CN110376497A - Low-voltage distribution system series fault arc method of identification based on all phase deep learning - Google Patents

Low-voltage distribution system series fault arc method of identification based on all phase deep learning Download PDF

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Publication number
CN110376497A
CN110376497A CN201910739837.0A CN201910739837A CN110376497A CN 110376497 A CN110376497 A CN 110376497A CN 201910739837 A CN201910739837 A CN 201910739837A CN 110376497 A CN110376497 A CN 110376497A
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deep learning
arc
low
current
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冷继伟
陈烜
段卫平
杜刚
肖屏
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State Grid Sichuan Electric Power Service Co Ltd
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State Grid Sichuan Electric Power Service Co Ltd
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing

Abstract

The invention discloses the low-voltage distribution system series fault arc method of identifications based on all phase deep learning, it solves in existing low-voltage network failure, recognition methods for series fault arc is interfered vulnerable to noise, spectrum leakage, influence recognition effect, recognition efficiency is not high, and the problem that stability is not high.The method of the present invention includes: to carry out current signal acquisition to the different loads in low tension loop under low-voltage alternating-current system;All phase discrete Fourier transform is carried out to collected current signal, all phase spectrum signature amount loaded is extracted, and constructs all phase spectrum signature vector;The deep learning neural network model returned based on Logistic is constructed, by carrying out deep learning training to all phase spectrum signature amount under different loads, different operating statuses, until model convergence;The examination to different loads type is completed using trained model and the identification of series fault arc whether occurs.

Description

Low-voltage distribution system series fault arc method of identification based on all phase deep learning
Technical field
The present invention relates to low-voltage distribution system series fault arc identification technology fields, and in particular to is based on all phase depth The low-voltage distribution system series fault arc method of identification of study.
Background technique
In low-voltage distribution system, due to route or equipment insulation ag(e)ing, rupture or electrical missing contact when, be often associated with Fault electric arc, and then cause fire hazard.Low voltage failure electric arc is divided into parallel arc fault and series fault arc, parallel failure Electric arc is caused by short trouble occurs between distribution line, and short circuit current is larger, and protective device correctly can be identified and be acted.String Connection fault electric arc is in contact bad or broken string by route and is caused, and fault curre is 5~30A, is unable to satisfy protection dress The sensitivity requirements set.In practical low-voltage network failure, most indiscernible is also series fault arc.
It to the detection method of series fault arc, can be divided into three classes at present: the emulation 1. based on Cassie Arc Modelling Circuit;The physical features such as light, heat, the sound generated when 2. being occurred using electric arc are judged;3. analyzing electricity when serial arc occurs Stream, voltage signal identify fault electric arc with mathematical method.The 3. for kind method due to wide adaptation range, high reliablity is main to collect In Time-frequency Analysis and feature vector to electric current and voltage signal mathematical analysis etc..Wherein, analysis of time-domain characteristic is Series fault electricity is identified using features such as electric current, the change rate of voltage waveform, the waveform slope value of mutation, " zero stops " times Arc, the disadvantage is that the influence of ambient noise or the pollution of sampled data can all make time domain index generate biggish error, interference judgement As a result.Frequency domain character analysis is spectrum signature, harmonic wave and the m-Acetyl chlorophosphonazo content using signal on frequency domain to determine whether occurring Series fault arc, the disadvantage is that the influence vulnerable to spectrum leakage, determining interference result.The Mathematical Method of feature vector includes Auto-regressive parameter model, fractal dimension and support vector machines, wavelet transformation and singular value decomposition etc., principle and testing process are more multiple Miscellaneous, actually detected middle stability is not high, cannot preferably apply to engineering in practice.
Summary of the invention
The technical problems to be solved by the present invention are: in existing low-voltage network failure, for series fault arc Recognition methods is interfered vulnerable to noise, spectrum leakage, influences recognition effect, and recognition efficiency is not high, and the problem that stability is not high, this Invention provides the low-voltage distribution system series fault arc method of identification based on all phase deep learning to solve the above problems.
