds vector machine to identify the working condition of breaker closing coil, constructs closing coil circuit multi-category support vector machines, Fault diagnosis is carried out to breaker closing wire loop using closing coil circuit multi-category support vector machines；

7th step determines closing coil fault degree evaluation index:

As the denoising closing coil electric current S ' for determining input by closing coil circuit multi-category support vector machines_hc(t) it is When fault-current signal, it is diagnosed to be in closing coil loop fault and iron core stroke deficiency and coil voltage deficiency failure occurs, with EEMD energy square, as fault degree evaluation index, carries out fault degree assessment with respect to entropy；

8th step constructs closing coil loop fault degree characteristic curve:

Every kind of failure is respectively provided with 4 kinds of different fault degrees, the iron core stroke under calculating in various degree using the 7th step Then insufficient EEMD energy square corresponding with coil voltage deficiency failure is fitted, linearly with respect to entropy using linear function Function is formula (16), obtains closing coil loop fault degree characteristic curve,

Y=a₁x+a₂ (16)

Wherein: x is fault degree, and y is EEMD energy square with respect to entropy, a₁,a₂For constant；

Respectively obtain less than two kinds corresponding closing coil loop fault degree of failure of iron core stroke deficiency and coil voltage Characteristic curve carries out at denoising closing coil current signal to be assessed through second step when needing to carry out fault degree assessment Then reason calculates the EEMD energy square of denoising closing coil current signal with respect to entropy according to the 7th step, substitutes into above-mentioned corresponding In closing coil loop fault degree characteristic curve, you can get it corresponding closing coil fault degree quantitative values；

Three, fault diagnosis and scale evaluation are carried out to the separating brake stage using opening coil electric current, comprising the following steps:

The first step acquires opening coil electric current S of the breaker under different faults state_fc(t), opening coil circuit is set Shared working condition normal, armature resistance is abnormal, armature travel is insufficient and coil voltage is different less than 4 kinds, every kind of work shape R group current signal is acquired under state；

Second step, to opening coil electric current S_fc(t) denoising is carried out, denoising opening coil electric current S ' is obtained_fc(t), it goes Algorithm of making an uproar is consistent with Denoising Algorithm in the processing of energy storage motor current signal；

Third step, to denoising opening coil electric current S '_fc(t) material time, current amplitude parameter extraction are carried out, separating brake is obtained The local feature vectors component T that coil current signal is formed in time domain_f1；It analyzes opening coil electric current and passes through characteristic, generate 5 Material time point t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, there are 2 trough I altogether in opening coil electric current₂₁、I₂₃, 2 wave crest I₂₂、I₂₄, obtain The local feature vectors component T that opening coil electric current is formed in time domain_f1=[t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, I₂₁, I₂₂, I₂₃, I₂₄]；

4th step will denoise opening coil electric current S '_fc(t) EEMD energy Moment Feature Extraction, denoising opening coil electricity are carried out Flow S '_fc(t) it is decomposed into m_fA IMF component, acquires m altogether_fA energy square, then opening coil current signal is formed in time-frequency domain Global characteristics component of a vector T_f2=[E_1f, E_2f... ..., Em_f]；

5th step establishes opening coil fault feature vector, constructs opening coil sample dimensionality reduction eigenmatrix:

By local feature vectors component T of the opening coil current signal in time domain_f1=[t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, I₂₁, I₂₂, I₂₃, I₂₄] and global characteristics component of a vector T in time-frequency domain_f2=[E_1f, E_2f... ..., Em_f] fusion constructs opening coil Fault feature vector T_f=[T_f1, T_f2], the dimension of thus obtained closing coil fault feature vector is 9+m_f, through principal component point Feature vector T ' after the dimensionality reduction obtained after analysis method dimensionality reduction_fDimension be λ_fDimension；After opening coil dimensionality reduction under all working state Feature vector constitute opening coil sample dimensionality reduction eigenmatrix, the dimension of closing coil sample dimensionality reduction eigenmatrix be 4r × λ_f；

6th step constructs opening coil circuit multi-category support vector machines, carries out fault diagnosis:

The total class number of opening coil loop works state identified is 4, is dropped using above-mentioned opening coil sample Dimensional feature matrix constructs 3 Sub-SVMs, and 3 Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function core letter Multi-kernel support vector machine is constituted based on several and multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, And multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification branch It holds vector machine to identify the working condition of breaker open operation coil, constructs opening coil circuit multi-category support vector machines, Fault diagnosis is carried out to breaker open operation wire loop by opening coil circuit multi-category support vector machines；

7th step determines opening coil fault degree evaluation index:

As the denoising closing coil electric current S ' for determining input by opening coil circuit multi-category support vector machines_hc(t) it is When fault-current signal, it is diagnosed to be in opening coil loop fault and armature travel deficiency and coil voltage deficiency failure occurs, with EEMD energy square relative entropy carries out fault degree assessment as fault degree evaluation index；

8th step constructs opening coil loop fault degree characteristic curve, every kind of failure is respectively provided with 4 kinds of different failures Degree, the corresponding EEMD energy square of armature travel deficiency and coil voltage deficiency failure under being calculated in various degree using the 7th step Opposite entropy, is then fitted using linear function, and linear function is formula (17), and it is special to obtain opening coil loop fault degree Linearity curve；

Y=b₁x+b₂ (17)

Wherein: x is fault degree, and y is EEMD energy square with respect to entropy, b₁, b₂For constant；

It is completed by the opening coil circuit multi-category support vector machines of above-mentioned building to occurring in opening coil circuit Normally, armature resistance is abnormal, armature travel is insufficient, less than 4 kinds working conditions of coil voltage are diagnosed；When the failure being diagnosed to be To need to continue fault degree assessment, after calculating denoising to be assessed when armature travel is insufficient or coil voltage deficiency Opening coil electric current EEMD energy square relative entropy, substitutes into the opening coil loop fault degree characteristic curve of above-mentioned foundation, i.e., The achievable qualitative assessment to opening coil fault degree.

Compared with prior art, the beneficial effects of the present invention are:

The invention proposes the processing modes of the ac signal based on operation attachment, not with traditional DC analysis mode Together, operation attachment AC signal is electric signal, and stability is more preferable, and it is any subtle more accurately to reflect that operation attachment generates Variation, and not vulnerable to external interference, feature extraction mode diversification, due to the existing fault diagnosis for breaker both for In high-voltage circuitbreaker, operates various coils in attachment and use direct current supply, the research object frame-type open circuit in the present invention Device is low-voltage circuit breaker, is powered for alternating voltage；Multicore supporting vector of the fault diagnosis mode Selection utilization based on genetic algorithm Machine efficiently solves the problems, such as the Selection of kernel function of conventional single-core support vector machines, and fault diagnosis reliability is higher, has stronger Engineering practicability；Propose fault degree quantitative evaluating method, rather than simple judgement failure whether there is or not and fault location, together When overcome the problem of existing fault degree qualitative analysis inaccuracy.

Present invention substantive distinguishing features outstanding are: for frame-type circuit breaker fault diagnosis and the practical need of scale evaluation It wants, constructs completely circuit breaker failure diagnosis and scale evaluation mode based on operation attachment electric current.Using non-intrusion type The measurement method operation attachment electric current sensitive to circuit-breaker status variation detects, and can conveniently and efficiently utilize breaker signal Acquisition system；When handling current signal, by the way of ac signal, feature extraction mode diversification, by office Portion's feature is combined with global characteristics effectively prevents the existing problem to fault signature deficiency in direct current signal processing；Utilize base In multi-kernel support vector machine (the Multiple-Kernel Learning of genetic algorithm (GeneticAlgorithm, GA) Support Vector Machine, MKL-SVM) carry out fault diagnosis, effective solution monokaryon support vector machines The Selection of kernel function and parameter of (SupportVector Machine, SVM) determine problem；For breaker energy storage, close a floodgate and The problem of needing to continue fault degree assessment in the separating brake stage on the basis of having diagnosed and being out of order, to different failure structures Different fault degree characteristic curves has been made, according to this fault degree characteristic curve, the qualitative assessment of corresponding failure can be completed, The problem of fault location can only be carried out in fault diagnosis technology is not only solved, also solving existing fault degree research cannot The problem of quantitative analysis.

The present invention is based on the marked improvements of the frame-type circuit breaker fault diagnosis of operation attachment electric current and degree assessment method It is:

(1) the method for the present invention not only identifies circuit breaker failure type, also to breaker occur fault degree into Row Quantitative Analysis can help service personnel preferably to grasp the mechanical breakdown degree of injury of breaker, and effectively guidance is set Standby maintenance.

(2) the method for the present invention is to include the current signal of abundant mechanical property information during breaker actuation as data Source, the electrical cable of the energy storage motor of breaker and divide-shut brake coil, which is passed through current sensor, can be completed to electric current The measurement of signal does not need to destroy breaker body structure, realizes the state-detection to frame-type circuit breaker non-intrusion type.

(3) current signal used in the method for the present invention is AC signal, can not only carry out time-domain analysis, can be with Time and frequency domain characteristics analysis is carried out, i.e., the fault signature extraction of current signal material time, amplitude, embodies electric current in completion time domain The local feature of signal, while EEMD energy Moment Feature Extraction in time-frequency domain is carried out, the global characteristics of current signal are embodied, it will Local feature is combined with global characteristics, can preferably reflect the faint variation of current signal.

(4) the method for the present invention uses work of the multi-kernel support vector machine to breaker that weight coefficient is determined based on genetic algorithm Identified solve in monokaryon support vector machines when sample size is very big, sample characteristics include Heterogeneous Information, sample as state The problem of good recognition effect cannot be obtained whens unevenness distribution etc., improve the accuracy of intelligent diagnostics algorithm.

(5) during fault degree characteristic curve constructs, different fault degrees requires to establish under each failure Fault degree evaluation index is described, fault degree evaluation index be also by current signal under different faults degree into Row feature extraction obtains, unified using EEMD energy square relative entropy as fault degree evaluation index, the fault degree in the present invention Evaluation index is able to reflect the difference between the probability distribution of two signal energy spectrum.

(6) the method for the present invention is by establishing fault degree spy to the relationship between fault degree and fault degree characteristic index Linearity curve, the fault degree characteristic curve can the failure to the arbitrary extent in fault degree scope of assessment carry out assessment point Analysis, compared in the research of current fault degree only for specific fault degree carries out orderly identification, by it is qualitative switch to it is quantitative, Fault degree assessment accuracy, which has, to be obviously improved.

