CN115144742A - Method and device for distinguishing mechanical state of circuit breaker energy storage system - Google Patents
Method and device for distinguishing mechanical state of circuit breaker energy storage system Download PDFInfo
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Abstract
The application relates to a method and a device for judging the mechanical state of a circuit breaker energy storage system, wherein the method comprises the following steps: acquiring a motor current signal and a vibration signal of the circuit breaker; extracting current characteristics of the motor current signal; extracting vibration characteristics of the vibration signal; determining a feature vector of the circuit breaker based on the current feature and the vibration feature; and judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector. The scheme of the application integrates the current characteristic and the vibration characteristic of the energy storage motor of the novel environment-friendly gas circuit breaker, the state information of the energy storage system can be more comprehensively reflected by the integration of the current characteristic and the vibration characteristic, the extracted characteristic quantity has complementarity, and the diagnosis effect can be improved; the scheme has the advantages that the current signal is easy to obtain, the operation of the circuit breaker is not interfered, and the vibration signal can realize the advantage of non-invasive monitoring on the state of the circuit breaker, and the calculation amount is small, so that the method has high practicability.
Description
Technical Field
The application relates to the technical field of electrical engineering, in particular to a method and a device for judging the mechanical state of a circuit breaker energy storage system.
Background
With the increasing proportion of new generation high voltage circuit breakers using new environmental protection gas as insulating and arc extinguishing medium in switchgear, the reliability of their operation becomes more and more important for the safe operation of the whole power system. At present, a plurality of researches focus on solving the problem of the operation process of the breaker, the fault research in the energy storage process is not deep enough, and the basis for quantitatively evaluating the conversion, transmission and storage processes from electric energy to mechanical energy is lacked. However, defects in any link of energy storage can affect the opening and closing operations of the circuit breaker, the energy storage system is used as an important component of the circuit breaker, and the mechanical state of the energy storage system is an important factor for determining the action reliability of the circuit breaker. Once the energy storage system fails, the energy storage is not smooth and the opening and closing speed is reduced, so that the action performance of the circuit breaker is reduced; and if so, malfunction occurs or failure occurs, so that serious accidents are caused. Therefore, the method has important significance for identifying the mechanical state fault of the novel environment-friendly gas circuit breaker energy storage system.
The current state monitoring and fault diagnosis method mainly focuses on a vacuum circuit breaker and an SF6 circuit breaker, and the commonly used monitoring characteristics mainly comprise energy storage motor current characteristics, vibration signal characteristics, contact travel-time characteristics and opening and closing coil current characteristics, wherein motor current signals are commonly used for analyzing the fault problem of a motor, but the research on the application of the motor current signals to the mechanical state judgment of a mechanism connected with the motor is less.
In the correlation technique, the research on the energy storage system mechanical state discrimination of the novel environment-friendly gas circuit breaker is less, and the defects of low identification accuracy and low universality exist.
Disclosure of Invention
In order to overcome the problems of low identification accuracy and low universality in the related technology at least to a certain extent, the application provides a method and a device for judging the mechanical state of a circuit breaker energy storage system.
According to a first aspect of the embodiments of the present application, a method for determining a mechanical state of a circuit breaker energy storage system is provided, including:
acquiring a motor current signal and a vibration signal of the circuit breaker;
extracting current characteristics of the motor current signal; the current characteristics include: an envelope and/or kurtosis;
extracting vibration characteristics of the vibration signal; the vibration feature includes: box dimension;
determining a feature vector of the circuit breaker based on the current feature and the vibration feature;
and judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector.
Further, before extracting the current characteristic of the motor current signal, the method further includes: performing wavelet denoising on the motor current signal/the vibration signal;
the wavelet denoising method comprises the following steps:
determining a decomposition scale n according to the frequency of the motor current signal and/or the vibration signal;
performing n-layer decomposition on the motor current signal/the vibration signal by adopting a preset wavelet basis function;
and processing the decomposed signals through a preset threshold function.
Further, extracting a current signature of the motor current signal comprises:
extracting the motor current an envelope of the signal;
a kurtosis is calculated based on the envelope.
