Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm
The reliability of power electronics is very important to the safety and efficiency operation of the multilevel inverters, and it is very necessary to quickly detect and locate the faults. To solve the difficulty in locating open-circuit faults and improve the diagnosis accuracy for the H-bridge cas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723002548 |
| _version_ | 1797821556865368064 |
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| author | Qingqing Yuan Qian Tu Lei Yan Kun Xia |
| author_facet | Qingqing Yuan Qian Tu Lei Yan Kun Xia |
| author_sort | Qingqing Yuan |
| collection | DOAJ |
| description | The reliability of power electronics is very important to the safety and efficiency operation of the multilevel inverters, and it is very necessary to quickly detect and locate the faults. To solve the difficulty in locating open-circuit faults and improve the diagnosis accuracy for the H-bridge cascaded five-level inverters, an improved support vector machine (SVM) with gray wolf (GWO) optimization method has been elaborated and proposed in this paper especially for single switch faults and double switches faults in one phase. Firstly, the output voltage signals are selected as the sampling data, and whose eigenvectors have been taken as the input signals of the wavelet packet energy entropy method, and the dimensionality of these extracted eigenvectors is furtherly reduced with a probabilistic principle component analysis (PPCA) method to reduce the calculations and improve the diagnosis efficiency. Then, the SVM method is used for the fault classification, during which the GWO optimization has been introduced to find the optimal value of SVM, so as to improve the diagnostic efficiency. Experimental results show that this diagnosis method can not only achieve higher diagnostic accuracy, smaller identification error, and has greater efficiency compared with ordinary SVM or some other traditional diagnosis methods and other intelligent optimization algorithms, which promotes the fault diagnosis research for these multilevel inverters. |
| first_indexed | 2024-03-13T09:54:29Z |
| format | Article |
| id | doaj.art-4d497ddb7c3c46b5bd79aa80f0617899 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T09:54:29Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-4d497ddb7c3c46b5bd79aa80f06178992023-05-24T04:20:36ZengElsevierEnergy Reports2352-48472023-04-019485495Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithmQingqing Yuan0Qian Tu1Lei Yan2Kun Xia3Corresponding author.; Department of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Electrical Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaThe reliability of power electronics is very important to the safety and efficiency operation of the multilevel inverters, and it is very necessary to quickly detect and locate the faults. To solve the difficulty in locating open-circuit faults and improve the diagnosis accuracy for the H-bridge cascaded five-level inverters, an improved support vector machine (SVM) with gray wolf (GWO) optimization method has been elaborated and proposed in this paper especially for single switch faults and double switches faults in one phase. Firstly, the output voltage signals are selected as the sampling data, and whose eigenvectors have been taken as the input signals of the wavelet packet energy entropy method, and the dimensionality of these extracted eigenvectors is furtherly reduced with a probabilistic principle component analysis (PPCA) method to reduce the calculations and improve the diagnosis efficiency. Then, the SVM method is used for the fault classification, during which the GWO optimization has been introduced to find the optimal value of SVM, so as to improve the diagnostic efficiency. Experimental results show that this diagnosis method can not only achieve higher diagnostic accuracy, smaller identification error, and has greater efficiency compared with ordinary SVM or some other traditional diagnosis methods and other intelligent optimization algorithms, which promotes the fault diagnosis research for these multilevel inverters.http://www.sciencedirect.com/science/article/pii/S2352484723002548H-bridge cascaded five-level inverterFault diagnosisSupport vector machine (SVM)Gray Wolf (GWO) optimization |
| spellingShingle | Qingqing Yuan Qian Tu Lei Yan Kun Xia Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm Energy Reports H-bridge cascaded five-level inverter Fault diagnosis Support vector machine (SVM) Gray Wolf (GWO) optimization |
| title | Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm |
| title_full | Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm |
| title_fullStr | Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm |
| title_full_unstemmed | Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm |
| title_short | Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm |
| title_sort | fault diagnosis of h bridge cascaded five level inverter based on improved support vector machine with gray wolf algorithm |
| topic | H-bridge cascaded five-level inverter Fault diagnosis Support vector machine (SVM) Gray Wolf (GWO) optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723002548 |
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