Detailed description of the invention

Fig. 1 is that the present invention a kind of frame-type circuit breaker fault diagnosis and degree assessment method based on operation attachment electric current are total Body flow chart.

Fig. 2 is the energy storage motor current signal waveform figure in embodiment 1 under different working condition.

Fig. 3 is that energy storage motor current signal denoises front and back comparison diagram under normal condition in embodiment 1.

Fig. 4 is energy storage motor current signal envelope line drawing figure under normal condition in embodiment 1.

Fig. 5 is that energy storage motor current signal EEMD schemes under normal condition in embodiment 1.

Fig. 6 is the fault diagnosis result figure based on energy storage motor current signal in embodiment 1.

Fig. 7 is the closing coil current signal waveform figure in embodiment 2 under different working condition.

Fig. 8 is that closing coil current signal denoises front and back comparison diagram under normal condition in embodiment 2.

Fig. 9 is that closing coil current signal EEMD schemes under normal condition in embodiment 2.

Figure 10 is the fitness curve based on genetic algorithm optimizing parameter in embodiment 2.

Figure 11 is genetic algorithm optimization weight coefficient variation tendency in embodiment 2.

Figure 12 is closing coil failure of the current degree characteristic curve in embodiment 2.

Figure 13 is the opening coil current signal waveform figure in embodiment 3 under different working condition.

Figure 14 is that opening coil current signal denoises front and back comparison diagram under normal condition in embodiment 3.

Figure 15 is that opening coil current signal EEMD schemes under normal condition in embodiment 3.

Figure 16 is opening coil failure of the current degree characteristic curve in embodiment 3.

Specific embodiment

The present invention is further described with the present embodiment with reference to the accompanying drawing, but does not want in this, as to the application right Ask the restriction of protection scope.

The present invention provides a kind of (simple based on the frame-type circuit breaker fault diagnosis for operating attachment electric current and degree assessment method Title method), this method first determines whether the working stage wait diagnose and assess breaker, the working stage include energy storage stage, Then combined floodgate stage and separating brake stage carry out fault diagnosis to energy storage stage using energy storage motor electric current, utilize closing coil electricity Stream fault diagnosis and scale evaluation are carried out to the stage of combined floodgate and using opening coil electric current to separating brake stage progress fault diagnosis and Scale evaluation；Wherein,

The specific algorithm step of the mean filter Denoising Algorithm is according to (Wu Jianhua, Li Chisheng, the Zhou Weixing such as Wu Jianhua Mean filter [J] University Of Nanchang journal (engineering version), 1998,20 (1): 32-35. compared with the denoising performance of mean filter) it is public The mean filter Denoising Algorithm step opened carries out calculating analysis；

Third step, by Hilbert envelope method to denoising energy storage motor current signal S '_cc(t) envelope is carried out to seek, Since Hilbert envelope method is the envelope of time-domain signal absolute value, it extracts modulated signal from signal, analyzes modulated signal Variation, it is simple and effective and be chiefly used in engineer application, therefore select Hilbert envelope method to denoising energy storage motor in the application The extraction of current signal progress envelope；

Hilbert envelope method comprises the concrete steps that:

3-1. is to denoising energy storage motor current signal S '_cc(t) Hilbert transform is carried out, hubert transformed signal is obtainedFor formula (1):

In formula: τ is the time shifting distinguished with time t.

3-2. is by denoising energy storage motor current signal S '_cc(t) and its hubert transformed signalAccording to formula (2) structure At denoising energy storage motor current signal S '_cc(t) analytic signal S '_zcc(t):

Wherein j indicates that hubert transformed signal is the imaginary part of analytic signal；

Then analytic signal S '_zcc(t) amplitude A (t) is indicated with formula (3):

The phase of analytic signalIt is indicated with formula (4):

It is then to denoise energy storage motor current signal S ' according to the amplitude A (t) that formula (3) acquires_cc(t) Hilbert envelope Line；

4th step, by Hilbert envelope line to denoising energy storage motor current signal S '_cc(t) material time and electricity are carried out It flows magnitude parameters to extract, obtains the local feature vectors component T that energy storage motor electric current is formed in time domain₁；

The most direct nuance of current signal of the breaker under different faults state is embodied in time domain, is carried out to it Fault signature extraction is most important, and Hilbert envelope line is the absolute value envelope in time domain, has very to fault signature is extracted Big superiority.By analyzing energy storage motor electric current operation characteristic, energy storage motor is first with the progress of biggish starting current Starting, motor stabilizing is run later, and when energy storage is near completion, switch dynamic/static contact is just contacted, and load increases suddenly, electric current It rises with it, reaches maximum value when stable operation, then limit switch acts, and electric current is cut off.It therefore, can be by energy storage motor Current maxima I when starting in current signal₁, motor stabilizing operation current maxima I₂And maximum value I₂Corresponding moment t₁、 Energy storage motor electric current continues total time t₂The local fault feature formed in time domain as energy storage motor electric current；

Wherein about the extraction to energy storage motor electric current material time and magnitude parameters, referring to Sun Yinshan etc., (Sun Yinshan opens The such as Wen Tao, Zhang Yiming high-voltage circuit-breaker switching on-off coil current signal feature extraction and failure are in method of discrimination research [J] high Piezoelectricity device, 2015,51 (9): 134-139.) utilize advantage of the wavelet transformation in terms of abrupt climatic change, it may be convenient to it detects The amplitude of current signal is mutated and frequency discontinuity.The part formed in time domain using the available energy storage motor electric current of the method Feature vector components makes T₁=[I₁, I₂, t₁, t₂]；

5th step, to denoising energy storage motor current signal S '_cc(t) set empirical mode decomposition (Ensemble is carried out Empirical Mode Decomposition, EEMD) energy Moment Feature Extraction, i.e. global characteristics extraction；

Set empirical mode decomposition is carried out to denoising energy storage motor current signal first, then seeks each component after decomposing Energy square.For the more comprehensively faint variation of reflection energy storage motor current signal, in addition to extracting in time domain other than local feature, Increasing global characteristics the slight change of energy storage motor electric current can pass through feature vector under the more three-dimensional state by different faults It embodies.

EEMD algorithm is a kind of signal processing method based on noise auxiliary proposed on the basis of empirical mode decomposition, It is zero this principle according to the average statistical of uncorrelated sequence in statistical theory, and noise is added into signal to be decomposed, by It is random sequence in noise, frequency-flat is distributed, and the addition of continuing noise makes discrete signal become continuous signal, and noise makes original While discrete change in beginning signal is continuous, and because average statistical be zero, can cancel out each other its influence to signal, it in addition to Except the advantages of retaining empirical mode decomposition, modal overlap can be effectively suppressed, improve the accuracy of signal analysis；It is more using EEMD Energy storage motor current signal S ' will be denoised by differentiating analysis_cc(t) multilayer decomposition is carried out, this unconspicuous signal frequency feature can be made It is showed in the form of significant energy variation in several subspaces of different resolution, so as to extract reflection system The global characteristics information of operating status.Traditional frequency band energy feature does not account for each decomposition frequency band rise time axis distribution The characteristics of, the characteristic parameter that may cause extraction not can accurately reflect the feature of energy storage motor current signal, thus in the application Energy feature is showed in the form of energy square, EEMD is combined with energy square and carries out global fault's feature in time-frequency domain It seeks.

EEMD energy Moment Feature Extraction comprises the concrete steps that:

x_m(t)=S '_cc(t)+n_m(t) (5)

h₁=x_m(t)-m₁ (6)

The IMF component condition are as follows: 1) in entire signal length, both extreme point number and zero crossing number must phases Deng or at most differ one；2) coenvelope of the signal and the mean value of lower envelope are necessarily equal to zero at any data point；

r₁=x_m(t)-c₁ (7)

6th step establishes energy storage motor fault feature vector, constructs energy storage motor sample dimensionality reduction eigenmatrix: passing through the 4th Step acquires the local feature vectors component T that energy storage motor electric current is formed in time domain₁=[I₁, I₂, t₁, t₂] and the 5th step acquire storage The global characteristics component of a vector T that energy current of electric is formed in time-frequency domain₂=[E₁, E₂... ..., E_m], by two feature vectors point Fusion constructs are measured into energy storage motor fault feature vector T, are indicated with formula (12):

T=[T₁ T₂] (12)

The dimension of this energy storage motor fault feature vector is 4+m dimension, in order to reduce consequent malfunction diagnosis support vector machines instruction Practice and the time of diagnosis, while reducing influence of the existence of redundant to fault diagnosis accuracy rate in fault feature vector, in the application Dimension-reduction treatment is carried out to fault feature vector using principal component analysis (principlecomponent analysis, PCA) method, The specific steps of Principal Component Analysis are with reference to (finger of the such as Han little Hai, Zhang Yaohui, Sun Fujun based on principal component analysis such as children Han Mark the Sichuan Weight Determination [J] war industry institute, 2012,33 (10): 124-126) to the step in Principal Component Analysis research It carries out, the dimension of the feature vector T ' after dimensionality reduction is λ dimension.