The present invention is achieved through the following technical solutions:
Low-voltage distribution system series fault arc method of identification based on all phase deep learning, this method comprises:
Under low-voltage alternating-current system, current signal acquisition is carried out to the different loads in low tension loop;
All phase discrete Fourier transform, all phase spectrum signature amount loaded are carried out to collected current signal It extracts, and constructs all phase spectrum signature vector;
Using all phase spectrum signature vector built up, the deep learning neural network mould returned based on Logistic is constructed Type, by carrying out deep learning training to all phase spectrum signature amount under different loads, different operating statuses, until the model Until convergence;
The deep learning neural network model returned based on Logistic completed using learning training, to be identified point Section load current waveform carries out all phase FFT spectrum analysis measurement, completes the examination to different loads type and whether series fault occurs The identification of electric arc is exported according to the modelEach load is pressed type, normal operation or failure by it Conditions at the arc are numbered,AndWherein u ≠ v, u, v=0,1 ..., 47, indicate the type number for identifying load For round (u+1)/2, operating status is normal arc, and u is even number;Or operating status is fault electric arc, u is odd number;Wherein Round () expression, which takes, to round up.
Working principle is: in existing low-voltage network failure, for series fault arc recognition methods vulnerable to noise, Spectrum leakage interference, influences recognition effect, recognition efficiency is not high, and the problem that stability is not high, and the present invention is using the above scheme Using all phase spectrum signature formation mechenism of load current signal as point of penetration, the spectrum leakage and frequency spectrum interference composed using all phase Rejection extracts the characteristic quantity of distorted signal and normal signal on all phase frequency spectrum;Building is based on Logistic The deep learning neural network model of recurrence, using the deep learning classifying identification method returned based on Logistic, to input All phase spectrum signature differentiated, realize examination to load type, the electric arc that whether breaks down;Utilize neural network Non-linear, adaptivity and fault-tolerant ability carry out intelligence to series fault arc, efficiently identify.The present invention is for series connection event The recognition methods for hindering electric arc is not interfered vulnerable to noise, spectrum leakage, and recognition efficiency is high, and stability is strong.
Further, the low-voltage alternating-current system includes power supply unit, failure generating unit, data sampling analytical unit, The power supply unit uses low-voltage alternating-current experiment power supply AC, the low-voltage alternating-current experiment power supply AC to become by switch K1 connection isolation Primary coil one end of depressor, the primary coil other end ground connection of isolating transformer, secondary coil one end of isolating transformer connects Arc generator is connect, arc generator connects resistance R1, resistance R1 connection slide rheostat Rp, rheostat RpConnected by switch K2 Connect the secondary coil other end of isolating transformer;The arc generator is used to generate stable series fault arc, and described Switch K3 is parallel on arc generator;Data acquisition unit DA, the data acquisition unit DA are connected on the resistance R1 It connects computer PC and realizes analysis;Wherein, slide rheostat Rp, resistance R1Respectively indicate load and sampling resistor.
Further, described that all phase discrete Fourier transform is carried out to collected current signal, wherein final full phase The frequency offset formula of position discrete Fourier transform are as follows:
In formula: G (ki)、Y(ki) it is respectively frequency peak spectral line serial number kiCorresponding discrete Fourier transform and all phase are discrete Fourier transformation spectral vector value,For corresponding phase spectrum, βi-kiFor peak spectral line kiThe frequency at place is inclined Shifting amount, N are random natural number.
Specifically, the derivation process of the frequency offset formula of final all phase discrete Fourier transform is as follows:
For low-voltage alternating-current system, current signal acquisition, actual current letter are carried out to the different loads in low tension loop Number it is expressed as the linear superposition of multiple signal components, load current sampled value y (n Δ t) formula is as follows:
(1) in formula: n ∈ [- N+1, N-1], 2N-1 are analysis data length;Δ t is sampling interval, ωi、Ai、piRespectively Angular frequency, amplitude and the initial phase of i signal component, M are signal component sum.
