CN111239521B - Wind power generation system converter fault identification method based on PCA-kNN - Google Patents

Wind power generation system converter fault identification method based on PCA-kNN Download PDF

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CN111239521B
CN111239521B CN202010099166.9A CN202010099166A CN111239521B CN 111239521 B CN111239521 B CN 111239521B CN 202010099166 A CN202010099166 A CN 202010099166A CN 111239521 B CN111239521 B CN 111239521B
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卢军锋
姜劲
邹政耀
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Abstract

The invention relates to a fault identification method for a converter of a wind power generation system based on PCA-kNN.

Description

Wind power generation system converter fault identification method based on PCA-kNN
Technical Field
The invention relates to the field of converter fault identification, in particular to a method for identifying converter faults based on a PCA-kNN wind power generation system.
Background
Wind power is an important energy source supplied by the world as a renewable energy source. With the continuous development of economy and science and technology, wind power generation is greatly developed in China, however, the working state of a fan cannot be stable due to the randomness of wind, so that all components of a wind power generation system frequently break down, a double Pulse Width Modulation (PWM) converter is used as an interface of the power generation system and a power grid, namely, the voltage is controlled to be stable, the maximum wind energy utilization rate is realized in a matching mode, and meanwhile, the converter is also one of the components which are most prone to break down.
The IGBT module is an important component of the converter, and due to the reasons of drive failure, switch damage and the like of the wind power generation system, the converter fault of the wind power generation system mainly considers the fault of an IGBT device, and the short-circuit fault of the IGBT can be monitored by the drive module integrated protection circuit at present, however, the open-circuit fault identification technology of the IGBT is still under study.
In 1968, Cover and Hart proposed a K-nearest neighbor (kNN) classification algorithm, which is one of the simplest classification algorithms in a data statistical model. The kNN algorithm idea is to calculate the distance between the measured sample and the known sample, and then determine the category of the measured sample according to the nearest k known samples. Because the kNN algorithm needs to calculate the distances between the detected samples and all the samples, when the characteristics extracted by the samples are more, the calculation amount of the kNN algorithm is more, and the calculation time is longer. Principal Component Analysis (PCA) is a common data dimension reduction and feature extraction method and is widely applied to the field of pattern recognition such as face recognition, so that the calculation amount of the algorithm can be reduced and the operation speed and recognition accuracy of the algorithm can be improved by combining the PCA algorithm with the kNN algorithm.
Disclosure of Invention
To solve the above existing problems. The invention provides a fault identification method for a converter of a wind power generation system based on PCA-kNN, which solves the problem of fault identification of the converter in the wind power generation system. To achieve this object:
the invention provides a PCA-kNN-based wind power generation system converter fault identification method, which comprises the following specific steps of:
step 1: detecting direct-current side output voltage signals of a back-to-back type three-phase PWM rectifier in the wind power generation system under various open-circuit faults;
step 2: extracting time domain, frequency domain and time-frequency domain characteristics of the output voltage, and establishing a sample set by the extracted characteristics;
and step 3: carrying out PCA (principal component analysis) processing on the characteristics of the sample set to obtain a characteristic vector of the output voltage signal after dimensionality reduction; arranging the reduced features according to the importance sequence from large to small, and selecting n-dimensional features retaining 99% of main information;
and 4, step 4: dividing a sample set into training samples and testing samples, taking n-dimensional features obtained after PCA calculation as input, calculating the distance from the testing samples to the training samples by using a kNN algorithm, and selecting the optimal k value in the kNN algorithm through cross validation;
and 5: detecting a direct current side output voltage signal of the rectifier to be detected, repeating the step 2 and the step 3, and finally classifying the rectifier faults by using a kNN classification algorithm.
As a further improvement of the present invention, the open-circuit faults of the rectifier in step 1 are as follows:
the open-circuit fault of the IGBT element of the PWM rectifier comprises the following steps: the single IGBT element open-circuit fault, 2 IGBT element open-circuit faults on the upper part and the lower part of the same bridge arm, 2 IGBT element open-circuit faults in the same half bridge and 2 IGBT element open-circuit faults on the upper part and the lower part of different bridge arms.