When constructing energy storage motor multi-category support vector machines, need to construct the progress of energy storage motor sample dimensionality reduction eigenmatrix It trains, energy storage motor shares 4 kinds of different working conditions and identifies in the application, acquires r under every kind of working condition Energy storage motor current signal is organized, the dimension after the feature vector dimensionality reduction that every group of energy storage motor current signal extracts is λ, then is formed The dimension of energy storage motor sample dimensionality reduction eigenmatrix is 4r × λ, with this energy storage motor sample dimensionality reduction eigenmatrix to energy storage motor Multi-category support vector machines are trained；

The total class number of the energy storage motor working condition identified is 4, and the data under some working condition are regarded as just Class, the data under remaining working condition regard negative class as, and the energy storage motor sample dimensionality reduction eigenmatrix obtained using the 6th step is used " one-to-many " method constructs three Sub-SVMs, and three Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function Multi-kernel support vector machine is constituted based on kernel function and multilayer perceptron kernel function, the nuclear parameter of each kernel function is true using empirical method It is fixed, and multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification Support vector machines identifies the working condition of breaker energy storage motor；The application selects genetic algorithm to multicore supporting vector Weight coefficient in machine training process optimizes, and genetic algorithm has obtained in monokaryon support vector machines parameter optimization extensively Application, generalization ability is stronger, can be effectively prevented and causes the problem of classification inaccuracy because regularisation parameter selection is not smart Occur, weight coefficient be one dynamic adjustment process, one group of weight coefficient of every generation, will linear combination at a multicore into Row test sample obtains nicety of grading, iteration adjustment weight coefficient, until optimal value is found in search；

Constructing energy storage motor multi-category support vector machines, specific step is as follows:

A7-1. the feature vector under normal operating conditions in sample dimensionality reduction eigenmatrix is classified as positive class, remaining work shape Feature vector under state is classified as negative class, constructs normal Sub-SVM；By transmission gear bite in sample dimensionality reduction eigenmatrix Feature vector under working condition is classified as positive class, and the feature vector under remaining working condition is classified as negative class, constructs transmission gear card Puckery Sub-SVM；Feature vector under spring bite working condition in sample dimensionality reduction eigenmatrix is classified as positive class, remaining Feature vector under working condition is classified as negative class, constructs spring bite Sub-SVM；If above-mentioned 3 Sub-SVMs are equal It cannot effectively identify, then energy storage motor is in spring and falls off working condition；Wherein multi-kernel support vector machine, i.e. Sub-SVM Construction step and Ye Hui (unmanned plane sensor fault diagnosis system research of the Ye Hui based on small echo Yu multiple supporting vectors [D] Nanjing Aero-Space University, 2014.) step used when using based on genetic algorithm optimization multi-kernel support vector machine Unanimously.

A7-2. to above-mentioned 3 Sub-SVMs be respectively adopted in sample dimensionality reduction eigenmatrix corresponding feature vector into Row is trained, and rule of thumb method is arranged Polynomial kernel function parameter d in training process, and gaussian radial basis function parameter is using intersection Verification mode obtains, and multilayer perceptron kernel functional parameter takes the derivative of sample classification number.Determine that multicore is supported in conjunction with genetic algorithm The core weight coefficient of vector machine, obtains optimal output node, reaches the operating mode recognition accuracy of Sub-SVM most Height to get arrive the energy storage motor multi-category support vector machines based on breaker energy storage motor current signal.

When breaker energy storage motor needs to carry out fault diagnosis, energy storage motor current signal is acquired, is mentioned according to the 6th step Energy storage motor fault feature vector is taken, then input quantity will be used as after energy storage motor fault feature vector dimensionality reduction, be input to step The energy storage motor multi-category support vector machines that A7-2 is established, can be completed the identification to energy storage motor working condition；

The first step acquires closing coil electric current S of the breaker under different working condition_hc(t), it sets and closes a floodgate in the application Shared normal, the iron core bite of wire loop, mechanical structure bite, the work that iron core stroke is insufficient and coil voltage is different less than 5 kinds Make state, r group current signal is acquired under every kind of working condition；

Closing coil Current wave-shape characteristic is analyzed, the process point by iron core movement can be four-stage: first stage is Iron core setting in motion, coil magnetization, but do not generate magnetic saturation always；Second stage is coil magnetic saturation, the excitation electricity of generation Stream is the peaked wave with 1/4 periodic symmetry；Three phases are electric current approximation steady state stage, coil current waveform and magnetic It is constant to collude phase, peak valley amplitude；Fourth stage is the current waveform of auxiliary switch separation phase, and electric current shows as subtracting rapidly It is small, until electric current thoroughly disappears；The time t in aforementioned four stage₁₁、t₁₂、t₁₃、t₁₄Different work can be in due to breaker State and occur to change accordingly, while closing coil electric current continues total time t₁₅Also it can change；Since closing coil is Alternating voltage power supply, the coil current of generation is also AC wave shape, and 3 trough I occurs altogether in closing coil electric current₁₁、I₁₃、I₁₅, 2 A wave crest I₁₂、I₁₄, it is hereby achieved that the local feature vectors component T that closing coil current signal is formed in time domain_h1= [t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅]；

4th step will denoise closing coil electric current S '_hc(t) EEMD energy Moment Feature Extraction, denoising closing coil electricity are carried out Flow S '_hc(t) it is decomposed into m_hA IMF component, acquires m altogether_hA energy square, then closing coil current signal is formed in time-frequency domain Global characteristics component of a vector T_h2=[E_1h, E_2h... ..., E_mh]；Specific steps are the same as the EEMD energy square in energy storage motor current processing The method of feature extraction；

5th step establishes closing coil fault feature vector, constructs closing coil sample dimensionality reduction eigenmatrix: by closing line Local feature vectors component T of the loop current signal in time domain_h1=[t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅] and Global characteristics component of a vector T in time-frequency domain_h2=[E_1h, E_2h... ..., E_mh] fusion constructs closing coil fault feature vector T_h=[T_h1, T_h2], the dimension of thus obtained closing coil fault feature vector is 10+m_h, after Principal Component Analysis dimensionality reduction Obtained feature vector T '_hDimension be λ_hDimension；

Meanwhile when constructing closing coil circuit multi-category support vector machines, it is special to need to construct closing coil sample dimensionality reduction Sign matrix is trained, and closing coil circuit shares 5 kinds of different working conditions and diagnoses in the application, every kind of state Under acquire r group closing line loop current signal, dimension is after the dimensionality reduction of the feature vector of every group of closing line loop current signal extraction λ_h, the feature vector after closing coil dimensionality reduction under all working state constitutes closing coil sample dimensionality reduction eigenmatrix, closes a floodgate The dimension of coil sample dimensionality reduction eigenmatrix is 5r × λ_h, with this closing coil sample dimensionality reduction eigenmatrix to closing coil circuit Multi-category support vector machines are trained；

The total class number of closing coil loop works state identified is 5, is dropped using above-mentioned closing coil sample Dimensional feature matrix constructs 4 Sub-SVMs, and 4 Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function core letter Multi-kernel support vector machine is constituted based on several and multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, And multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification branch It holds vector machine to identify the working condition of breaker closing coil, constructs closing coil circuit multi-category support vector machines, The closing coil circuit multi-category support vector machines can be used for the fault diagnosis to breaker closing wire loop；Specific steps are same The building process of energy storage motor multi-category support vector machines；

7th step determines closing coil fault degree evaluation index:

As the denoising closing coil electric current S ' for determining input by closing coil circuit multi-category support vector machines_hc(t) it is When fault-current signal, that is, it is diagnosed to be in closing coil loop fault and iron core stroke deficiency and coil voltage deficiency failure occurs, Then continue fault degree assessment, the application is asked it needs to be determined that fault degree evaluation index by closing coil current signal The index value out, in conjunction with subsequent foundation fault degree characteristic curve can quantitative analysis show that the event occurs for closing coil circuit The fault degree of barrier.The application is using the opposite entropy of EEMD energy square as fault degree evaluation index；Fault degree evaluation index Current signal under using normal operating conditions calculates the EEMD of current signal under fault-current signal and normal condition as benchmark For energy square with respect to entropy, opposite entropy is smaller, indicates that two signal distributions similarity degrees are higher, conversely, then difference is bigger；

Steps are as follows with respect to the calculating of entropy for EEMD energy square:

B7-1. by acquiring denoising closing coil electric current S ' in the 4th step_hc(t) m_hThe m of a IMF component_hA energy square, i.e., E_1h, E_2h... ..., Em_h；Then m is found out according to formula (13)_hThe summation E of a energy square_H:

E_H=E₁+E₂+…+E_mh (13)

B7-2. kth being found out according to formula (14) ' a energy square accounts for the specific gravity of energy square summation are as follows:

Then denoise closing coil electric current S '_hc(t) energy square distribution character matrix are as follows: P=(p₁,p₂,…,p_k′,…, p_mh)；

It similarly calculates and denoises closing coil current signal S ' under normal condition_hz(t) energy square distribution character matrix are as follows: P ' =(p '₁,p′₂,…,p′_k′,…,p′_mh)；

B7-3. it obtains denoising closing coil current signal S ' under malfunction by formula (15)_hc(t) go down with normal condition Make an uproar closing coil current signal S '_hz(t) EEMD energy square relative entropy:

8th step constructs closing coil loop fault degree characteristic curve.In fault degree analytic process, some failures There are monotonic relationshi between scale evaluation index and fault degree, referred to as dullness fault signature, that is, the fault degree constructed is commented Index is estimated with the increase of fault degree and dullness becomes larger or becomes smaller, dull fault signature is able to reflect out and fault severity level Between monotonic trend, for fault severity level assessment intuitive, simple information is provided.

Every kind of failure is respectively provided with 4 kinds of different fault degrees, calculates the iron core under different faults degree using the 7th step Then stroke deficiency and the corresponding EEMD energy square of coil voltage deficiency failure are fitted with respect to entropy using linear function, Linear function is formula (16), obtains closing coil loop fault degree characteristic curve,

Y=a₁x+a₂ (16)

Wherein: x is fault degree, and y is EEMD energy square with respect to entropy, a₁, a₂For constant.

Respectively obtain less than two kinds corresponding closing coil loop fault degree of failure of iron core stroke deficiency and coil voltage Characteristic curve carries out at denoising closing coil current signal to be assessed through second step when needing to carry out fault degree assessment Reason, (fault degree assessment refers to the opposite entropy of the EEMD energy square for then calculating denoising closing coil current signal according to the 7th step Mark), it substitutes into above-mentioned corresponding closing coil loop fault degree characteristic curve, corresponding closing coil failure journey that you can get it Spend quantitative values；

By the closing coil circuit multi-category support vector machines of above-mentioned building, can be completed to closing coil circuit just Often, less than 5 kinds iron core bite, mechanical structure bite, iron core stroke deficiency, coil voltage working conditions are diagnosed；Work as diagnosis When haveing closing coil circuit and iron core stroke deficiency and coil voltage deficiency failure occur, need to continue fault degree assessment, EEMD energy square by calculating denoising closing coil current signal to be assessed substitutes into the closing coil of above-mentioned foundation with respect to entropy In loop fault degree characteristic curve, the qualitative assessment to fault degree can be completed；

The first step acquires opening coil electric current S of the breaker under different faults state_fc(t), separating brake is set in the application The shared working condition normal, armature resistance is abnormal, armature travel is insufficient and coil voltage is different less than 4 kinds of wire loop, often R group current signal is acquired under kind working condition；

Second step, to opening coil electric current S_fc(t) denoising is carried out, denoising opening coil electric current S ' is obtained_fc(t), it goes Algorithm of making an uproar is consistent with Denoising Algorithm in the processing of energy storage motor current signal.