Discrete Fourier transform is carried out to the current signal in formula (1) respectively and all phase discrete Fourier transform (is ignored The influence of negative frequency), respective spectrum expression formula are as follows:
(2) in (3) formula: G (k) is discrete Fourier transform frequency spectrum, and Y (k) is all phase discrete Fourier transform frequency spectrum, k For spectral line serial number, βii/ Δ ω, Δ ω are angular frequency resolution ratio, ωiFor the angular frequency value of i frequency component, W (βi- k) be Frequency offset βi- k locates corresponding rectangular window spectral function, AiFor the amplitude of i signal component, piFor the initial phase of i signal component, M For signal component sum;Wherein
If frequency component ωiSpectral magnitude peak spectral line serial number ki, in (4) formula: βi-kiFor peak spectral line kiThe frequency at place is inclined Shifting amount, N are random natural number, βii/ Δ ω, Δ ω are angular frequency resolution ratio, ωiFor the angular frequency value of i frequency component.By This, can be derived by the frequency offset of final all phase discrete Fourier transform are as follows:
(5) in formula: G (ki)、Y(ki) it is respectively frequency peak spectral line serial number kiCorresponding discrete Fourier transform and all phase Discrete Fourier transform spectral vector value,For corresponding phase spectrum, βi-kiFor peak spectral line kiThe frequency at place Offset, N are random natural number.
The then angular frequency value of i signal frequency component are as follows:
ωiiΔω (6)
The corresponding amplitude of i signal frequency component are as follows:
(7) in formula: G (k) is discrete Fourier transform frequency spectrum, and Y (k) is all phase discrete Fourier transform frequency spectrum, and k is spectrum Line sequence number.
Formula (6) (7) is the corresponding angular frequency value of i signal frequency component and width based on all phase discrete Fourier transform It is worth calculation formula.From formula (2) (3) as can be seen that when spectrum leakage occur, the amplitude of all phase discrete Fourier transform occurs Decay at double, this is also the frequency spectrum of all phase discrete Fourier transform with good spectrum leakage and frequency spectrum interference rejection Basic reason, therefore the frequency component measured has very high precision.
Further, all phase spectrum signature amount loaded is extracted, and is specifically included when load operates normally Current spectrum feature extraction under current spectrum feature and series fault arc, by electric to operating normally and series fault occurring Current waveform when arc carries out all phase spectrometry, extracts spectrum signature of the different loads under different conditions;By to each time The amplitude distribution of harmonic component and the frequency of the m-Acetyl chlorophosphonazo component to play a leading role, the identification of the feature of amplitude distribution situation, are realized Whether break down the examination of electric arc to different loads;It the amplitude distribution by each harmonic component and plays a leading role The frequency of m-Acetyl chlorophosphonazo component, the identification of the feature of amplitude distribution situation, wherein the specific features of extraction specifically include it is leading between it is humorous Wave frequency rate, leading m-Acetyl chlorophosphonazo content, harmonic content.
Further, building all phase spectrum signature vector is by dominating m-Acetyl chlorophosphonazo frequency fIh-m, leading m-Acetyl chlorophosphonazo content AIh-m With harmonic content AH-mForm 150 dimensional feature vector X=[fIh-m, AIh-m, AH-m], wherein m=1,2 ..., 50, Ih-m indicate m A m-Acetyl chlorophosphonazo, H-m indicate m-th of harmonic wave.
Further, all phase spectrum signature vector that the utilization is built up constructs the depth returned based on Logistic Learning neural network model, specific as follows:
The present invention is based on the deep learning methods that Logistic is returned, this is because using traditional neural network to event When hindering electric arc progress discriminant analysis, Accurate classification can not be carried out to complicated sample, while the convergence of result is by initial value shadow The case where sound is larger, is as a result easily trapped into local optimum or overfitting or even does not restrain.And Logistic is returned not by initial It is worth the influence chosen, result is still highly stable when initial value is unsatisfactory for assuming, has stronger robustness, therefore the present invention adopts Sample training is carried out with the deep learning method returned based on Logistic.
To screen whether different loads generate fault electric arc, 50 subharmonic contents as defined in IEC61000-4-7 standard are selected AH-mAs characteristic parameter (m=1,2 ..., 50), while choose two subharmonic between leading m-Acetyl chlorophosphonazo frequency fIh-m, content AIh-mAs characteristic parameter, 150 dimensional feature vector X=[f are collectively constitutedIh-m,AIh-m,AH-m] it is the defeated of neural network deep learning Enter parameter, deep learning neural network includes 5 layers of hidden layer, the output response variable of deep learning neural network Each sample is loaded and is numbered by type, normal operation or fault electric arc state by it,And The type number for indicating to identify load is round (u+1)/2, and operating status is normal (u For even number) or fault electric arc (u is odd number), wherein round () expression, which takes, rounds up.