Further, extracting an envelope of the motor current signal includes:
performing Hilbert transform on a motor current signal x (t) to obtain a transformed signal y (t);
obtaining an analytic signal Z (t) of the motor current signal based on x (t) and y (t);
the mode of the analytic signal Z (t) is the envelope a (t) of the signal.
Further, the kurtosis calculation method comprises the following steps:
wherein x is the instantaneous value of the current envelope, mu is the mean value of the envelope, p (x) is the probability density, and sigma is the standard deviation.
Further, extracting the vibration characteristics of the vibration signal comprises:
decomposing said vibration signal into the form of a sum of a plurality of product functions and a residual component;
the box dimension is determined based on a frequency domain distribution law of the plurality of product functions.
Further, decomposing the vibration signal into a form of a sum of a plurality of product functions and a residual component includes:
taking the vibration signal as an original signal x (t);
determining all local extreme points of the original signal;
obtaining pure frequency-modulated signal s based on local extremum points 1n (t)；
Based on purely frequency-modulated signals s 1n (t) obtaining a first product function PF 1 (t)；
Separation of PF from the original signal x (t) 1 (t) obtaining a signal y 1 (t)；
Will y 1 (t) repeating the above steps as the original signalK times until y k (t) is a monotonic function;
the vibration signal is then:
further, a pure frequency modulation signal s is obtained based on the local extreme point 1n (t) comprising:
calculating the average value m of all adjacent local extreme points i And an envelope estimate a i ；
Smoothing by adopting a moving average method to obtain a local mean function m 11 (t) and an envelope estimation function a 11 (t)；
Separating m from the original signal x (t) 11 (t) obtaining h 11 (t) and dividing by the envelope estimation function to obtain a frequency modulated signal s 11 (t)；
Calculating s 11 Envelope estimation function a of (t) 12 (t) if a 12 (t) is not equal to 1, repeating the iteration n times until s 1n (t) is a pure frequency modulated signal.
Further, the judging the mechanical state of the energy storage system of the circuit breaker based on the characteristic vector comprises the following steps:
acquiring a preset sample set;
calculating the feature vector X i With each sample Y in the set of samples j The Euclidean distance of;
finding out K samples with the minimum distance;
determining a weight value of each of the K samples based on a weight function;
the feature vector X is combined i And classifying the data into the category with the largest weight value.
According to a second aspect of the embodiments of the present application, there is provided an apparatus for determining a mechanical state of a circuit breaker energy storage system, including:
the acquisition module is used for acquiring a motor current signal and a vibration signal of the circuit breaker;
the first extraction module is used for extracting the current characteristics of the motor current signal; the current characteristics include: an envelope and/or kurtosis;
the second extraction module is used for extracting the vibration characteristics of the vibration signal; the vibration characteristics include: box dimension;
a determination module to determine a feature vector of the circuit breaker based on the current feature and the vibration feature;
and the judging module is used for judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
the scheme of the application integrates the current characteristic and the vibration characteristic of the energy storage motor of the novel environment-friendly gas circuit breaker, the state information of an energy storage system can be more comprehensively reflected by the integration of the current characteristic and the vibration characteristic, the extracted characteristic quantity has complementarity, and the diagnosis effect can be improved; the scheme has the advantages that the current signals are easy to obtain and cannot interfere with the operation of the circuit breaker, the vibration signals can realize the advantage of non-invasive monitoring of the state of the circuit breaker, the calculated amount is small, and the practicability is high.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart illustrating a method for determining a mechanical state of a circuit breaker energy storage system according to an exemplary embodiment.
FIG. 2 is a detailed flow diagram illustrating a discrimination method according to an exemplary embodiment.
Fig. 3 is a block diagram illustrating an apparatus for determining a mechanical state of a circuit breaker energy storage system according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of methods and apparatus consistent with certain aspects of the present application, as detailed in the appended claims.
