CN112834959A - Direct-current power distribution system fault detection method based on high-frequency feature extraction - Google Patents
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Abstract
The invention relates to a direct current power distribution system fault detection method based on high-frequency feature extraction, which comprises the following steps of S1: constructing a fault classification model combining an automatic encoder and a support vector machine; step S2, mounting the protection device on the direct current line, and collecting the current and voltage waveform of the direct current line to obtain the positive voltage upStep S3, self-adaptively decomposing the current waveform by adopting an empirical wavelet algorithm to obtain an empirical wavelet modal componentf 0~2(ii) a Step S4, calculatingf 2The maximum amplitude of the component and comparing with a set threshold to detect a fault; step S5, according to the obtained empirical wavelet modal componentf 0~2And positive electrode voltage upAnd fusing the constructed characteristic vectors, inputting the constructed characteristic vector matrix into a fault classification model, and dividing the fault types. The invention effectively improves the fault detection efficiency and accuracy of the direct current power distribution system.
Description
Technical Field
The invention relates to the field of power distribution system fault detection, in particular to a direct-current power distribution system fault detection method based on high-frequency feature extraction.
Background
The direct-current power distribution network has a series of advantages of improving the transmission capacity of a line, facilitating the access of distributed new energy and energy storage equipment, improving the quality of electric energy, reducing the loss of the line and the like, and provides a new solution for the development of urban power distribution networks. However, the rising speed of the fault current of the direct-current power distribution network is high, the characteristic of a natural zero crossing point is not provided, the current value can be kept at a high level after the fault enters a steady state, high requirements on the aspects of quickness, sensitivity and selectivity of direct-current fault treatment are provided, and the quick and reliable fault detection is more significant.
At present, methods for detecting faults of a direct-current power distribution network can be roughly divided into two main categories: one is fault detection based on electrical quantities, which are usually current voltages on dc lines. The methods directly taking the amplitude of the electrical quantity as the fault detection criterion include overcurrent and overvoltage detection and current reverse zero crossing point detection, the methods are simple and easy to implement, but the identification speed is difficult to meet the requirement of quick action, and the method is greatly influenced by fault transition resistance, is easy to generate false detection and missing detection, and generally needs to be matched with other detection means for use. More commonly, some processing means is applied to the electrical quantity, for example, fourier transform, wavelet transform, empirical mode decomposition, etc. are used as signal processing methods, and intelligent processing methods include automatic encoders, neural networks, random forests, etc., and mathematical differential processing. The signal processing method can accurately capture singular points of the electrical quantity, extract fault characteristics, and is high in reliability and less affected by fault transition resistance. The intelligent processing method has the advantages that the advantages are gradually revealed, the fault characteristics do not need to be manually selected, the reliability is good, and the fault detection model of the corresponding power distribution network needs to be trained in advance. The method of electrical quantity differentiation processing increases the speed with respect to overcurrent overvoltage detection, but still fails to overcome the disadvantage of being greatly affected by the fault transition resistance. Secondly, communication-based fault detection needs to be carried out, communication equipment is arranged at two ends of a direct-current line, an electric quantity analog signal is converted into a digital signal, and a fault is detected through comparison of difference values of the digital signals at the two ends, so that the fault identification speed is high due to the fact that analog-to-digital conversion is fast, but the problem of communication synchronism exists.
Disclosure of Invention
In view of this, the present invention provides a method for detecting a fault of a dc power distribution system based on high-frequency feature extraction, so as to effectively improve detection efficiency and accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme:
a direct current power distribution system fault detection method based on high-frequency feature extraction comprises the following steps
Step S1: constructing a fault classification model combining an automatic encoder and a support vector machine;
step S2, mounting the protection device on the direct current line, and collecting the current and voltage waveform of the direct current line to obtain the positive voltage up；
Step S3, self-adaptively decomposing the current waveform by adopting an empirical wavelet algorithm to obtain an empirical wavelet modal component f0～2；
Step S4 calculating f2The maximum amplitude of the component and comparing with a set threshold to detect a fault;
step S5, according to the obtained empirical wavelet modal component f0～2And positive electrode voltage upAnd fusing the constructed characteristic vectors, inputting the constructed characteristic vector matrix into a fault classification model, and dividing the fault types.
Further, the step S1 is specifically:
s11: acquiring various fault data and constructing a training set database;
s12: according to the parameters of the automatic encoder trained by the training set, obtaining a fault characteristic h after dimensionality reduction;
s13: training a support vector machine classifier by using the characteristic quantity h obtained in the step S12;
s14: and (4) saving parameters of the automatic encoder and the support vector machine, and establishing a direct-current line fault classification model.