Logistic regression model in the deep learning neural network model returned based on Logistic is non-thread Property regression model, have following distribution form:
(8) in formula: E (X) indicates probability distribution, and p is the prediction probability value of corresponding input variable X, and ε is prediction error, β0 And β1For the nonlinear regression coefficient of model;
Logistic recurrence is the prediction probability value p for calculating Y=1 to the different level of input variable X, wherein probability value Cut-point, formula are used as using 0.5 are as follows:
(9) in formula: u=0,1 ..., 47;yu=1 indicates that (u is surprise for normal condition (u is even number) or fault electric arc state Number).
The present invention has the advantage that and the utility model has the advantages that
1, the present invention is using all phase spectrum signature formation mechenism of load current signal as point of penetration, to distorted signal and normally Characteristic quantity of the signal on all phase frequency spectrum extracts, not vulnerable to noise, spectrum leakage, spectral interference;It is composed based on all phase The measurement method of analysis can effectively extract current characteristic parameter under the various operating statuses of load, mention for the identification of fault electric arc For theoretical foundation;
2, the deep learning neural network model that present invention building is returned based on Logistic, neural network have non-thread Property, adaptivity and fault-tolerant ability, prototype network can stablize, fast convergence;Utilize the depth returned based on Logistic Classifying identification method is practised, all phase spectrum signature of input is differentiated, is realized to load type, whether break down electric arc Examination;
3, the present invention for series fault arc recognition methods process it is reasonable, easy for operation, this method not vulnerable to Noise, spectrum leakage interference, recognition efficiency is high, and stability is strong.
Detailed description of the invention
Attached drawing described herein is used to provide to further understand the embodiment of the present invention, constitutes one of the application Point, do not constitute the restriction to the embodiment of the present invention.In the accompanying drawings:
Fig. 1 is the low-voltage distribution system series fault arc method of identification process of the invention based on all phase deep learning Figure.
Fig. 2 is series fault arc test platform schematic diagram of the invention.
Fig. 3 (a) is the normal current waveform figure of linear load of the invention.
Fig. 3 (b) is the normal current spectrum figure of linear load of the invention.
Fig. 4 (a) is the normal current waveform figure of nonlinear load of the invention.
Fig. 4 (b) is the normal current spectrum figure of nonlinear load of the invention.
Current waveform figure when Fig. 5 (a) is linear load fault electric arc of the invention.
Current spectrum figure when Fig. 5 (b) is linear load fault electric arc of the invention.
Current waveform figure when Fig. 6 (a) is nonlinear load fault electric arc of the invention.
Current spectrum figure when Fig. 6 (b) is nonlinear load fault electric arc of the invention.
Fig. 7 is the deep learning neural network model of the invention returned based on Logistic.
Fig. 8 is the deep learning training error figure of neural network of the invention.
Fig. 9 is the network training output error figure of the invention with other each methods.
Figure 10 (a) is the content tendency chart of each harmonic of the invention.
Figure 10 (b) is the content tendency chart of each secondary m-Acetyl chlorophosphonazo of the invention.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below with reference to embodiment and attached drawing, to this Invention is described in further detail, and exemplary embodiment of the invention and its explanation for explaining only the invention, are not made For limitation of the invention.
Embodiment
As shown in Fig. 1 to Figure 10 (b), the low-voltage distribution system series fault arc identification based on all phase deep learning Method, this method comprises:
Under low-voltage alternating-current system, current signal acquisition is carried out to the different loads in low tension loop;
All phase discrete Fourier transform, all phase spectrum signature amount loaded are carried out to collected current signal It extracts, and constructs all phase spectrum signature vector;
Using all phase spectrum signature vector built up, the deep learning neural network mould returned based on Logistic is constructed Type, by carrying out deep learning training to all phase spectrum signature amount under different loads, different operating statuses, until the model Until convergence;
The deep learning neural network model returned based on Logistic completed using learning training, to be identified point Section load current waveform carries out all phase FFT spectrum analysis measurement, completes the examination to different loads type and whether series fault occurs The identification of electric arc.
Specifically, described under low-voltage alternating-current system, current signal acquisition, electricity are carried out to the different loads in low tension loop Stream signal is expressed as the linear superposition of multiple signal components, wherein load current samples formula are as follows:
In formula: n ∈ [- N+1, N-1], 2N-1 are analysis data length;Δ t is sampling interval, ωi、Ai、piRespectively i letter Angular frequency, amplitude and the initial phase of number component, M are signal component sum.