As a further improvement of the present invention, the time domain, frequency domain and time-frequency domain characteristics of the output voltage in step 2 are as follows:
the extracted time domain, frequency domain and time-frequency domain features include: area, energy, power spectrum estimation, wavelet coefficient sum of squares;
output voltage area: integration of voltage signal over time
Figure BDA0002386296670000021
Where u (n) is a voltage signal;
output voltage energy: sum of squares of voltage signal amplitudes
Figure BDA0002386296670000022
Output voltage power spectrum estimation:
Figure BDA0002386296670000031
Figure BDA0002386296670000032
Figure BDA0002386296670000033
where e (n) is white noise with a mean of 0 and a variance of σ2P is the order of the AR model, H (z) is the transfer function of the AR model system;
sum of squares of wavelet coefficients of output voltage: performing wavelet decomposition and reconstruction on the output voltage signal, wherein the decomposition layer number is 4, and db6 wavelet is selected
Figure BDA0002386296670000034
Figure BDA0002386296670000035
Wherein a is1、a2、a3、a4Is a low frequency approximate component of the signal, d1、d2、d3、d4Is the high frequency detail component of the signal.
As a further improvement of the present invention, in the step 3, PCA processing is performed on the characteristics of the sample set as follows:
for a given sample time, frequency and time-frequency domain feature set D ═ x1,x2,...,xmLet its projection coordinate in the low-dimensional coordinate system be Zi＝(Zi1,Zi2,...,Zid) And has:
Figure BDA0002386296670000036
wherein ω isjIs a set of orthonormal vector bases, which can be based on Z according to equation 9iTo obtain xiOf the reconstructed sample
Figure BDA0002386296670000037
Figure BDA0002386296670000038
Original sample xiAnd reconstructed samples obtained based on projection
Figure BDA0002386296670000039
The distance between can be expressed as:
Figure BDA0002386296670000041
where const is a constant, and W ═ ω1,ω2,...,ωd) According to the recent reconstructability principle, the minimization operation is performed on equation 10, since ω isjIs a basis of an orthonormal vector and,
Figure BDA0002386296670000042
is a covariance matrix, the optimization objective of principal component analysis of equation 11 can be obtained:
Figure BDA0002386296670000043
wherein I represents an identity matrix, and formula 11 is transformed using lagrange multipliers to obtain formula 12:
XXTωi＝λiωi (12)
final pair covariance matrix XXTDecomposing the eigenvalues, sorting the obtained eigenvalues in the order from small to large, and finally taking the first n eigenvectors corresponding to the maximum eigenvalue containing 99% of information to form a new eigenvector matrix Wopt＝(ω1,ω2,…,ωn) Is the optimal solution of principal component analysis.
As a further improvement of the present invention, the distance from the test sample to the training sample is calculated by the kNN algorithm in step 4 as follows:
Figure BDA0002386296670000044
wherein xiIs a test characteristic sample, x'iAre training set feature samples.
As a further improvement of the present invention, in the step 4, the optimal k value in the kNN algorithm is selected as follows:
finding and testing sample x in training sample setiThe nearest k points, the field covering the k training sample points is denoted as Nk(x) Determining the class of the test sample according to the classification decision rule
Figure BDA0002386296670000045
Wherein, yi∈{c1,c2,...,ckIs a training sample x'iClass of instance, I is an indicator function when yi＝ciIf so, I is 1, otherwise, I is 0; and finally, performing cross validation, and determining the k value according to the identification rate of the test sample.
The invention discloses a PCA-kNN wind power generation system-based converter fault identification method, which has the beneficial effects that:
1. the method extracts the characteristics of a time domain, a frequency domain and a time-frequency domain of a direct-current side output voltage signal, wherein the characteristics comprise more converter fault information;
2. the invention uses the PCA algorithm to reduce the dimension of the voltage signal characteristics, reduces the redundancy among the characteristics, improves the calculation speed of the kNN algorithm and reduces the classification time;
3. according to the method, the kNN algorithm is applied to converter fault identification, so that the accuracy and efficiency of converter fault identification are improved;
4. the invention provides an important technical means for identifying the fault of the converter of the wind power generation system.
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FIG. 1 is a flow chart of the overall algorithm principle;
FIG. 2 is a kNN classification schematic;
fig. 3 is a diagram of the converter fault identification result.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a PCA-kNN-based wind power generation system converter fault identification method, which extracts the characteristics of time domain, frequency domain and time-frequency domain of direct-current voltage at the direct-current side of a wind power generation system converter, performs dimension reduction processing on the characteristics by using the dimension reduction capability of the PCA, and finally classifies samples to be detected by using a kNN algorithm. The overall algorithm principle flow of the invention is shown in fig. 1, and the specific steps are as follows:
step 1: detecting direct-current side output voltage signals of a back-to-back type three-phase PWM rectifier in the wind power generation system under various open-circuit faults;
the open-circuit faults of the rectifier in the step 1 are specifically described as follows:
the open-circuit fault of the IGBT element of the PWM rectifier comprises the following steps: the single IGBT element open-circuit fault, 2 IGBT element open-circuit faults on the upper part and the lower part of the same bridge arm, 2 IGBT element open-circuit faults in the same half bridge and 2 IGBT element open-circuit faults on the upper part and the lower part of different bridge arms.
Step 2: extracting time domain, frequency domain and time-frequency domain characteristics of the output voltage, and establishing a sample set by the extracted characteristics;
the time domain, frequency domain and time-frequency domain characteristics of the output voltage in step 2 are described as follows:
the extracted time domain, frequency domain and time-frequency domain features include: area, energy, power spectrum estimation, wavelet coefficient sum of squares;
output voltage area: integration of voltage signal over time
Figure BDA0002386296670000061
Wherein u (n) is a voltage signal;
output voltage energy: sum of squares of voltage signal amplitudes
Figure BDA0002386296670000062
Output voltage power spectrum estimation:
Figure BDA0002386296670000063
Figure BDA0002386296670000064
Figure BDA0002386296670000065
where e (n) is white noise with a mean of 0 and a variance of σ2P is the order of the AR model, H (z) is the transfer function of the AR model system;
sum of squares of wavelet coefficients of output voltage: performing wavelet decomposition and reconstruction on the output voltage signal, wherein the decomposition layer number is 4, and db6 wavelet is selected
Figure BDA0002386296670000066
Figure BDA0002386296670000067
Wherein a is1、a2、a3、a4Is a low frequency approximate component of the signal, d1、d2、d3、d4Is the high frequency detail component of the signal.
And step 3: carrying out PCA (principal component analysis) processing on the characteristics of the sample set to obtain a characteristic vector of the output voltage signal after dimensionality reduction; arranging the reduced features according to the importance sequence from large to small, and selecting n-dimensional features retaining 99% of main information;
the PCA treatment of the features of the sample set in step 3 is described in detail as follows:
for a given sample time, frequency and time-frequency domain feature set D ═ x1,x2,...,xmLet its projection coordinate in the low-dimensional coordinate system be Zi＝(Zi1,Zi2,...,Zid) And has:
Figure BDA0002386296670000071
wherein ω isjIs a set of orthonormal vector bases, which can be based on Z according to equation 9iTo obtain xiOf the reconstructed sample
Figure BDA0002386296670000072
Figure BDA0002386296670000073
Original sample xiAnd reconstructed samples obtained based on projection
Figure BDA0002386296670000074
The distance between can be expressed as:
Figure BDA0002386296670000075
where const is a constant, and W ═ ω1,ω2,…,ωd) According to the recent reconstructability principle, the minimization operation is performed on equation 10, since ω isjIs a basis of an orthonormal vector and,
Figure BDA0002386296670000076
is a covariance matrix, the optimization objective of principal component analysis of equation 11 can be obtained:
Figure BDA0002386296670000077
wherein I represents an identity matrix, and formula 11 is transformed using lagrange multipliers to obtain formula 12:
XXTωi＝λiωi (12)
final pair covariance matrix XXTDecomposing the eigenvalues, sorting the obtained eigenvalues in the order from small to large, and finally taking the first n eigenvectors corresponding to the maximum eigenvalues containing 99% of information to form a new eigenvector matrix Wopt＝(ω1,ω2,…,ωn) Is the optimal solution of principal component analysis.
And 4, step 4: dividing a sample set into training samples and testing samples, taking n-dimensional features obtained after PCA calculation as input, calculating the distance from the testing samples to the training samples by using a kNN algorithm, and selecting the optimal k value in the kNN algorithm through cross validation;
the calculation of the distance from the test sample to the training sample by the kNN algorithm in step 4 is specifically described as follows:
Figure BDA0002386296670000081
wherein xiIs a test characteristic sample, x'iAre training set feature samples.
The selection of the optimal k value in the kNN algorithm in step 4 is specifically described as follows:
finding and testing sample x in training sample setiThe nearest k points, the field covering the k training sample points is denoted as Nk(x) As shown in FIG. 2, the classification of the test sample is determined according to the classification decision rule
Figure BDA0002386296670000082
Wherein, yi∈{c1,c2,…,ckIs a training sample x'iClass of instance, I is an indicator function when yi＝ciIf so, I is 1, otherwise, I is 0; and finally, performing cross validation, and determining the k value according to the identification rate of the test sample.
And 5: detecting a direct current side output voltage signal of the rectifier to be detected, repeating the step 2 and the step 3, and finally classifying the rectifier faults by using a kNN classification algorithm, wherein the classification result is shown in fig. 3.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (2)