4th step will denoise opening coil electric current S '_fc(t) EEMD energy Moment Feature Extraction, denoising opening coil electricity are carried out Flow S '_fc(t) it is decomposed into m_fA IMF component, acquires m altogether_fA energy square, then opening coil current signal is formed in time-frequency domain Global characteristics component of a vector T_f2=[E_1f, E_2f... ..., Em_f]；Specific steps are the same as EEMD energy Moment Feature Extraction in energy storage stage Method；

5th step establishes opening coil fault feature vector, constructs opening coil sample dimensionality reduction eigenmatrix: by separating brake line Local feature vectors component T of the loop current signal in time domain_f1=[t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, I₂₁, I₂₂, I₂₃, I₂₄] and when Global characteristics component of a vector T in frequency domain_f2=[E_1f, E_2f... ..., Em_f] fusion constructs opening coil fault feature vector T_f= [T_f1, T_f2], the dimension of thus obtained closing coil fault feature vector is 9+m_f, obtained after Principal Component Analysis dimensionality reduction Feature vector T '_fDimension be λ_fDimension；Feature vector after opening coil dimensionality reduction under all working state constitutes opening coil sample This dimensionality reduction eigenmatrix, the dimension of closing coil sample dimensionality reduction eigenmatrix are 4r × λ_f；

The total class number of opening coil loop works state identified is 4, is dropped using above-mentioned opening coil sample Dimensional feature matrix constructs 3 Sub-SVMs, and 3 Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function core letter Multi-kernel support vector machine is constituted based on several and multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, And multicore weight coefficient is optimized using genetic algorithm, and then obtain multi-category support vector machines, pass through more classification branch It holds vector machine to identify the working condition of breaker open operation coil, constructs opening coil circuit multi-category support vector machines, The opening coil circuit multi-category support vector machines can be used for the fault diagnosis to breaker open operation wire loop；Specific steps are same The building process of energy storage motor multi-category support vector machines；

7th step determines opening coil fault degree evaluation index:

As the denoising closing coil electric current S ' for determining input by opening coil circuit multi-category support vector machines_hc(t) it is When fault-current signal, that is, it is diagnosed to be in opening coil loop fault and armature travel deficiency and coil voltage deficiency failure occurs, Then continue fault degree assessment, using EEMD energy square relative entropy as fault degree evaluation index, specific EMD energy square phase Step is calculated with respect to entropy with EMD energy square described in closing coil fault degree evaluation index to the calculating step of entropy；

8th step constructs opening coil loop fault degree characteristic curve, every kind of failure is respectively provided with 4 kinds of different failures Degree calculates armature travel deficiency and the corresponding EEMD energy of coil voltage deficiency failure under different faults degree using the 7th step Square is measured with respect to entropy, is then fitted using linear function, linear function is formula (17), obtains opening coil loop fault journey Spend characteristic curve；

Y=b₁x+b₂ (17)

In breaker closing coil current circuit by the relationship construction between fault degree and EEMD energy square relative entropy The fault degree characteristic curve of iron core stroke deficiency and coil voltage deficiency failure is respectively as follows:

Iron core stroke is insufficient: y₁=0.17989x+0.5502；

Coil voltage is insufficient: y₂=0.87358x-0.5279；

The armature travel deficiency and the insufficient failure journey of coil voltage in breaker open operation coil current circuit constructed Degree characteristic curve is respectively as follows:

Armature travel is insufficient: y₃=0.86404x+0.40975；

Coil voltage is insufficient: y₄=1.41578x-0.16885.

The working stage of frame-type circuit breaker in the present invention includes energy storage stage, combined floodgate stage and separating brake stage, energy storage Stage, combined floodgate stage and separating brake stage are three different processes, successively carry out in breaker work, need in the application method The failure to occur respectively to the three above stage is diagnosed and is assessed respectively, i.e., breaker is using energy storage motor electric current to storage It can carry out fault diagnosis the stage, fault diagnosis and scale evaluation are carried out to the stage of combined floodgate using closing coil electric current and utilize separating brake Coil current carries out fault diagnosis and scale evaluation to the separating brake stage.

The present invention is carried out when building is based on above-mentioned multi-category support vector machines using more classification methods of " one-to-many ", It is calculated simply, is able to solve the multiple classification problems needed in fault diagnosis；If the total class number identified is G, F class regards positive class as, remaining G-1 class regards negative class as, constructs multiple two classification Sub-SVMs with this and forms more points Class support vector machines.

Support vector machines, its determined generalization ability of the kernel function of support vector machines and popularization energy are used in the present invention Power, the difference of kernel function, the sample distribution being mapped in high-dimensional feature space is just different, influences on nicety of grading very big.Using Multicore can improve the performance of kernel function, i.e., replace single kernel function using the linear combination of multiple single kernel functions, can Obtain performance more preferably than monokaryon model.Polynomial kernel function (Polynomial Kernel is used in the application Function), gaussian radial basis function (Radial Basis Function, RBF) and multilayer perceptron kernel function (SigmoidKernel Function) as mostly with the kernel function of support vector machines because Polynomial kernel function have it is good Global nature has very strong extrapolability, and RBF kernel function local property is fine, and it is very strong inside to push away ability i.e. learning ability, more Layer perceptron kernel function derives from neural network, is suitable for deep learning, therefore, in order to establish an existing preferable extrapolation energy Power has the multi-kernel support vector machine of preferable learning ability again, can carry out linear combination using these three kernel functions.Multi-kernel function In linear combination, the weight coefficient before all monokaryons determines the model of multi-kernel function, the study for support vector machines Ability, Generalization Ability have a great impact, therefore need to optimize weight coefficient, and the basic point of optimization is to solve secondary rule The problem of drawing, when in the training process of support vector machines using the genetic algorithm based on real coding, weight coefficient is that dynamic is adjusted Whole, one group of weight coefficient of every generation, linear combination is tested at a monokaryon and obtains nicety of grading, iteration adjustment weight coefficient, Genetic algorithm illustrates the adaptive process of nature and manual system.

Embodiment illustrated in fig. 1 shows a kind of frame-type circuit breaker failure based on operation attachment electric current provided by the invention The overall procedure of diagnosis and degree assessment method is: the operation attachment electricity under acquisition characterization frame-type circuit breaker different working condition Stream signal (including divide-shut brake coil current signal, energy storage motor current signal) → use Mean Filtering Algorithm respectively to various works Make material time and current amplitude parameter that current signal under state carries out noise suppression preprocessing → seek current signal, when composition Empirical Mode is gathered in domain local feature vectors component (wherein energy storage motor current signal needs first to carry out envelope extraction) → utilization State decomposition algorithm decomposes the current signal after denoising, and calculates the energy square of each component, constitutes time-frequency domain global characteristics vector point Amount → constituted using principal component analysis (PCA) method to by time domain local feature vectors component and time-frequency domain global characteristics component of a vector Fault feature vector carry out dimension-reduction treatment → using the feature vector after dimensionality reduction as input quantity and be input to more class Support Vectors Working condition identification, fault diagnosis are carried out in machine (MKL-SVM), opening coil includes normal, abnormal (the mandril resistance of armature resistance It is abnormal), the working condition that armature travel is insufficient and coil voltage is different less than 4 kinds, closing coil include normal, iron core bite, Mechanical structure bite, the working condition that iron core stroke is insufficient and coil voltage is different less than 5 kinds, energy storage motor electric current include just Often, transmission gear bite, spring bite and spring fall off 4 kinds of different working conditions → when to breaker carry out divide-shut brake coil return When the fault diagnosis of road, if closing coil loop diagnostics have iron core stroke deficiency and coil voltage deficiency failure, opening coil diagnosis When haveing armature travel deficiency and coil voltage deficiency failure, need to continue fault degree assessment, at this time to the line after denoising Divide-shut brake loop current signal directly carries out the calculating of EEMD energy square relative entropy, and substituting into fault degree characteristic curve can be obtained failure Degree quantitative values.In above-mentioned operating mode identification process, when energy storage motor is diagnosed to be the failure of any abnormal condition, it is both needed to Maintenance personal is notified to immediately treat；It is directly logical when going out iron core bite and mechanical structure bite to closing coil loop diagnostics Know that maintenance personal immediately treats, does not need to carry out fault degree assessment at this time；When being diagnosed to be armature to opening coil loop fault When resistance exception, need directly maintenance personal to be notified to handle；During above-mentioned three kinds, it is diagnosed to be energy storage motor, closes a floodgate When wire loop and opening coil circuit are in normal condition, do not need to carry out any processing.

Embodiment 1

The present embodiment is using the frame-type circuit breaker of model DW15-1600 as subjects, the breaker mechanical structure Visualization is strong, and suitable for the power distribution network that rated operational voltage is 380V, 50Hz, rated current 1600A, switching mechanism is bullet Spring operating mechanism.Energy storage motor is reluctance synchronous motor, and voltage rating AC380V, starting torque is not less than 8Nm, revolving speed For 750r/min.Fault simulation mode in energy storage stage are as follows: simulated using the energy-stored spring of the fixed certain length of hard wire disconnected The corrosion of road device energy-stored spring and caused by spring bite, timber wedge is placed between transmission gear mimic-disconnecting switch, transmission gear occurs Jam faults since the spring probability of happening that falls off is smaller and more extreme, therefore only simulate a spring dropping situations, that is, remove A piece energy-stored spring.

Using the circuit breaker failure diagnostic method based on energy storage motor electric current to the frame-type under above-mentioned simulated failure state Breaker carries out fault diagnosis, the specific steps are as follows:

The first step uses the energy storage motor electric current in the frame-type circuit breaker fault detection system acquisition breaker energy storage stage Signal, the sample frequency that data collecting card is arranged is 20kHz/s, sampling time 2s, and acquisition breaker is normal, transmission gear card Puckery, spring bite and spring fall off the energy storage motor current signal S under 4 kinds of different working conditions_cc(t), under every kind of working condition 30 groups of current signals are acquired, wherein 15 groups of training for multi-category support vector machines, 15 groups are supported for testing more classification The classification accuracy of vector machine, i.e. r=15；

Typical energy storage motor current signal under 4 kinds of working conditions is as shown in Fig. 2, respectively be normal, transmission gear Bite, spring bite and spring fall off, it can be seen from the figure that energy storage motor current signal is the friendship that peak valley amplitude gradually changes Signal is flowed, the energy storage motor current signal under different conditions only can not observe difference with naked eyes.