Specifically, described that all phase discrete Fourier transform is carried out to collected current signal, wherein final all phase The frequency offset formula of discrete Fourier transform are as follows:
In formula: G (ki)、Y(ki) it is respectively frequency peak spectral line serial number kiCorresponding discrete Fourier transform and all phase are discrete Fourier transformation spectral vector value,For corresponding phase spectrum, βi-kiFor peak spectral line kiThe frequency at place is inclined Shifting amount, N are random natural number.
Specifically, all phase spectrum signature amount loaded is extracted, and specifically includes electricity when load operates normally The current spectrum feature extraction under spectrum signature and series fault arc is flowed, by normal operation and generation series fault arc When current waveform carry out all phase spectrometry, extract spectrum signature of the different loads under different conditions;By humorous to each time The amplitude distribution of wave component and the frequency of the m-Acetyl chlorophosphonazo component to play a leading role, the identification of the feature of amplitude distribution situation, realization pair Whether different loads break down the examination of electric arc.It the amplitude distribution by each harmonic component and plays a leading role The frequency of m-Acetyl chlorophosphonazo component, the identification of the feature of amplitude distribution situation, wherein the specific features of extraction specifically include leading m-Acetyl chlorophosphonazo Frequency, leading m-Acetyl chlorophosphonazo content, harmonic content.
The all phase spectrum signature amount that is loaded is extracted, specifically include current spectrum feature when load operates normally and Current spectrum feature extraction under series fault arc, wherein all phase spectrum signature amount analytic process is as follows:
(1) current spectrum feature when load operates normally
According to all phase frequency spectrum formation mechenism above-mentioned, to the corresponding linear load of formula (1), nonlinear load current signal It carries out all phase discrete Fourier transform to go forward side by side the frequency and amplitude measurement of row-frequency component, obtains its time domain waveform and spectrogram Respectively as shown in Fig. 3 (a), Fig. 3 (b) and Fig. 4 (a), Fig. 4 (b).
From Fig. 3 (a), Fig. 3 (b) and Fig. 4 (a), Fig. 4 (b) as can be seen that waveform is close ideal when linear load operates normally Sine wave, contain only fundametal compoment in spectrogram, all phase spectrum measurement result contains only fundamental voltage amplitude;Nonlinear load is normally transported More regular cyclic distortion occurs for waveform when row, and each spectrum line in spectrogram forms spectral band, all phase spectrometry knot Fruit includes each harmonic component and fewer parts m-Acetyl chlorophosphonazo component, and wherein odd-order harmonic components are more prominent.Load operates normally When extracted by the spectrum signature that linear load and nonlinear load may be implemented in all phase spectrometry method, and it is different types of Load has different spectrum signatures, therefore can distinguish under normal operating condition different types of linear load and non-linear negative It carries.
(2) the current spectrum feature under series fault arc
By building series fault arc analogue test platform, the conventional linear under series fault arc is loaded, is non-thread Property load current signal waveform carry out time and frequency domain analysis, obtain waveform diagram and spectrogram respectively such as Fig. 5 (a), Fig. 5 (b) and Shown in Fig. 6 (a), Fig. 6 (b).
From 5 (a), Fig. 5 (b) and Fig. 6 (a), Fig. 6 (b) as can be seen that linear load, nonlinear load are in series fault electricity Irregularity distortion occurs for waveform when arc, and waveform carries out all phase spectrum measurement with burr, to it, obtains humorous comprising each time The m-Acetyl chlorophosphonazo component of wave component and different frequency.Comparison diagram 3 (a), Fig. 3 (b) and Fig. 4 (a), Fig. 4 (b) simultaneously, when connecting Current waveform when fault electric arc can be regarded as the polyteny superposition of the current distortion waveform when operating normally, aggravate letter Number distortion degree, form spectral band on spectrogram, and with significantly different frequency, the m-Acetyl chlorophosphonazo component of amplitude and outstanding Harmonic component (such as 3 times, 5 subharmonic).
By carrying out all phase spectrometry to current waveform when operating normally and series fault arc occurs, can extract To spectrum signature of the different loads under different conditions.By the amplitude distribution to each harmonic component and between playing a leading role The frequency of harmonic component, the identification of the feature of amplitude distribution situation, the Zhen for the electric arc that may be implemented whether to break down to different loads Not.