At present, the methods for identifying the fault of the mechanical state of the energy storage system are few, and an energy storage motor current signal analysis method, a vibration signal analysis method and the like are available. Although the motor current method has the advantages that the current signal is easy to obtain and the operation of the circuit breaker is not interfered, the mechanical state of the circuit breaker operating mechanism is less; the vibration signature method, while enabling non-intrusive monitoring of the state of the circuit breaker, is sensitive to the mounting location of the sensor and susceptible to mechanical resonance.
Because the motor current signal of novel environmental protection gas circuit breaker energy storage process accompanies the vibration signal simultaneously, consequently need urgent research one kind and fuse two kinds of signals and carry out the diagnostic method of circuit breaker energy storage mechanism mechanical state, remain the advantage of two kinds of methods, alleviate the influence of its shortcoming. In the fusion process, the weight of different feature vectors is determined by adopting an entropy weight method, so that subjective factor errors are avoided, and the accuracy of mechanical state identification is improved.
Fig. 1 is a flowchart illustrating a method for determining a mechanical state of an energy storage system of a circuit breaker according to an exemplary embodiment. The method may comprise the steps of:
s1, acquiring a motor current signal and a vibration signal of a circuit breaker;
s2, extracting current characteristics of the motor current signal; the current characteristics include: an envelope and/or kurtosis;
s3, extracting vibration characteristics of the vibration signals; the vibration characteristics include: box dimension;
s4, determining a feature vector of the circuit breaker based on the current feature and the vibration feature;
and S5, judging the mechanical state of the energy storage system of the circuit breaker based on the characteristic vector.
The scheme of the application integrates the current characteristic and the vibration characteristic of the energy storage motor of the novel environment-friendly gas circuit breaker, the state information of the energy storage system can be more comprehensively reflected by the integration of the current characteristic and the vibration characteristic, the extracted characteristic quantity has complementarity, and the diagnosis effect can be improved; the scheme has the advantages that the current signals are easy to obtain and cannot interfere with the operation of the circuit breaker, the vibration signals can realize the advantage of non-invasive monitoring of the state of the circuit breaker, the calculated amount is small, and the practicability is high.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. Unless explicitly stated otherwise herein, the steps are not performed in a strict order, the steps may be performed in other orders. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings. In practical applications, the scheme of the present application may include the following steps, as shown in fig. 2.
Step 1: wavelet denoising method for motor current signal and vibration signal of novel environment-friendly gas circuit breaker
In the embodiment of the present invention, before extracting the current characteristics of the motor current signal, the method further includes the following steps: and carrying out wavelet denoising on the motor current signal/the vibration signal. The wavelet denoising method comprises the following steps: determining a decomposition scale n according to the frequency of the motor current signal and/or the vibration signal; performing n-layer decomposition on the motor current signal/the vibration signal by adopting a preset wavelet basis function; and processing the decomposed signals through a preset threshold function.
Step 1.1: selection of wavelet basis
Since wavelet basis functions have their own characteristics during signal processing, none of them can provide the best noise suppression effect for all types of signals. The generally common subset of db and sym of signals is a two-family subgroup, commonly used to suppress speech noise. Where the db8 wavelet is selected for denoising.
Step 1.2: selection of decomposition scales
The larger the value of the decomposition scale n is, the more obvious the different characteristics of the obtained noise and signal expression are, and the more convenient the separation of the two signals is; but the larger the decomposition gradient, the greater the distortion of the reconstructed signal, and to some extent, the main factor affecting the final cleaning effect is the decomposition scale. Therefore, special attention should be paid to handle these two contradictions by selecting an appropriate decomposition scale.
In step 1.2, if the frequency of the current of the energy storage motor is about 0 to 300Hz, only four layers of decomposition are needed for the wavelet, and the frequency of the bottom layer is 0 to 312.5Hz (specifically, the bottom frequency of n layers of decomposition is basically consistent with the current frequency, the number n of decomposition layers is determined by taking the bottom frequency as the principle), and then the noise and the signal can be separated by carrying out threshold processing on coefficients of each layer.