Second step, to the energy storage motor current signal S of acquisition_cc(t) noise suppression preprocessing is carried out using Mean Filtering Algorithm, obtained To denoising energy storage motor current signal S '_cc(t)；

Use the filter window of mean filter Denoising Algorithm for 200.It is with the energy storage motor current signal under normal condition Example, the effect for denoising front and back are as shown in Figure 3.

Third step, to denoising energy storage motor current signal S '_cc(t) Hilbert envelope line is carried out to seek；With normal condition Under energy storage motor current signal for, Hilbert envelope line is as shown in Figure 4.

4th step, by Hilbert envelope line to denoising energy storage motor current signal S '_cc(t) material time and electricity are carried out Magnitude parameters are flowed to extract；I.e. to the starting current maximum value I in energy storage motor current signal₁, electric current stable operation maximum value I₂And Maximum value I₂Corresponding moment t₁, electric current continues total time t₂It extracts, it is special to obtain part in the time domain of energy storage motor current signal Levy component of a vector.To material time and current amplitude parameter in the time domain of the energy storage motor current signal under the different conditions of acquisition The results are shown in Table 1 for feature extraction.

Material time and current amplitude parameter attribute extract result in energy storage motor current signal time domain under 1 different conditions of table

5th step carries out set empirical mode decomposition energy Moment Feature Extraction to energy storage motor current signal, i.e., global special Sign is extracted；

By taking the energy storage motor current signal under normal condition as an example, set empirical mode decomposition is carried out, each IMF after decomposition Component is decomposed into 16 components, therefore time and frequency domain characteristics as shown in figure 5, each component signal is sequentially distributed from high frequency to low frequency altogether The dimension of component of a vector is 16 dimensions, i.e. m=16 carries out energy Moment Feature Extraction, different conditions according to formula (11) to each component The results are shown in Table 2 for EEMD energy Moment Feature Extraction in lower energy storage motor current signal time-frequency domain.

EEMD energy moment characteristics (unit/J) in energy storage motor current signal time-frequency domain under 2 different conditions of table

EEMD energy moment characteristics (continued) in energy storage motor current signal time-frequency domain under 2 different conditions of table

6th step establishes energy storage motor fault feature vector, constructs energy storage motor sample dimensionality reduction eigenmatrix；

The local feature vectors component T formed in time domain by the available energy storage motor electric current of the 4th step₁=[I₁, I₂, t₁, t₂], dimension 4, the global characteristics vector point formed in time-frequency domain by the available energy storage motor electric current of the 5th step Measure T₂=[E₁, E₂... ..., E₁₆], dimension 16, then energy storage motor fault feature vector T=[T₁,T₂] dimension be 20.Setting The principal component percentage of Principal Component Analysis is 95%, then the dimension after Principal Component Analysis dimensionality reduction is 8.

It needs to construct sample dimensionality reduction eigenmatrix simultaneously and carries out the training of energy storage motor fault diagnosis model and test, energy storage electricity Machine shares 4 kinds of different states and is diagnosed, and 30 groups of signals are acquired under every kind of state, wherein 15 groups are used to train, 15 Group is for testing, and dimension of the feature vector that each energy storage motor current signal extracts after dimensionality reduction is 8, then the training sample formed This dimensionality reduction eigenmatrix dimension is 60 × 8, and test sample dimensionality reduction eigenmatrix dimension is 60 × 8.

7th step constructs energy storage motor multi-category support vector machines, carries out fault diagnosis；

The training that multi-category support vector machines are carried out using the sample dimensionality reduction eigenmatrix that the 6th step obtains, utilizes test specimens This dimensionality reduction eigenmatrix is for the test to multi-category support vector machines classification accuracy.3 are constructed using " one-to-many " method Sub-SVM, 3 Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function and multilayer perceptron core letter Based on number, and core weighting parameter is optimized using genetic algorithm, and then obtain multi-category support vector machines, it is more by this Category support vector machines test the classification accuracy of breaker.Penalty factor and core ginseng in gaussian radial basis function nuclear parameter Number σ is respectively [2^-15..., 2¹⁵], [2^-7,…,2⁷] obtained using cross validation mode, after optimization C=1.2578, σ= Rule of thumb method is set as d=3 by 0.3185, Polynomial kernel function parameter d, and multilayer perceptron kernel functional parameter γ takes sample classification Several derivatives, i.e. γ=0.25.Core weight coefficient is used to be optimized in the genetic algorithm of [0,1] interior real coding form, Optimize during determining weight coefficient, population quantity 20, it is 100 that heredity, which terminates the number of iterations,.Utilize 60 test samples Energy storage motor fault diagnosis model is tested, for the ease of comparing, the setting of monokaryon function parameter and multi-kernel support vector machine Parameter setting is identical, while multi-kernel support vector machine is carried out fault diagnosis to single features and fusion feature respectively, each to identify Rate is as shown in table 3.Fault diagnosis based on multi-kernel support vector machine and fusion feature (local feature and global characteristics combine) Result figure is as shown in Figure 6.

3 energy storage motor electric current difference kernel function support vector machine discrimination of table

Embodiment 1 shows to can be good at energy storage motor electric current being applied to open circuit by the method proposed in the application Device fault diagnosis, and accuracy rate of diagnosis is higher.

Embodiment 2

The present embodiment is using the frame-type circuit breaker of model DW15-1600 as subjects, the breaker mechanical structure Visualization is strong, and suitable for the power distribution network that rated operational voltage is 380V, 50Hz, rated current 1600A, switching mechanism is bullet Spring operating mechanism.Switching electromagnet is armoured type solenidal electromagnet in closing coil circuit, and voltage 380V, 50Hz, the number of turns is 2130 circles, enamel covered wire is through 0.25mm.Fault simulation mode in closing coil circuit are as follows: in the dynamic iron core of solenoid electromagnet Thin wire simulation iron core jam faults are filled between axle portion and coil；By pushing rod slide to rise elliptic space internal plug in tripping Enter small wood analog mechanical structure jam faults；The insufficient fault degree simulation of iron core stroke at the top of dynamic iron core by increasing not Rubber pad (1.0mm) with quantity realizes that rubber pad addition quantity is more, and fault degree is bigger；The insufficient difference of coil voltage Fault degree adjusts voltage by electric voltage regulator and completes.It is as shown in table 4 that concrete mode is arranged in closing coil fault degree:

The setting of 4 closing coil fault degree of table

Wherein: the insufficient distance of iron core stroke is the difference with standard iron core stroke, when closing iron core stroke it is insufficient away from When from being greater than 4.0mm, breaker closing failure；U₁For closing coil voltage rating, it is reliable which releases energy electromagnet Operation voltage need to be greater than 80%U₁；Fault severity level: d1 < d2 < d3 < d4.

It is diagnosed using the circuit breaker failure based on closing coil electric current and degree assessment method is to above-mentioned simulated failure state Under frame-type circuit breaker carry out fault diagnosis and scale evaluation, the specific steps are as follows:

The first step uses the closing coil electric current in the frame-type circuit breaker fault detection system acquisition breaker closing stage Signal, the sample frequency that data collecting card is arranged is 20kHz/s, sampling time 0.07s.Circuit-breaker switching on-off coil is acquired to return Road is in normal, iron core bite, mechanical structure bite, iron core stroke is insufficient, the closing coil under less than 5 kinds states of coil voltage Current signal data S_hc(t), 30 groups of current signals are acquired under every kind of working condition, wherein 15 groups are used for more class Support Vectors The training of machine, 15 groups for testing the classification accuracy of multi-category support vector machines, i.e. r=15；Typical case under 5 kinds of working conditions Closing coil current signal as shown in fig. 7, respectively be normal, iron core bite, mechanical structure bite, iron core stroke it is insufficient, Coil voltage deficiency failure, electricity when wherein the closing coil current signal of iron core stroke not foot be fault degree is 4.0mm Flow waveform, the closing coil current signal of coil voltage foot be not fault degree is 80%U₁When current waveform, Cong Tuzhong As can be seen that closing coil current signal is the AC signal that peak valley amplitude gradually changes, 3 troughs, 2 wave crests are shared.

Second step, to the closing coil current signal S of acquisition_hc(t) noise suppression preprocessing is carried out using Mean Filtering Algorithm, obtained To denoising closing coil current signal S '_hc(t)；The filter window of mean filter Denoising Algorithm is 50.With the conjunction under normal condition For lock coil current signal, the effect for denoising front and back is as shown in Figure 8.

Third step, to denoising closing coil electric current S '_hc(t) material time is carried out, current amplitude parameter attribute extracts.I.e. pair The time t in 4 stages₁₁, t₁₂, t₁₃, t₁₄Continue total time t with electric current₁₅, 3 trough I of electric current₁₁、I₁₃、I₁₅, 2 wave crests I₁₂、I₁₄It extracts, material time and current amplitude parameter attribute such as table in closing coil current signal time domain under different conditions Shown in 5.

Material time and current amplitude parameter attribute in closing coil current signal time domain under 5 different conditions of table

4th step carries out set empirical mode decomposition energy Moment Feature Extraction to closing line loop current signal, i.e., global special Sign is extracted；By taking the closing coil current signal under normal condition as an example, EEMD is carried out, each component after decomposition is as shown in figure 9, altogether It is decomposed into 11 components, i.e. m_h=11, therefore feature vector components makes dimension is 11 dimensions in time-frequency domain, to each component according to formula (11) energy Moment Feature Extraction is carried out, EEMD energy moment characteristics such as table 6 in closing coil current signal time-frequency domain under different conditions It is shown.

EEMD energy moment characteristics (unit/J) in switching current signal time-frequency domain under 6 different conditions of table

5th step, the foundation of fault feature vector；Through the available closing coil current signal of third step in time domain The local feature vectors component dimension of formation is 10, passes through the available closing coil current signal shape in time-frequency domain of the 4th step At global characteristics component of a vector dimension be 11, then the dimension of fault feature vector be 21.Set Principal Component Analysis it is main at Dividing percentage is 95%, then the dimension after Principal Component Analysis dimensionality reduction is 9.

It needs to construct sample dimensionality reduction eigenmatrix simultaneously and carries out the training of closing coil loop fault diagnostic model, closing coil Circuit shares 5 kinds of different states and is diagnosed, and 30 groups of signals are acquired under every kind of state, wherein 15 groups are used to train, 15 groups are used to test, and the dimension after the feature vector dimensionality reduction that every group of current signal extracts is 9, then the training sample dimensionality reduction formed is special The dimension for levying matrix is 75 × 9, and the dimension of test sample dimensionality reduction eigenmatrix is 75 × 9.