The building all phase spectrum signature vector is by dominating m-Acetyl chlorophosphonazo frequency fIh-m, leading m-Acetyl chlorophosphonazo content AIh-mIt is harmonious Wave content AH-mForm 150 dimensional feature vector X=[fIh-m, AIh-m, AH-m], wherein m=1,2 ..., 50, Ih-m indicate m-th between Harmonic wave, H-m indicate m-th of harmonic wave.
The all phase spectrum signature vector that the utilization is built up constructs the deep learning nerve net returned based on Logistic Network model, specific as follows:
150 dimensional feature vector X=[fIh-m, AIh-m, AH-m] input parameter as neural network deep learning, deep learning Neural network includes 5 layers of hidden layer, the output response variable of deep learning neural networkIt will Each sample load is numbered by type, normal operation or fault electric arc state,And The type number for indicating to identify load is round (u+1)/2, operating status be normal (u is even number) or Fault electric arc (u is odd number), wherein round () expression, which takes, rounds up.
Logistic regression model in the deep learning neural network model returned based on Logistic is non-thread Property regression model, have following distribution form:
In formula: E (X) indicates probability distribution, and p is the prediction probability value of corresponding input variable X, and ε is prediction error, β0And β1 For the nonlinear regression coefficient of model;
Logistic recurrence is the prediction probability value p for calculating Y=1 to the different level of input variable X, wherein probability value Cut-point, formula are used as using 0.5 are as follows:
U=0 in formula, 1 ..., 47;yu=1 indicates normal condition (u is even number) or fault electric arc state (u is odd number).
As shown in fig. 7, for neural network training process in the deep learning network model returned based on Logistic, examination Sample is tested using resistance box, insulating pot, incandescent lamp, power drill, the micro-wave oven, vacuum met under the different capacity of UL1699 standard Total 24 kinds of loads such as dust catcher, LED light, fluorescent lamp, desk lamp with dimmer switch, and type, normal operation or fault electric arc are pressed into each load State is numbered (0~47), takes 100 normal operation waveform samples, 100 fault electric arc waveform samples under every kind of load, Total 4800 samples carry out neural network deep learning shown in Fig. 7.Sample is finally restrained, and does not occur traditional neural network The case where not restraining, e-learning accuracy is up to 100%.Neural network is returning the training in deep learning based on Logistic Error mean square value as shown in figure 8, with deep learning frequency of training increase, network error gradient decline index it is more satisfactory, net Network can stablize, fast convergence.
Depth is returned using traditional BP neural network (method 1), genetic algorithm neural network (method 2) and Logistisc The output error of learning neural network (method 3) is as shown in Figure 9.
As seen from Figure 9, returning deep learning neural network (method 3) based on Logistic has preferable convergence Property, the rapidity and the more other two methods of stability in convergence process are more excellent.Traditional BP neural network convergence is avoided simultaneously Slowly, the neural network based on genetic algorithm restrains unstable disadvantage.
Therefore, the fault electric arc identification process figure based on all phase spectrum and deep learning is as shown in Figure 1.
For the low-voltage distribution system series fault arc method of identification of the invention based on all phase deep learning, for simulation Electrical quantity situation of change when true generation series fault arc builds series fault electricity according to UL1699-2008 AFCI standard Arc analogue test platform is as shown in Fig. 2, the low-voltage alternating-current system includes power supply unit, failure generating unit, data sampling point Unit is analysed, the power supply unit uses low-voltage alternating-current experiment power supply AC, the low-voltage alternating-current experiment power supply AC to connect by switch K1 Connect primary coil one end of isolating transformer, the primary coil other end ground connection of isolating transformer, the secondary sideline of isolating transformer It encloses one end and connects arc generator, arc generator connects resistance R1, resistance R1 connection slide rheostat Rp, rheostat RpPass through The secondary coil other end of switch K2 connection isolating transformer;Rp、R1=0.1 Ω respectively indicates test load and sampling resistor;Institute Arc generator is stated for generating stable series fault arc, and is parallel with switch K3 on the arc generator;The electricity Data acquisition unit DA is connected on resistance R1, the data acquisition unit DA connection computer PC realizes analysis.