Step 1.3: selecting threshold function and threshold
The embodiment of the invention selects a soft threshold function and a threshold value, and the mathematical expression of the threshold function is as follows
The threshold value is
And 2, step: energy storage motor current feature extraction
Step 2.1: envelope acquisition of a current signal
In the embodiment of the invention, the current characteristic of the motor current signal is extracted, the method specifically comprises the following steps: extracting an envelope line of the motor current signal; a kurtosis is calculated based on the envelope.
For the sampled stored energy motor current signal x (t), assuming y (t) as its Hilbert Transform (HT), then:
the analytic signal of the original signal can be obtained by Hilbert transform:
Z(t)＝x(t)+jy(t) (4)
resolving the norm of the signal to be the envelope a (t) of the signal:
in the current envelope curve of the energy storage motor, the starting current, the maximum current with load for stable operation, the corresponding time and the total energy storage duration reflect the key characteristics of the operation state of the motor and can be used as characteristic quantities.
Step 2.2: kurtosis acquisition
Kurtosis, which is a dimensionless parameter that is particularly sensitive to impulse signals, can be used to describe the kurtosis of the envelope of the startup or shutdown current signal, and is calculated as follows:
in the formula, x is a current envelope instantaneous value, μ is an envelope mean value, p (x) is a probability density, and σ is a standard deviation.
And 3, step 3: energy storage vibration signal feature quantity extraction
In the embodiment of the present invention, extracting the vibration characteristics of the vibration signal specifically includes the following steps: decomposing said vibration signal into the form of a sum of a plurality of product functions and a residual component; the box dimension is determined based on a frequency domain distribution law of the plurality of product functions.
Step 3.1: the signal is decomposed into the form of the sum of a plurality of Product Functions (PF) and a residual component using a Local Mean Decomposition (LMD) based EMD refinement method.
Determining all local extreme points of the original signal x (t) and calculating the average value m of all adjacent local extreme points i And an envelope estimate a i Then smoothing by adopting a sliding average method to obtain a local mean function m 11 (t) and an envelope estimation function a 11 (t) separating the local mean function from the original signal x (t) to obtain h 11 (t) and dividing by the envelope estimation function to obtain the frequency modulated signal s 11 (t) of (d). Iterative calculation of s 11 (t) an envelope estimation function n times up to s 1n (t) is a pure frequency modulated signal.
Multiplying the envelope estimation function generated in the iterative process to obtain an envelope signal a 1 (t) and the pure frequency modulation function S 1n (t) to obtain the PF of the signal x (t) 1 (t); separation of PF from the original signal x (t) 1 (t) obtaining a signal y 1 (t) adding y 1 (t) repeating the above steps k times as raw data until y k (t) is a monotonic function. Then the signal:
step 3.2: the PF component of the vibration signal processed by the LMD comprises all information of an impact event in the energy storage process, and a box dimension characterization method is introduced to depict the frequency domain distribution rule of the PF component.
The specific calculation process is as follows: the box dimension is particularly sensitive to texture roughness of image space distribution, and can quantitatively represent the distribution rule of a PF component energy spectrogram (hereinafter referred to as an energy spectrogram). Dividing the energy spectrogram with the size of M multiplied by M into a plurality of L multiplied by L grids (L is more than 1 and less than or equal to M/2, L is an integer), making r = L/M, G is the total gray level of the spectrogram, and then each grid has the height of L 1 A box of L 1 = rG. Suppose the minimum and maximum gray values of the (i, j) -th grid imageThe values are located in the kth and 1 st boxes, respectively, then the number of boxes required to cover the (i, j) th grid image is:
n r (i，j)＝k-l+1 (8)
the number of cells required to cover the entire image is then N r ：
N r ＝∑n r (i，j) (9)
The box dimension of an energy spectrum of a certain PF component can be calculated by the following equation:
where N is a point pair { log (N) r ) Log (1/r) }, i.e. N pairs of { log (N) are calculated by changing the size of the grid L r ) Log (1/r) }, then fitting log (N) by least squares r ) And the log (1/r) slope, the absolute value of which is the box dimension D.