6th step constructs closing coil circuit multi-category support vector machines and is used for state recognition with the 7th step of embodiment 1. Wherein in gaussian radial basis function parameter penalty factor and nuclear parameter σ respectively [2^-15..., 2¹⁵], [2^-7,…,2⁷] use Cross validation mode obtains, and C=0.3711, σ=1.6352 are obtained after optimization；Rule of thumb method is arranged Polynomial kernel function parameter d For d=3；Multilayer perceptron kernel functional parameter γ takes the derivative of sample classification number, i.e. γ=0.2.List based on different kernel functions The fault diagnosis discrimination of kernel support vectors machine and the multi-kernel support vector machine based on different characteristic is as shown in table 7.

7 closing coil electric current difference kernel function SVM discrimination of table

During determining weight coefficient using GA algorithm optimization, population quantity 20, heredity terminates the number of iterations and is 100, Figure 10 be the fitness curve based on genetic algorithm optimizing parameter.Evolution of Population is best to or so 30 generations as seen from the figure Fitness value is just basically stable at 92.0%.

Figure 11 is the variation tendency that GA algorithm carries out each kernel function weight coefficient in weight coefficient optimization process.It can be seen that For each weight coefficient there are the process of a dynamic adjustment, gaussian radial basis function core remains at higher weight, polynomial kernel power Value shows the trend of rising, but finally tends towards stability, and multilayer perceptron core constantly learns in preceding 30 generation, and final weight coefficient is steady It is scheduled on 0.21 or so, algorithm convergence when population iterates to or so 30 generations exports weight coefficient optimal value.

7th step calculates fault-current signal entropy opposite with the EEMD of normal current signal；When the closing line being diagnosed to be When there is iron core stroke deficiency and coil voltage deficiency failure in circle loop fault, need to continue fault degree assessment, i.e., Calculate the EEMD energy square relative entropy of different faults degree current signal under iron core stroke deficiency and coil voltage deficiency failure Value.Its EEMD energy square relative entropy with normal switching current signal is calculated by formula (15), table 8 is that 4 are arranged in this application Carried out respectively under kind of fault degree EEMD energy square with respect to entropy calculate as a result, every kind of result is 5 test averagings of income Value.

EEMD energy square relative entropy under 8 combined floodgate different faults degree of table

8th step constructs closing coil loop fault degree characteristic curve.Become using linear function to fault degree variation Gesture is fitted, and constructs fault degree characteristic curve with this, and fitting result is as shown in figure 12, matching correlation 98% with On.Iron core stroke deficiency and the fault characteristic curve of coil voltage deficiency fitting are respectively as follows:

Iron core stroke is insufficient: y₁=0.17989x+0.5502；

Coil voltage is insufficient: y₂=0.87358x-0.5279；

2 show that the multi-category support vector machines of the application building can be efficiently accomplished to breaker closing through this embodiment The fault diagnosis of wire loop, by the fault degree evaluation index EEMD energy square relative entropy of building, in conjunction with the failure of fitting Degree characteristic curve, the achievable qualitative assessment to closing coil loop fault degree.

Embodiment 3

The present embodiment is using the frame-type circuit breaker of model DW15-1600 as subjects, the breaker mechanical structure Visualization is strong, and suitable for the power distribution network that rated operational voltage is 380V, 50Hz, rated current 1600A, switching mechanism is bullet Spring operating mechanism.Tripping electromagnet is beating type electromagnet, voltage 380V, 50Hz, the number of turns 4650 in opening coil circuit Circle, enamel covered wire is through 0.21mm.Opening coil loop fault analog form are as follows: tie down mandril push jack, simulation top using elastic string Bar suffered resistance in stroke is abnormal；Since tripping electromagnet is beating type electromagnet, armature travel is less than combined floodgate electromagnetism Iron, in order to construct the failure of the different degrees of grade of sufficient amount, therefore the hard paper by pasting different number on armature (0.1mm) simulates armature travel deficiency failure, and stickup quantity is more, and fault degree is bigger；Coil voltage deficiency fault simulation is closed Brake cable circle mode is identical, i.e., the insufficient different faults degree of coil voltage adjusts voltage by electric voltage regulator and completes.Closing line It is as shown in table 9 to enclose fault degree setting concrete mode:

The setting of 9 separating brake fault degree of table

Wherein: the insufficient distance of armature travel is the difference with standard armature travel, when separating brake armature travel it is insufficient away from When from being greater than 2.0mm, breaker open operation failure；U₂For opening coil voltage rating, the model breaker shunt release can 70%U need to be greater than by operation voltage₂；Fault severity level: d1 < d2 < d3 < d4.

It is diagnosed using the circuit breaker failure based on opening coil electric current and degree assessment method is to above-mentioned simulated failure state Under frame-type circuit breaker carry out fault diagnosis and scale evaluation, the specific steps are as follows:

The first step uses the opening coil electric current in the frame-type circuit breaker fault detection system acquisition breaker open operation stage Signal；Circuit-breaker switching on-off wire loop is acquired in normal, armature resistance is abnormal, armature travel is insufficient, coil voltage is less than 4 Opening coil current signal data S under kind state_fc(t), 30 groups of current signals are acquired under every kind of working condition, wherein 15 groups For the training of multi-category support vector machines, 15 groups for testing the classification accuracy of multi-category support vector machines, i.e. r=15；

Typical closing coil current signal under 4 kinds of working conditions is as shown in figure 13, respectively is normal, armature resistance It is abnormal, armature travel is insufficient, coil voltage deficiency failure, wherein the opening coil current signal of armature travel not foot is failure The opening coil current signal of current waveform when degree is 2.0mm, coil voltage foot be not fault degree is 72%U₂When Current waveform share 2 from the waveforms it can be seen that opening coil current signal is the AC signal that gradually changes of peak valley amplitude A trough, 2 wave crests.

Second step, to the opening coil current signal S of acquisition_fc(t) noise suppression preprocessing is carried out using Mean Filtering Algorithm, obtained To denoising opening coil current signal S '_fc(t)；The filter window of mean filter Denoising Algorithm is 50.With point under normal condition For lock coil current signal, the effect for denoising front and back is as shown in figure 14.

Third step, to denoising opening coil electric current S '_fc(t) material time, current amplitude parameter extraction are carried out.I.e. to 4 The time t in stage₂₁, t₂₂, t₂₃, t₂₄Continue total time t with opening coil electric current₂₅, 2 trough I of electric current₂₁、I₂₃, 2 wave crests I₂₂、I₂₄It extracts, material time and current amplitude parameter attribute be such as in the opening coil current signal time domain under different conditions Shown in table 10.

Material time and current amplitude parameter attribute in separating brake current signal time domain under 10 different conditions of table

4th step carries out set empirical mode decomposition energy Moment Feature Extraction to opening coil current signal；With normal shape For opening coil current signal under state, EEMD decomposition is carried out, each component after decomposition is as shown in figure 15, is decomposed into 11 altogether Component, i.e., m at this time_f=11, energy Moment Feature Extraction is carried out according to formula (11) to each component, opening coil electricity under different conditions It is as shown in table 11 to flow EEMD energy moment characteristics in signal time-frequency domain.

EEMD energy moment characteristics (unit/J) in separating brake current signal time-frequency domain under 11 different conditions of table

5th step, the foundation of fault feature vector.It is formed in time domain by the available opening coil electric current of third step Local feature vectors component dimension be 9, the overall situation formed in time-frequency domain by the available opening coil electric current of the 4th step Feature vector components makes dimension is 11, then the dimension of fault feature vector is 20.Set the principal component percentage of Principal Component Analysis It is 95%, then the dimension after principal component analysis dimensionality reduction is 8.

It needs to construct sample dimensionality reduction eigenmatrix simultaneously and carries out the training of opening coil loop fault diagnostic model and test, point Lock wire loop shares 4 kinds of different states and is diagnosed, and 30 groups of signals are acquired under every kind of state, wherein 15 groups are used for Training, 15 groups are used to test, and the dimension after the feature vector dimensionality reduction that every group of current signal extracts is 8, then the training sample formed Dimensionality reduction eigenmatrix dimension is 60 × 8, and test sample dimensionality reduction eigenmatrix dimension is 60 × 8.

6th step constructs opening coil circuit multi-category support vector machines and is used for state recognition with the 6th step of embodiment 2. Wherein in gaussian radial basis function parameter penalty factor and nuclear parameter σ respectively [2^-15..., 2¹⁵], [2^-7,…,2⁷] use Cross validation mode obtains, and C=1.3367, σ=0.1524 are obtained after optimization；Rule of thumb method is arranged Polynomial kernel function parameter d For d=3；Multilayer perceptron kernel functional parameter γ takes the derivative of sample classification number, i.e. γ=0.25.List based on different kernel functions The fault diagnosis discrimination of kernel support vectors machine and the multi-kernel support vector machine based on different characteristic is as shown in table 12.

12 opening coil electric current difference kernel function SVM discrimination of table

GA algorithm population iteration to 40 generations or so parameter optimal adaptation degree during optimization reaches 93.3%, multicore power Value coefficient also tends towards stability.

7th step calculates opening coil fault-current signal entropy opposite with the EEMD of normal current signal.When being diagnosed to be Opening coil loop fault in when there is armature travel deficiency and coil voltage deficiency failure, need to continue fault degree Assessment.The EEMD energy square relative entropy of opening coil current signal and normal switching current signal, table 13 are calculated by formula (15) EEMD energy square is carried out under 4 kinds of fault degrees respectively with respect to entropy calculating as a result, every kind of result is equal to be arranged in this application For 5 test averaging of income values.

EEMD energy square relative entropy under 13 combined floodgate different faults degree of table

8th step constructs opening coil loop fault degree characteristic curve.Become using linear function to fault degree variation Gesture is fitted, and constructs fault degree characteristic curve with this, and fitting result is as shown in figure 16, matching correlation 99% with On.Armature travel deficiency and the fault characteristic curve of coil voltage deficiency fitting are respectively as follows:

Armature travel is insufficient: y₃=0.86404x+0.40975；

Coil voltage is insufficient: y₄=1.41578x-0.16885；

3 verifyings show that the multi-category support vector machines of the application building can be efficiently accomplished to breaker through this embodiment The diagnosis of opening coil loop fault, by the fault degree evaluation index EEMD energy square relative entropy of building, in conjunction with fitting Fault degree characteristic curve, the achievable qualitative assessment to opening coil loop fault degree.