To 4 sections of load current waves of State Grid Sichuan Electric Power Company's production and operation central data where the test platform of building Shape carries out all phase FFT spectrum analysis measurement, and the variation for obtaining each (with highest 10 times for example) harmonic wave, leading m-Acetyl chlorophosphonazo content becomes Gesture such as Figure 10 (a), shown in 10 (b).
The visible characteristic quantity based on all phase FFT spectrum analysis is more apparent in Figure 10 (a), the harmonic content concentration of signal 1, signal 3 It is distributed at odd harmonics, and the harmonic content of signal 1 is integrally minimum, highest content about 2.5%, the harmonic content of signal 3 Whole higher, highest content about 26.6%;Signal 2, signal 4 are distributed at each harmonic, from the point of view of harmonic content, signal 4 integral level is higher than signal 2.See that signal 2, the m-Acetyl chlorophosphonazo content of signal 4 are whole higher from Figure 10 (b), signal 1 and letter Numbers 3 m-Acetyl chlorophosphonazo content is integrally lower.Further stimulus is quantified, obtains the feature value part composed based on all phase Measurement result is as shown in table 1.
Part measurement result of the table 1 based on all phase FFT spectrum analysis
Above-mentioned test result shows that the measurement method based on all phase FFT spectrum analysis can effectively extract the various operations of load Current characteristic parameter under state provides theoretical foundation for the identification of fault electric arc.
By the leading m-Acetyl chlorophosphonazo frequency f based on all phase spectrometryIh-m, content AIh-mWith harmonic content AH-m150 dimension of composition Feature vector (m=1,2 ..., 50), as be completed in Fig. 7 Logistic return deep learning nerve network input parameter, Output and the fault electric arc recognition result for obtaining 4 sections of stimulus are as shown in table 2.The conclusion (of pressure testing) is consistent with test preset condition It closes, demonstrates the correct of the low-voltage distribution system series fault arc method of identification based on all phase deep learning that the present invention is mentioned Property and validity, realize the identification to load type, the electric arc that whether breaks down, and recognition efficiency is high, method stability is strong.
The test result of each segment signal of table 2
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include Within protection scope of the present invention.

Claims (9)

1. the low-voltage distribution system series fault arc method of identification based on all phase deep learning, which is characterized in that this method packet It includes:
Under low-voltage alternating-current system, current signal acquisition is carried out to the different loads in low tension loop;
All phase discrete Fourier transform is carried out to collected current signal, all phase spectrum signature amount loaded mentions It takes, and constructs all phase spectrum signature vector;
Using all phase spectrum signature vector built up, the deep learning neural network model returned based on Logistic is constructed, By carrying out deep learning training to all phase spectrum signature amount under different loads, different operating statuses, until the model is received Until holding back;
The deep learning neural network model returned based on Logistic completed using learning training, it is negative to segmentation to be identified It carries current waveform and carries out all phase FFT spectrum analysis measurement, carry out the examination to different loads type and whether series fault arc occurs Identification, according to the model exportEach load is pressed type, normal operation or fault electric arc by it State is numbered,AndWherein u ≠ v, u, v=0,1 ..., 47, indicate to identify that the type number of load is Round (u+1)/2, operating status are normal arc, and u is even number;Or operating status is fault electric arc, u is odd number;Wherein Round () expression, which takes, to round up.
2. the low-voltage distribution system series fault arc method of identification according to claim 1 based on all phase deep learning, It is characterized in that, it is described under low-voltage alternating-current system, current signal acquisition, electric current letter are carried out to the different loads in low tension loop Number it is expressed as the linear superposition of multiple signal components, wherein load current samples formula are as follows:
In formula: n ∈ [- N+1, N-1], 2N-1 are analysis data length;Δ t is sampling interval, ωi、Ai、piRespectively i signal point Angular frequency, amplitude and the initial phase of amount, M are signal component sum.
3. the low-voltage distribution system series fault arc method of identification according to claim 1 based on all phase deep learning, It is characterized in that, described carry out all phase discrete Fourier transform to collected current signal, wherein final all phase is discrete The frequency offset formula of Fourier transformation are as follows:
In formula: G (ki)、Y(ki) it is respectively frequency peak spectral line serial number kiCorresponding discrete Fourier transform and all phase direct computation of DFT Leaf transformation spectral vector value,For corresponding phase spectrum, βi-kiFor peak spectral line kiThe frequency offset at place, N is random natural number.