And 4, step 4: based on entropy weight method and KNN algorithm pair energy storage system for mechanical state discrimination
In the embodiment of the invention, the mechanical state of the energy storage system of the circuit breaker is judged based on the characteristic vector, and the method specifically comprises the following steps: acquiring a preset sample set; calculating the feature vector X i With each sample Y in the sample set j The Euclidean distance of; finding out K samples with the minimum distance; determining a weight value of each of the K samples based on a weight function; the feature vector X is combined i And classifying the data into the category with the largest weight value.
Step 4.1: selecting starting current and starting time, maximum current and corresponding time for stable operation with load, total energy storage duration and kurtosis as characteristic vectors of motor current, and selecting five-order box dimension D i (i =1, \8230;, 5) as the feature vector of the vibration signal, the weight of the feature vector is also found.
Establishing a characteristic vector matrix:
wherein m is four typical states of the energy storage of the circuit breaker (namely, a normal state, voltage deviation, mechanism jamming and spring falling), and m =1,2,3,4; n refers to the dimension of the feature vector. X ij Refer to the corresponding jth feature vector in the ith state.
Calculating the proportion of the ith state in the indicator under the jth characteristic, and the entropy value and the difference coefficient of the jth characteristic:
here, the constant k =1/ln m is generally used.
g j ＝1-e j (13)
The weight is then:
step 4.2: energy storage system mechanical fault type identification based on KNN (K Nearest Neighbors, K neighbor algorithm) algorithm
And KNN is used for determining and judging the category of the test sample according to the similarity and the category label of the training sample. The principle of KNN is that when predicting a new value x When the point is determined, the point is determined according to the type of the K points which are closest to the point x To which category.
First by the weight W found j Processing the extracted feature vectors of the motor current and the vibration signal (directly converting W into W) j Multiplying by the corresponding jth feature vector) to obtain an entropy-weighted feature vector value. Taking a sample with definite energy storage system state as a training sample set Y = (Y) 1 ，Y 2 ，…，Y p ) T In which Y is j ＝(y j1 ，y j2 ，…，y jn ) (ii) a The entropy weight characteristic vector of the circuit breaker energy storage system of which the mechanical state is to be judged forms a sample set X = (X) to be classified 1 ，X 2 ，…，X p ) T 。
Then, a sample set X = (X) to be classified is calculated 1 ，X 2 ，…，X p ) T In each sample X i ＝(x i1 ，x i2 ，…，x in ) And each sample Y in the training sample set j And finding out K training samples with the minimum distance in the training sample set as each sample X to be classified i K nearest neighbors. Suppose Y 1 ，Y 2 ，…Y λ …，Y K Calculating the weight P of each class (provided with class C) in turn for K nearest neighbors:
wherein l < g < C, when X i And Y g When it is a homogeneous sample, v (X) i ，Y g ) Is 1, otherwise is 0; λ is the number of K nearest neighbors. Finally, the sample X to be divided i Divided into weight coefficients P (X) i ，Y g ) And in the largest category, finishing the classification of all samples to be classified.
By adopting the technical scheme, the invention has the following beneficial effects:
1. the novel method for distinguishing the mechanical state of the energy storage system of the environment-friendly gas circuit breaker solves the problem that the extracted characteristic quantity of the energy storage motor current and the vibration signal which are obtained by direct collection can contain noise errors, and the selected wavelet denoising algorithm has the characteristic of multi-resolution analysis, and is high in resolution and good in denoising performance.
2. The method fills the blank of the method for identifying the mechanical fault of the energy storage system of the novel environment-friendly gas circuit breaker, and integrates the current characteristic and the vibration characteristic of the energy storage motor of the novel environment-friendly gas circuit breaker to judge the mechanical state of the energy storage system. Research proves that when the states of an energy storage control electrical loop, mechanical motion and a spring change, the electrical topology and the load moment are directly or indirectly reflected in the current waveform of the motor, and a series of shock wave superposed vibration signals are generated on the mechanism, so that mechanical faults can be better judged by giving weights of different sizes according to the contained information quantity, the two are fused to more comprehensively reflect the state information of an energy storage system, the extracted characteristic quantity has complementarity, and the diagnosis effect is improved.