Further, the step S12 is specifically:
a: encoding a signal according to equation (1);
h＝f(Wx+b) (1)
wherein W is weight, b is offset, and x is f0～2、upA signal;
b: decoding the fault feature h extracted in the step a according to a formula (2);
wherein W' is weight and d is offset;
c: calculating the signal x and the reconstructed signal according to equation (3)
A loss function of (d);
d: and c, judging whether the error calculated in the step c is in accordance with the expectation, if so, saving the weight and the bias parameter of the automatic encoder, and if not, updating the weight and the bias parameter and returning to the step a.
Further, the step S3 is specifically:
s31: dividing a Fourier amplitude spectrum by adopting self-adaption division to obtain N areas;
s32: boundary points ω according to the divided N regionsnConstructing an empirical wavelet function and an empirical scale function;
s33: calculating an empirical wavelet detail coefficient and an approximate coefficient according to the obtained empirical wavelet function and the empirical scale function;
s34: calculating to obtain an empirical wavelet modal component f according to the calculated empirical wavelet detail coefficient and approximation coefficient0～2。
Further, the step S31 is specifically:
a: the collected current waveform i is processed according to the formula (4)cPerforming discrete Fourier transform, and obtaining amplitude frequency spectrum by using formula (5)
Where x (M) is the sampling signal, M is the number of data
Where Re (k) denotes the real part and im (k) denotes the imaginary part;
b: finding local maxima in the frequency range, the middle point of the two maxima being set as the boundary omeganFinding out N-1 maximum value points and dividing N regions Lambdan。
Further, the empirical wavelet function and the empirical scale function are respectively:
wherein β (x) is x4(35-84x+70x2-20x3)，
Further, the step S33 is specifically: calculating empirical wavelet detail coefficients and approximation coefficients according to equation (8) and equation (9):
wherein, F-1Representing an inverse FourierThe result of the transformation is a transformation,
is the fourier transform of the signal f (τ).
Further, the step S34 is specifically: calculating empirical wavelet modal component f according to formula (10)n
Further, the step S4 is specifically:
step S41, adopting Hilbert transform to identify f2And the maximum value f of the amplitude of the component is obtained2_Amp；
S42: setting a threshold value delta when f is satisfied2_Amp>Delta, the fault is determined to occur.
Further, the step S41 is specifically:
calculating a Hilbert transform of the signal according to formula (11);
calculating an analytic signal according to formula (12);
calculating the instantaneous amplitude of the signal according to equation (13) and obtaining the maximum amplitude value f2_Amp
Compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts the empirical wavelet algorithm to extract the high-frequency component of the current in a self-adaptive manner, thereby effectively reducing the detection time consumption and improving the detection efficiency.
2. The invention has stronger fault transition resistance tolerance capability, further extracts the depth characteristic of the fault by using the automatic encoder, avoids the complexity of manual selection and fault characteristic calculation, and can automatically extract the characteristic.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of an embodiment of a dc distribution network fault line detection.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a method for detecting a fault of a dc power distribution system based on high frequency feature extraction, which includes the following steps
Step S1: constructing a fault classification model combining an automatic encoder and a support vector machine;
s11: acquiring various fault data and constructing a training set database;
s12: according to the parameters of the automatic encoder trained by the training set, obtaining a fault characteristic h after dimensionality reduction;
a: encoding a signal according to equation (1);
h＝f(Wx+b) (1)
wherein W is weight, b is offset, and x is f0～2、upA signal;
b: decoding the fault feature h extracted in the step a according to a formula (2);
wherein W' is weight and d is offset;
c: calculating the signal x and the reconstructed signal according to equation (3)
A loss function of (d);
d: and c, judging whether the error calculated in the step c is in accordance with the expectation, if so, saving the weight and the bias parameter of the automatic encoder, and if not, updating the weight and the bias parameter and returning to the step a.
S13: training a support vector machine classifier by using the characteristic quantity h obtained in the step S12;
s14: and (4) saving parameters of the automatic encoder and the support vector machine, and establishing a direct-current line fault classification model.
Step S2, mounting the protection device on the direct current line, and collecting the current and voltage waveform of the direct current line to obtain the positive voltage up；
Step S3, self-adaptively decomposing the current waveform by adopting an empirical wavelet algorithm to obtain an empirical wavelet modal component f0～2；
S31: dividing a Fourier amplitude spectrum by adopting self-adaption division to obtain N areas;
a: the collected current waveform i is processed according to the formula (4)cPerforming discrete Fourier transform, and obtaining amplitude frequency spectrum by using formula (5)
Where x (M) is the sampling signal, M is the number of data
Where Re (k) denotes the real part and im (k) denotes the imaginary part;
b: finding local maxima in the frequency range, the middle point of the two maxima being set as the boundary omeganFinding out N-1 maximum value points and dividing N regions Lambdan。
S32: boundary points ω according to the divided N regionsnConstructing an empirical wavelet function and an empirical scale function;
wherein β (x) is x4(35-84x+70x2-20x3)，
S33: calculating empirical wavelet detail coefficients and approximation coefficients according to equation (8) and equation (9):
wherein, F-1Which represents the inverse fourier transform of the signal,
is the fourier transform of the signal f (τ).