4. the low-voltage distribution system series fault arc method of identification according to claim 1 based on all phase deep learning, It is characterized in that, the low-voltage alternating-current system includes power supply unit, failure generating unit, data sampling analytical unit, the electricity Source unit uses low-voltage alternating-current experiment power supply AC, the low-voltage alternating-current experiment power supply AC to pass through switch K1 connection isolating transformer Primary coil one end, the primary coil other end ground connection of isolating transformer, secondary coil one end of isolating transformer connects electric arc Generator, arc generator connect resistance R1, resistance R1 connection slide rheostat Rp, rheostat RpIt is isolated by switch K2 connection The secondary coil other end of transformer;The arc generator is for generating stable series fault arc, and the electric arc is sent out Switch K3 is parallel on raw device;Data acquisition unit DA, the data acquisition unit DA connection meter are connected on the resistance R1 Calculation machine PC realizes analysis;Wherein, slide rheostat Rp, resistance R1Respectively indicate load and sampling resistor.
5. the low-voltage distribution system series fault arc method of identification according to claim 1 based on all phase deep learning, It is characterized in that, all phase spectrum signature amount loaded is extracted, electric current frequency when load operates normally is specifically included Current spectrum feature extraction under spectrum signature and series fault arc, when by normal operation and generation series fault arc Current waveform carries out all phase spectrometry, extracts spectrum signature of the different loads under different conditions;By to each harmonic point The amplitude distribution of amount and the frequency of the m-Acetyl chlorophosphonazo component to play a leading role, the identification of the feature of amplitude distribution situation, are realized to difference Load the examination for the electric arc that whether breaks down.
6. the low-voltage distribution system series fault arc method of identification according to claim 5 based on all phase deep learning, It is characterized in that, the amplitude distribution by each harmonic component and the frequency of the m-Acetyl chlorophosphonazo component to play a leading role, width The feature of Distribution value situation identifies, wherein the specific features of extraction specifically include leading m-Acetyl chlorophosphonazo frequency, leading m-Acetyl chlorophosphonazo contains Amount, harmonic content.
7. the low-voltage distribution system series fault arc method of identification according to claim 6 based on all phase deep learning, It is characterized in that, the building all phase spectrum signature vector is by dominating m-Acetyl chlorophosphonazo frequency fIh-m, leading m-Acetyl chlorophosphonazo content AIh-mWith Harmonic content AH-mForm 150 dimensional feature vector X=[fIh-m, AIh-m, AH-m], wherein m=1,2 ..., 50, Ih-m indicate m-th M-Acetyl chlorophosphonazo, H-m indicate m-th of harmonic wave.
8. the low-voltage distribution system series fault arc method of identification according to claim 7 based on all phase deep learning, It is characterized in that, all phase spectrum signature vector that the utilization is built up, constructs the deep learning mind returned based on Logistic It is specific as follows through network model:
150 dimensional feature vector X=[fIh-m, AIh-m, AH-m] input parameter as neural network deep learning, deep learning nerve Network includes 5 layers of hidden layer, the output response variable of deep learning neural networkIt is by various kinds This load is numbered by type, normal operation or fault electric arc state,AndWherein u ≠ v, u, v=0,1 ..., 47, indicate to identify that the type number of load is round (u+1)/2, operating status is normal arc, and u is even number;Or operation State is fault electric arc, and u is odd number;Wherein round () expression, which takes, rounds up.
9. the low-voltage distribution system series fault arc method of identification according to claim 8 based on all phase deep learning, It is characterized in that, the Logistic regression model in the deep learning neural network model returned based on Logistic is non- Linear regression model (LRM) has following distribution form:
In formula: E (X) indicates probability distribution, and p is the prediction probability value of corresponding input variable X, and ε is prediction error, β0And β1For mould The nonlinear regression coefficient of type;
Logistic recurrence is the prediction probability value p for calculating Y=1 to the different level of input variable X, and wherein probability value uses 0.5 is used as cut-point, formula are as follows:
U=0 in formula, 1 ..., 47;yu=1 indicates normal condition, and u is even number;Or yu=1 indicates fault electric arc state, and u is surprise Number.
CN201910739837.0A 2019-08-12 2019-08-12 Low-voltage distribution system series fault arc method of identification based on all phase deep learning Pending CN110376497A (en)

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