Above-mentioned steps are all made of LabVIEW and MATLAB software realization.

LabVIEW and MATLAB software used in above-mentioned the present embodiment is known to those skilled in the art 's.

Percentage in examples detailed above is numerical percentage.

The present invention does not address place and is suitable for the prior art.

Claims

1. a kind of frame-type circuit breaker fault diagnosis and degree assessment method, this method based on operation attachment electric current first determines whether Wait diagnose and assess the working stage of breaker, the working stage includes energy storage stage, combined floodgate stage and separating brake stage, then Fault diagnosis is carried out to energy storage stage using energy storage motor electric current, fault diagnosis is carried out to the stage of combined floodgate using closing coil electric current And scale evaluation and fault diagnosis and scale evaluation are carried out to the separating brake stage using opening coil electric current；Wherein,

The first step acquires energy storage motor electric current S of the breaker under different working condition_cc(t), setting energy storage motor is shared just Often, transmission gear bite, spring bite and spring fall off 4 kinds of different working conditions, and the storage of r group is acquired under every kind of working condition It can motor current signal；

Second step is carried out noise suppression preprocessing to the energy storage motor current signal of acquisition, is gone using mean filter Denoising Algorithm Make an uproar energy storage motor current signal S '_cc(t)；

Third step, by Hilbert envelope method to denoising energy storage motor current signal S '_cc(t) envelope is carried out to seek, according to Formula (3) acquires the amplitude A (t) of denoising energy storage motor current signal, and A (t) is to denoise energy storage motor current signal S '_cc(t) Hilbert envelope line；

4th step, by Hilbert envelope line to denoising energy storage motor current signal S '_cc(t) material time and electric current width are carried out Value parameter is extracted, by current maxima I when starting in energy storage motor current signal₁, motor stabilizing operation current maxima I₂ And maximum value I₂Corresponding moment t₁, energy storage motor electric current continue total time t₂As the local fault feature of energy storage motor electric current, obtain The local feature vectors component T formed in time domain to energy storage motor electric current₁=[I₁, I₂, t₁, t₂]；

5th step, to denoising energy storage motor current signal S '_cc(t) set empirical mode decomposition EEMD energy moment characteristics are carried out to mention It takes, i.e., global characteristics extract；

Set empirical mode decomposition EEMD energy Moment Feature Extraction comprises the concrete steps that:

5-1., which is determined, decomposes number M and the noise amplitude to be added, by white noise signal n_m(t) it is added to and to be decomposed Denoise energy storage motor current signal S '_cc(t) in, new signal x to be decomposed is obtained according to formula (5)_m(t):

x_m(t)=S '_cc(t)+n_m(t) (5)

5-2. determines new signal x to be decomposed_m(t) Local modulus maxima and minimum point, will be whole using cubic spline interpolation Maximum point be attached, formed coenvelope line；Similarly, cubic spline interpolation is carried out to all minimum points, obtains lower packet Winding thread；

5-4. is by new signal x to be decomposed_m(t) m1 is subtracted, obtains one-component h according to formula (6)₁If: h₁Meet intrinsic mode IMF component condition, then h₁It is just new signal x to be decomposed_m(t) the first intrinsic mode for meeting intrinsic mode IMF component condition IMF component, first intrinsic modal components for meeting intrinsic mode IMF component condition are denoted as c₁, enter step 5-6；

h₁=x_m(t)-m₁ (6)

If 5-5. h₁It is unsatisfactory for intrinsic mode IMF component condition, and includes the odd function of other different scales, then by h₁As original Beginning signal repeats step 5-2~5-4, obtains h₁Upper and lower envelope, calculate h₁Coenvelope average value, be denoted as m₁₁；So After judge h₁₁=h₁-m₁₁Intrinsic mode IMF component condition can be met to continue cycling through if be not met by, until h_1k =h_1(k-1)-m_1kUntil meeting intrinsic mode IMF component condition；By h_1kAs new signal x to be decomposed_m(t) first is full The intrinsic modal components of the intrinsic mode IMF component condition of foot, are denoted as c₁；

r₁=x_m(t)-c₁ (7)

5-7. is by r₁Step 5-2~5-5 is repeated as original signal to be decomposed, obtains second intrinsic mode IMF component c₂；

5-8. repeats step 5-2~5-7, obtains new signal x to be decomposed according to formula (8)_m(t) m intrinsic mode IMF components, As residual components r_mWhen for monotonic function, then stop repeating step 5-2~5-7, complete it is primary decompose,

In formula, r_mIt is survival function, the average tendency of representation signal；c_iFor i-th of intrinsic mode IMF component；

The amplitudes noise signal such as new is added to the denoising energy storage motor current signal S ' decomposed by 5-9._cc(t) in, It is repeated in step 5-2~5-8 M times, obtains the intrinsic mode IMF vector sequence { c of M group_n,l, c_n,lThe l decomposed for n-th A intrinsic mode IMF component, n=1,2 ... ..., M, l=1,2 ... ..., m；

5-10. seeks the intrinsic mode IMF vector sequence { c of M group according to formula (10)_n,lFirst of intrinsic mode IMF component M times point The average value of solutionIt willAs first of intrinsic mode IMF component of set empirical mode decomposition EEMD, i.e., by set warp Testing the intrinsic mode IMF component that mode decomposition EEMD is obtained is

5-11. is according to formula (11) to the intrinsic mode IMF component obtained by set empirical mode decomposition EEMDIt carries out taking energy Square calculates:

5-12. new signal x to be decomposed_m(t) m intrinsic mode IMF components are decomposed into, acquire m energy square E altogether_m, then energy storage The global characteristics component of a vector T that current of electric is formed in time-frequency domain₂=[E₁, E₂... ..., E_m]；

The local feature vectors component T that energy storage motor electric current is formed in time domain is acquired by the 4th step₁=[I₁, I₂, t₁, t₂] and 5th step acquires the global characteristics component of a vector T that energy storage motor electric current is formed in time-frequency domain₂=[E₁, E₂... ..., E_m], by two A feature vector components makes fusion constructs are indicated at energy storage motor fault feature vector T with formula (12):

T=[T₁ T₂] (12)

The dimension of this energy storage motor fault feature vector be 4+m dimension, using Principal Component Analysis to energy storage motor fault signature to Amount carries out dimension-reduction treatment, and the dimension of the feature vector T ' after dimensionality reduction is λ dimension；After energy storage motor dimensionality reduction under all working state Feature vector constitutes energy storage motor sample dimensionality reduction eigenmatrix, then the dimension of energy storage motor sample dimensionality reduction eigenmatrix is 4r × λ；

The total class number of the energy storage motor working condition identified is 4, and the data under some working condition are regarded as positive class, Data under remaining working condition regard negative class as, the energy storage motor sample dimensionality reduction eigenmatrix obtained using the 6th step, using " a pair of It is more " method three Sub-SVMs of building, three Sub-SVMs are again with Polynomial kernel function, gaussian radial basis function With constitute multi-kernel support vector machine based on multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, and Multicore weight coefficient is optimized using genetic algorithm, and then obtains multi-category support vector machines, is supported by more classification Vector machine identifies the working condition of breaker energy storage motor；

When breaker energy storage motor needs to carry out fault diagnosis, energy storage motor current signal is acquired, extracts and stores up according to the 6th step Then energy electrical fault feature vector will be used as input quantity, be input to energy storage motor after energy storage motor fault feature vector dimensionality reduction The working condition identification to energy storage motor can be completed in multi-category support vector machines；

The first step acquires closing coil electric current S of the breaker under different working condition_hc(t), setting closing coil circuit is shared Normally, iron core bite, mechanical structure bite, the working condition that iron core stroke is insufficient and coil voltage is different less than 5 kinds, every kind of work Make to acquire r group current signal under state；

Second step, to closing line loop current S_hc(t) denoising is carried out, denoising closing coil electric current S ' is obtained_hc(t), denoising is calculated Method is consistent with Denoising Algorithm in the processing of energy storage motor current signal；

Third step, to denoising closing coil electric current S '_hc(t) material time, current amplitude parameter extraction are carried out, closing coil is obtained The local feature vectors component T that current signal is formed in time domain_h1；

Closing coil Current wave-shape characteristic is analyzed, the process that iron core acts is divided into four-stage: first stage is opened for iron core Begin to move, coil magnetization, but does not generate magnetic saturation always；Second stage is coil magnetic saturation, and the exciting current of generation is tool There is the peaked wave of 1/4 periodic symmetry feature；Three phases are electric current approximation steady state stage, coil current waveform and the same phase of magnetic flux Position, peak valley amplitude are constant；Fourth stage is the current waveform of auxiliary switch separation phase, and electric current shows as being reduced rapidly, until Electric current thoroughly disappears；The time t in aforementioned four stage₁₁、t₁₂、t₁₃、t₁₄It can be sent out since breaker is in different working conditions Raw corresponding variation, while closing coil electric current continues total time t₁₅Also it can change；Since closing coil is alternating voltage Power supply, the coil current of generation is also AC wave shape, and 3 trough I occurs altogether in closing coil electric current₁₁、I₁₃、I₁₅, 2 wave crest I₁₂、 I₁₄, it is hereby achieved that the local feature vectors component T that closing coil current signal is formed in time domain_h1=[t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅]；

4th step will denoise closing coil electric current S '_hc(t) set empirical mode decomposition EEMD energy Moment Feature Extraction is carried out, is gone Make an uproar closing coil electric current S '_hc(t) it is decomposed into m_hA intrinsic mode IMF component, acquires m altogether_hA energy square, then closing coil electric current The global characteristics component of a vector T that signal is formed in time-frequency domain_h2=[E_1h, E_2h... ..., E_mh]；Specific steps are the same as in energy storage stage Gather the method for empirical mode decomposition EEMD energy Moment Feature Extraction；

By local feature vectors component T of the closing coil current signal in time domain_h1=[t₁₁, t₁₂, t₁₃, t₁₄, t₁₅, I₁₁, I₁₂, I₁₃, I₁₄, I₁₅] and global characteristics component of a vector T in time-frequency domain_h2=[E_1h, E_2h... ..., E_mh] fusion constructs closing coil Fault feature vector T_h=[T_h1, T_h2], the dimension of thus obtained closing coil fault feature vector is 10+m_h, through principal component The closing coil feature vector T ' obtained after analytic approach dimensionality reduction_hDimension be λ_hIt ties up, the closing coil dimensionality reduction under all working state Rear feature vector constitutes closing coil sample dimensionality reduction eigenmatrix, the dimension of closing coil sample dimensionality reduction eigenmatrix be 5r × λ_h；