3. The energy storage system mechanical fault discrimination method of the novel environment-friendly gas circuit breaker based on the KNN classification algorithm is simple in calculation and effective in result, the calculation time and the calculation space depend on the scale of the training set and are not very large, and the algorithm is suitable for being used for class domain crossing or overlapping more to-be-divided sample sets and is more practical in practice.
Fig. 3 is a block diagram illustrating an apparatus for determining a mechanical state of an energy storage system of a circuit breaker according to an exemplary embodiment. Referring to fig. 3, the apparatus includes: the device comprises an acquisition module, a first extraction module, a second extraction module, a determination module and a judgment module.
And the acquisition module is used for acquiring a motor current signal and a vibration signal of the circuit breaker.
The first extraction module is used for extracting the current characteristics of the motor current signal; the current characteristics include: envelope and/or kurtosis.
The second extraction module is used for extracting the vibration characteristics of the vibration signal; the vibration characteristics include: the box dimension.
A determination module to determine a feature vector of the circuit breaker based on the current feature and the vibration feature.
And the judging module is used for judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector.
In some embodiments, extracting the discriminating means further comprises: and the denoising module is used for performing wavelet denoising on the motor current signal/the vibration signal.
The step of performing wavelet denoising by the denoising module comprises the following steps: determining a decomposition scale n according to the frequency of the motor current signal and/or the vibration signal; performing n-layer decomposition on the motor current signal/the vibration signal by adopting a preset wavelet basis function; and processing the decomposed signals through a preset threshold function.
In some embodiments, the first extraction module is specifically configured to: extracting an envelope line of the motor current signal; a kurtosis is calculated based on the envelope.
The step of extracting the envelope curve of the motor current signal by the first extraction module comprises the following steps: performing Hilbert transform on a motor current signal x (t) to obtain a transformed signal y (t); obtaining an analytic signal Z (t) of the motor current signal based on x (t) and y (t); the mode of the analytic signal Z (t) is the envelope a (t) of the signal.
The first extraction module calculates kurtosis by the following steps:
wherein, x is the instantaneous value of the current envelope, mu is the average value of the envelope, p (x) is the probability density and σ is the standard deviation.
In some embodiments, the second extraction module is specifically configured to: decomposing said vibration signal into the form of a sum of a plurality of product functions and a residual component; the box dimension is determined based on a frequency domain distribution law of the plurality of product functions.
The step of the second extraction module decomposing the vibration signal comprises: taking the vibration signal as an original signal x (t); determining all local extreme points of the original signal; obtaining pure frequency modulation signal s based on local extreme point 1n (t); based on purely frequency-modulated signals s 1n (t) obtaining a first product function PF 1 (t); PF is separated from the original signal x (t) 1 (t) obtaining a signal y 1 (t); will y 1 (t) repeating the above steps k times as the original signal until y k (t) is a monotonic function; the vibration signal is then:
the second extraction module obtains pure frequency modulation signal s based on local extreme points 1n (t) Comprises the following steps: calculating the average value m of all adjacent local extreme points i And an envelope estimate a i (ii) a Smoothing by adopting a moving average method to obtain a local mean function m 11 (t) and an envelope estimation function a 11 (t); separating m from the original signal x (t) 11 (t) obtaining h 11 (t) and dividing by the envelope estimation function to obtain a frequency modulated signal s 11 (t); iterative calculation of s 11 (t) an envelope estimation function n times up to s 1n (t) is a pure frequency modulated signal.
In some embodiments, the determination module is specifically configured to: acquiring a preset sample set; calculating the feature vector X i With each sample Y in the sample set j The Euclidean distance of; finding out K samples with the minimum distance; determining a weight value of each of the K samples based on a weight function; the feature vector X is combined i And classifying the data into the category with the largest weight value.