S34: calculating empirical wavelet modal component f according to formula (10)n
Step S4 calculating f2The maximum amplitude of the component and comparing with a set threshold to detect a fault;
step S41, adopting Hilbert transform to identify f2And the maximum value f of the amplitude of the component is obtained2_Amp；
Calculating a Hilbert transform of the signal according to formula (11);
calculating an analytic signal according to formula (12);
calculating the instantaneous amplitude of the signal according to equation (13) and obtaining the maximum amplitude value f2_Amp
S42: setting a threshold value delta when f is satisfied2_Amp>Delta, the fault is determined to occur.
Step S5, according to the obtained empirical wavelet modal component fnAnd positive electrode voltage upAnd fusing the constructed characteristic vectors, inputting the constructed characteristic vector matrix into a fault classification model, and dividing the fault types.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (10)
1. A direct current power distribution system fault detection method based on high-frequency feature extraction is characterized by comprising the following steps
Step S1: constructing a fault classification model combining an automatic encoder and a support vector machine;
step S2, mounting the protection device on the direct current line, and collecting the current and voltage waveform of the direct current line to obtain the positive voltage up；
Step S3, self-adaptively decomposing the current waveform by adopting an empirical wavelet algorithm to obtain an empirical wavelet modal component f0～2；
Step S4 calculating f2The maximum amplitude of the component and comparing it with a set threshold to detect a faultA barrier;
step S5, according to the obtained empirical wavelet modal component f0～2And positive electrode voltage upAnd fusing the constructed characteristic vectors, inputting the constructed characteristic vector matrix into a fault classification model, and dividing the fault types.
2. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 1, wherein the step S1 specifically includes:
s11: acquiring various fault data and constructing a training set database;
s12: according to the parameters of the automatic encoder trained by the training set, obtaining a fault characteristic h after dimensionality reduction;
s13: training a support vector machine classifier by using the characteristic quantity h obtained in the step S12;
s14: and (4) saving parameters of the automatic encoder and the support vector machine, and establishing a direct-current line fault classification model.
3. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 2, wherein the step S12 specifically includes:
a: encoding a signal according to equation (1);
h＝f(Wx+b) (1)
wherein W is weight, b is offset, and x is f0～2、upA signal;
b: decoding the fault feature h extracted in the step a according to a formula (2);
wherein W' is weight and d is offset;
c: calculating the signal x and the reconstructed signal according to equation (3)
A loss function of (d);
d: and c, judging whether the error calculated in the step c is in accordance with the expectation, if so, saving the weight and the bias parameter of the automatic encoder, and if not, updating the weight and the bias parameter and returning to the step a.
4. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 1, wherein the step S3 specifically includes:
s31: dividing a Fourier amplitude spectrum by adopting self-adaption division to obtain N areas;
s32: boundary points ω according to the divided N regionsnConstructing an empirical wavelet function and an empirical scale function;
s33: calculating an empirical wavelet detail coefficient and an approximate coefficient according to the obtained empirical wavelet function and the empirical scale function;
s34: calculating to obtain an empirical wavelet modal component f according to the calculated empirical wavelet detail coefficient and approximation coefficient0～2。
5. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 4, wherein the step S31 specifically comprises:
a: the collected current waveform i is processed according to the formula (4)cPerforming discrete Fourier transform, and obtaining amplitude frequency spectrum by using formula (5)
Where x (M) is the sampling signal, M is the number of data
Where Re (k) denotes the real part and im (k) denotes the imaginary part;
b: finding local maxima in the frequency range, the middle point of the two maxima being set as the boundary omeganFinding out N-1 maximum value points and dividing N regions Lambdan。
6. The method for detecting the fault of the direct current power distribution system based on the high-frequency feature extraction as claimed in claim 5, wherein the empirical wavelet function and the empirical scale function are respectively as follows:
wherein β (x) is x4(35-84x+70x2-20x3)，
7. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 6, wherein the step S33 is specifically as follows: calculating empirical wavelet detail coefficients and approximation coefficients according to equation (8) and equation (9):
wherein, F-1Which represents the inverse fourier transform of the signal,
is the fourier transform of the signal f (τ).
8. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 6, wherein the step S34 is specifically as follows: calculating empirical wavelet modal component f according to formula (10)n
9. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 1, wherein the step S4 specifically includes:
step S41, adopting Hilbert transform to identify f2And the maximum value f of the amplitude of the component is obtained2_Amp；
S42: setting a threshold value delta when f is satisfied2_Amp>Delta, the fault is determined to occur.
10. The method for detecting faults of a direct current power distribution system based on high-frequency feature extraction as claimed in claim 1, wherein the step S41 specifically includes:
calculating a Hilbert transform of the signal according to formula (11);
calculating an analytic signal according to formula (12);
calculating the instantaneous amplitude of the signal according to equation (13) and obtaining the maximum amplitude value f2_Amp
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