1. The method for identifying the converter fault of the wind power generation system based on PCA-kNN comprises the following steps,
step 1: detecting direct-current side output voltage signals of a back-to-back type three-phase PWM rectifier in the wind power generation system under various open-circuit faults;
the various open-circuit faults of the rectifier in the step 1 are as follows:
the open-circuit fault of the IGBT element of the PWM rectifier comprises the following steps: the single IGBT element open-circuit fault, 2 IGBT element open-circuit faults on the upper part and the lower part of the same bridge arm, 2 IGBT element open-circuit faults in the same half bridge, and 2 IGBT element open-circuit faults on the upper part and the lower part of different bridge arms;
step 2: extracting time domain, frequency domain and time-frequency domain characteristics of the output voltage, and establishing a sample set by the extracted characteristics;
the time domain, frequency domain and time-frequency domain characteristics of the output voltage in step 2 are as follows:
the extracted time domain, frequency domain and time-frequency domain features include: area, energy, power spectrum estimation, wavelet coefficient sum of squares;
output voltage area: integration of voltage signal over time
Figure FDA0003382660540000011
Where u (n) is a voltage signal;
output voltage energy: sum of squares of voltage signal amplitudes
Figure FDA0003382660540000012
Output voltage power spectrum estimation:
Figure FDA0003382660540000013
Figure FDA0003382660540000014
Figure FDA0003382660540000015
where e (n) is white noise with a mean of 0 and a variance of σ2P is the order of the AR model, H (z) is the transfer function of the AR model system;
sum of squares of wavelet coefficients of output voltage: performing wavelet decomposition and reconstruction on the output voltage signal, wherein the decomposition layer number is 4, and db6 wavelet is selected
Figure FDA0003382660540000021
Figure FDA0003382660540000022
Wherein a is1、a2、a3、a4Is a low frequency approximate component of the signal, d1、d2、d3、d4Is the high frequency detail component of the signal;
and step 3: carrying out PCA (principal component analysis) processing on the characteristics of the sample set to obtain a characteristic vector of the output voltage signal after dimensionality reduction; arranging the reduced features according to the importance sequence from large to small, and selecting n-dimensional features retaining 99% of main information;
the PCA treatment of the sample set features in step 3 is as follows:
for a given sample time, frequency and time-frequency domain feature set D ═ x1,x2,…,xmLet its projection coordinate in the low-dimensional coordinate system be Zi＝(Zi1,Zi2,...,Zid) And has:
Figure FDA0003382660540000023
wherein ω isjIs a set of orthonormal vector bases, which can be based on Z according to equation 9iTo obtain xiOf the reconstructed sample
Figure FDA0003382660540000024
Figure FDA0003382660540000025
Original sample xiAnd reconstructed samples obtained based on projection
Figure FDA0003382660540000026
The distance between can be expressed as:
Figure FDA0003382660540000027
where const is a constant, and W ═ ω1,ω2,...,ωd) According to the recent reconstructability principle, the minimization operation is performed on equation 10, since ω isjIs a basis of an orthonormal vector and,
Figure FDA0003382660540000028
is a covariance matrix, the optimization objective of principal component analysis of equation 11 can be obtained:
Figure FDA0003382660540000031
wherein I represents an identity matrix, and formula 11 is transformed using lagrange multipliers to obtain formula 12:
XXTωi＝λiωi (12)
final pair covariance matrix XXTDecomposing the eigenvalues, sorting the obtained eigenvalues in the order from small to large, and finally taking the first n eigenvectors corresponding to the maximum eigenvalue containing 99% of information to form a new eigenvector matrix Wopt＝(ω1,ω2,...,ωn) The optimal solution of the principal component analysis is obtained;
and 4, step 4: dividing a sample set into training samples and testing samples, taking n-dimensional features obtained after PCA calculation as input, calculating the distance from the testing samples to the training samples by using a kNN algorithm, and selecting the optimal k value in the kNN algorithm through cross validation;
in step 4, the kNN algorithm calculates the distances from the test samples to the training samples as follows:
Figure FDA0003382660540000032
wherein xiIs a test characteristic sample, x'iAre training set feature samples.
2. The PCA-kNN-based wind power generation system converter fault identification method of claim 1, wherein; the optimal k value in the kNN algorithm selected in step 4 is as follows:
finding and testing sample x in training sample setiThe nearest k points, the field covering the k training sample points is denoted as Nk(x) Determining the class of the test sample according to the classification decision rule
Figure FDA0003382660540000033
Wherein, yi∈{c1,c2,...,ckIs a training sample x'iClass of instance, I is an indicator function when yi＝ciIf so, I is 1, otherwise, I is 0; finally, cross validation is carried out, and a k value is determined according to the identification rate of the test sample;
and 5: detecting a direct current side output voltage signal of the rectifier to be detected, repeating the step 2 and the step 3, and finally classifying the rectifier faults by using a kNN classification algorithm.
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Citations (5)

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Publication number Priority date Publication date Assignee Title
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CN105136454A (en) * 2015-10-15 2015-12-09 上海电机学院 Wind turbine gear box fault recognition method
CN106646096A (en) * 2016-11-15 2017-05-10 国网四川省电力公司广安供电公司 Transformer fault classification and identification method based on vibration analysis method
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