With regard to the apparatus in the above embodiment, the specific steps in which the respective modules perform operations have been described in detail in the embodiment related to the method, and are not described in detail herein. All or part of each module in the above determination device can be realized by software, hardware and their combination. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried out in the method of implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method for judging the mechanical state of a circuit breaker energy storage system is characterized by comprising the following steps:
acquiring a motor current signal and a vibration signal of the circuit breaker;
extracting current characteristics of the motor current signal; the current characteristics include: an envelope and/or kurtosis;
extracting vibration characteristics of the vibration signal; the vibration feature includes: box dimension;
determining a feature vector of the circuit breaker based on the current feature and the vibration feature;
and judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector.
2. The method of claim 1, wherein prior to extracting the current signature of the motor current signal, further comprising: performing wavelet denoising on the motor current signal/the vibration signal;
the wavelet denoising method comprises the following steps:
determining a decomposition scale n according to the frequency of the motor current signal and/or the vibration signal;
performing n-layer decomposition on the motor current signal/the vibration signal by adopting a preset wavelet basis function;
and processing the decomposed signals through a preset threshold function.
3. The method of claim 1 or 2, wherein extracting a current signature of the motor current signal comprises:
extracting the motor current an envelope of the signal;
a kurtosis is calculated based on the envelope.
4. The method of claim 3, wherein extracting the envelope of the motor current signal comprises:
performing Hilbert transform on a motor current signal x (t) to obtain a transformed signal y (t);
obtaining an analytic signal Z (t) of the motor current signal based on x (t) and y (t);
the mode of the analytic signal Z (t) is the envelope a (t) of the signal.
5. The method of claim 4, wherein the kurtosis is calculated by:
wherein, x is the instantaneous value of the current envelope, mu is the mean value of the envelope, p (x) is the probability density, and sigma is the standard deviation.
6. The method according to claim 1 or 2, wherein extracting vibration features of the vibration signal comprises:
decomposing the vibration signal into a plurality of products a form of a sum of the function and a residual component;
the box dimension is determined based on a frequency domain distribution law of the plurality of product functions.
7. The method of claim 6, wherein decomposing the vibration signal into a form of a sum of a plurality of product functions and a residual component comprises:
taking the vibration signal as an original signal x (t);
determining all local extreme points of the original signal;
obtaining pure frequency-modulated signal s based on local extremum points 1n (t)；
Based on purely frequency-modulated signals s 1n (t) obtaining a first product function PF 1 (t)；
PF is separated from the original signal x (t) 1 (t) obtaining a signal y 1 (t)；
Will y 1 (t) repeating the above steps k times as the original signal until y k (t) is a monotonic function;
the vibration signal is then:
8. method according to claim 7, characterized in that the pure frequency-modulated signal s is obtained on the basis of local extremum points 1n (t) comprising:
calculating the average value m of all adjacent local extreme points i And an envelope estimate a i ；
Smoothing by adopting a moving average method to obtain a local mean function m 11 (t) and an envelope estimation function a 11 (t)；
Separating m from the original signal x (t) 11 (t) obtaining h 11 (t) and dividing by the envelope estimation function to obtain the frequency modulated signal s 11 (t)；
Iterative calculation of s 11 (t) an envelope estimation function n times up to s 1n (t) is a pure frequency modulated signal.
9. The method of claim 1 or 2, wherein discriminating a mechanical state of a circuit breaker energy storage system based on the eigenvector comprises:
acquiring a preset sample set;
calculating the feature vector X i With each sample Y in the set of samples j The Euclidean distance of;
finding out K samples with the minimum distance;
determining a weight value of each of the K samples based on a weight function;
the feature vector X is combined i And classifying the data into the category with the largest weight value.
10. The utility model provides a discriminating gear of circuit breaker energy storage system mechanical state which characterized in that includes:
the acquisition module is used for acquiring a motor current signal and a vibration signal of the circuit breaker;
the first extraction module is used for extracting the current characteristics of the motor current signal; the current characteristics include: an envelope and/or kurtosis;
the second extraction module is used for extracting the vibration characteristics of the vibration signals; the vibration characteristics include: box dimension;
a determination module to determine a feature vector of the circuit breaker based on the current feature and the vibration feature;
and the judging module is used for judging the mechanical state of the circuit breaker energy storage system based on the characteristic vector.
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