The total class number of closing coil loop works state identified is 5, special using above-mentioned closing coil sample dimensionality reduction Levy matrix, construct 4 Sub-SVMs, 4 Sub-SVMs again with Polynomial kernel function, gaussian radial basis function and Multi-kernel support vector machine is constituted based on multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, and is adopted Multicore weight coefficient is optimized with genetic algorithm, and then obtains multi-category support vector machines, by the more classification support to Amount machine identifies the working condition of breaker closing coil, constructs closing coil circuit multi-category support vector machines, uses Closing coil circuit multi-category support vector machines carry out fault diagnosis to breaker closing wire loop；

7th step determines closing coil fault degree evaluation index:

As the denoising closing coil electric current S ' for determining input by closing coil circuit multi-category support vector machines_hcIt (t) is failure It when current signal, is diagnosed to be in closing coil loop fault and iron core stroke deficiency and coil voltage deficiency failure occurs, with set Empirical mode decomposition EEMD energy square, as fault degree evaluation index, carries out fault degree assessment with respect to entropy；

Steps are as follows with respect to the calculating of entropy for the set empirical mode decomposition EEMD energy square of closing coil:

B7-1. by acquiring denoising closing coil electric current S ' in the 4th step_hc(t) m_hThe m of a intrinsic mode IMF component_hA energy Square, i.e. E_1h, E_2h... ..., Em_h；Then m is found out according to formula (13)_hThe summation E of a energy square_H:

Then denoise closing coil electric current S '_hc(t) energy square distribution character matrix are as follows: P=(p₁,p₂,…,p_k′,…,p_mh)；

It similarly calculates and denoises closing coil current signal S ' under normal condition_hz(t) energy square distribution character matrix are as follows: P '= (p′₁,p′₂,…,p′_k′,…,p′_mh)；

B7-3. failure is obtained by formula (15) denoise closing coil current signal S '_hc(t) and closing coil is denoised under normal condition Current signal S '_hz(t) set empirical mode decomposition EEMD energy square relative entropy:

8th step constructs closing coil loop fault degree characteristic curve:

Every kind of failure is respectively provided with 4 kinds of different fault degrees, the iron core stroke under calculating in various degree using the 7th step is insufficient Set empirical mode decomposition EEMD energy square corresponding with coil voltage deficiency failure with respect to entropy, then using linear function into Row fitting, linear function are formula (16), obtain closing coil loop fault degree characteristic curve,

Y=a₁x+a₂ (16)

Wherein: x is fault degree, and y is set empirical mode decomposition EEMD energy square with respect to entropy, a₁, a₂For constant；

Respectively obtain the corresponding closing coil loop fault degree characteristic of less than two kinds failures of iron core stroke deficiency and coil voltage Curve carries out denoising to closing coil current signal to be assessed through second step, so when needing to carry out fault degree assessment The set empirical mode decomposition EEMD energy square of denoising closing coil current signal is calculated according to the 7th step afterwards with respect to entropy, is substituted into In above-mentioned corresponding closing coil loop fault degree characteristic curve, you can get it, and corresponding closing coil fault degree is quantitative Value；

The first step acquires opening coil electric current S of the breaker under different faults state_fc(t), setting opening coil circuit is shared Normally, the working condition that armature resistance is abnormal, armature travel is insufficient and coil voltage is different less than 4 kinds, under every kind of working condition Acquire r group current signal；

Second step, to opening coil electric current S_fc(t) denoising is carried out, denoising opening coil electric current S ' is obtained_fc(t), denoising is calculated Method is consistent with Denoising Algorithm in the processing of energy storage motor current signal；

Third step, to denoising opening coil electric current S '_fc(t) material time, current amplitude parameter extraction are carried out, opening coil is obtained The local feature vectors component T that current signal is formed in time domain_f1；It analyzes opening coil electric current and passes through characteristic, generate 5 keys Time point t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, there are 2 trough I altogether in opening coil electric current₂₁、I₂₃, 2 wave crest I₂₂、I₂₄, obtain separating brake The local feature vectors component T that coil current is formed in time domain_f1=[t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, I₂₁, I₂₂, I₂₃, I₂₄]；

4th step will denoise opening coil electric current S '_fc(t) set empirical mode decomposition EEMD energy Moment Feature Extraction is carried out, is gone Opening coil of making an uproar electric current S '_fc(t) it is decomposed into m_fA intrinsic mode IMF component, acquires m altogether_fA energy square, then opening coil electric current The global characteristics component of a vector T that signal is formed in time-frequency domain_f2=[E_1f, E_2f... ..., Em_f]；

By local feature vectors component T of the opening coil current signal in time domain_f1=[t₂₁, t₂₂, t₂₃, t₂₄, t₂₅, I₂₁, I₂₂, I₂₃, I₂₄] and global characteristics component of a vector T in time-frequency domain_f2=[E_1f, E_2f... ..., Em_f] fusion constructs opening coil failure Feature vector T_f=[T_f1, T_f2], the dimension of thus obtained closing coil fault feature vector is 9+m_f, through Principal Component Analysis The feature vector T ' obtained after dimensionality reduction_fDimension be λ_fDimension；The feature vector structure after opening coil dimensionality reduction under all working state At opening coil sample dimensionality reduction eigenmatrix, the dimension of closing coil sample dimensionality reduction eigenmatrix is 4r × λ_f；

The total class number of opening coil loop works state identified is 4, special using above-mentioned opening coil sample dimensionality reduction Levy matrix, construct 3 Sub-SVMs, 3 Sub-SVMs again with Polynomial kernel function, gaussian radial basis function and Multi-kernel support vector machine is constituted based on multilayer perceptron kernel function, the nuclear parameter of each kernel function is determined using empirical method, and is adopted Multicore weight coefficient is optimized with genetic algorithm, and then obtains multi-category support vector machines, by the more classification support to Amount machine identifies the working condition of breaker open operation coil, constructs opening coil circuit multi-category support vector machines, passes through Opening coil circuit multi-category support vector machines carry out fault diagnosis to breaker open operation wire loop；

7th step determines opening coil fault degree evaluation index:

As the denoising opening coil electric current S ' for determining input by opening coil circuit multi-category support vector machines_fcIt (t) is event It when hindering current signal, is diagnosed to be in opening coil loop fault and armature travel deficiency and coil voltage deficiency failure occurs, with collection Empirical mode decomposition EEMD energy square relative entropy is closed as fault degree evaluation index, carries out fault degree assessment；

Steps are as follows with respect to the calculating of entropy for the set empirical mode decomposition EEMD energy square of opening coil:

By acquiring denoising opening coil electric current S ' in the 4th step_fc(t) m_fThe m of a intrinsic mode IMF component_fA energy square, i.e., E_1f, E_2f... ..., Em_f；Then according toFind out m_fThe summation E of a energy square_F；

Then basisK '=1_f,2_f,…,m_fFinding out kth ' a energy square accounts for the specific gravity of energy square summation；

Then denoise opening coil electric current S '_fc(t) energy square distribution character matrix are as follows: P=(p_1f,p_2f,…,p_k′,…,p_mf)；

It similarly calculates and denoises opening coil current signal S ' under normal condition_fc(t) energy square distribution character matrix are as follows: P '= (p′_1f,p′_2f,…,p′_k′,…,p′_mf)^；

Finally byObtain failure denoising opening coil current signal S '_fc (t) and under normal condition closing coil current signal S ' is denoised_fc(t) set empirical mode decomposition EEMD energy square relative entropy；

8th step constructs opening coil loop fault degree characteristic curve, every kind of failure is respectively provided with 4 kinds of different failure journeys Degree, the corresponding set empirical modal of armature travel deficiency and coil voltage deficiency failure under being calculated in various degree using the 7th step EEMD energy square is decomposed with respect to entropy, is then fitted using linear function, linear function is formula (17), obtains opening coil Loop fault degree characteristic curve；

Y=b₁x+b₂ (17)

Wherein: x is fault degree, and y is set empirical mode decomposition EEMD energy square with respect to entropy, b₁, b₂For constant；

By the opening coil circuit multi-category support vector machines of above-mentioned building complete to occur in opening coil circuit it is normal, Armature resistance is abnormal, armature travel is insufficient, less than 4 kinds working conditions of coil voltage are diagnosed；When the failure being diagnosed to be is rank It when iron stroke is insufficient or coil voltage deficiency, needs to continue fault degree assessment, passes through separating brake after calculating denoising to be assessed Coil current set empirical mode decomposition EEMD energy square relative entropy, the opening coil loop fault degree for substituting into above-mentioned foundation are special In linearity curve, the qualitative assessment to opening coil fault degree can be completed.

2. the frame-type circuit breaker fault diagnosis and scale evaluation side according to claim 1 based on operation attachment electric current Method, it is characterised in that specific step is as follows for building energy storage motor multi-category support vector machines:

A7-1. the feature vector under normal operating conditions in sample dimensionality reduction eigenmatrix is classified as positive class, under remaining working condition Feature vector be classified as negative class, construct normal Sub-SVM；Transmission gear bite in sample dimensionality reduction eigenmatrix is worked Feature vector under state is classified as positive class, and the feature vector under remaining working condition is classified as negative class, building transmission gear bite Support vector machines；Feature vector under spring bite working condition in sample dimensionality reduction eigenmatrix is classified as positive class, remaining work Feature vector under state is classified as negative class, constructs spring bite Sub-SVM；If above-mentioned 3 Sub-SVMs cannot Effectively identification, then energy storage motor is in spring and falls off working condition；

A7-2. corresponding feature vector in sample dimensionality reduction eigenmatrix is respectively adopted to above-mentioned 3 Sub-SVMs to instruct Practice, rule of thumb method is arranged Polynomial kernel function parameter d in training process, and gaussian radial basis function parameter uses cross validation Mode obtains, and multilayer perceptron kernel functional parameter takes the derivative of sample classification number；Multicore supporting vector is determined in conjunction with genetic algorithm The core weight coefficient of machine, obtains optimal output node, so that the operating mode recognition accuracy of Sub-SVM is reached highest, i.e., Obtain the energy storage motor multi-category support vector machines based on breaker energy storage motor current signal.