Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks
Modular multilevel converter (MMC), which is highly modular, easy to be expanded, and has good output voltage waveform, is especially suitable for medium and high voltage power system applications. The MMC bridge arm inductance fault and the independent sub-module fault have the greatest influence o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722013877 |
| _version_ | 1797947069749526528 |
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| author | Bo Xu Cui Li Huipeng Li Ruchun Ding |
| author_facet | Bo Xu Cui Li Huipeng Li Ruchun Ding |
| author_sort | Bo Xu |
| collection | DOAJ |
| description | Modular multilevel converter (MMC), which is highly modular, easy to be expanded, and has good output voltage waveform, is especially suitable for medium and high voltage power system applications. The MMC bridge arm inductance fault and the independent sub-module fault have the greatest influence on the transmission quality of transmission systems. However, there is few research on fault diagnosis and location for multiple sub-modules(SM) in a single arm at present. A deep bidirectional Long Short Term Memory (Bid-LSTM) network is proposed in this paper, which is used to diagnose and locate the faults of different sub-modules. which can deal well with the prediction problem with sequence dependence. In addition, because the Bid-LSTM model contains many hyperparameters, which seriously affects the prediction performance of BiD-LSTM, an optimal fruit fly optimization (FOA) algorithm based on the composite chaotic mapping of 3-dimensional Logistic-Sine (3d-CCFOA), which searches the optimal value of related hyperparameters to ensure the accuracy of classification. 3d-CCFOA has good chaos characteristics, which enables FOA to avoid falling into local optimal solution effectively and obtains an effective global optimal solution. Then, the MMC simulation system of RT-TAB is used to simulate the open circuit faults of different independent sub-modules of one bridge arm and collect relevant fault data. The analysis process and experimental results adequately indicate that this method can effectively and accurately diagnose and locate the faults of independent sub-modules. Meanwhile, this method also provides a reference diagnosis strategy for multi-bridge arm fault diagnosis and multi-sub-module location. |
| first_indexed | 2024-04-10T21:20:56Z |
| format | Article |
| id | doaj.art-9ff29e3fc8454cb6ae4a10d474861134 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T21:20:56Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-9ff29e3fc8454cb6ae4a10d4748611342023-01-20T04:25:42ZengElsevierEnergy Reports2352-48472022-11-01811931206Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networksBo Xu0Cui Li1Huipeng Li2Ruchun Ding3Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China; School of Electronic Information, Huang gang Normal University, Huanggang 438000, ChinaSchool of Electronic Information, Huang gang Normal University, Huanggang 438000, China; Corresponding author.Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China; School of Electronic Information, Huang gang Normal University, Huanggang 438000, ChinaSchool of Electronic Information, Huang gang Normal University, Huanggang 438000, ChinaModular multilevel converter (MMC), which is highly modular, easy to be expanded, and has good output voltage waveform, is especially suitable for medium and high voltage power system applications. The MMC bridge arm inductance fault and the independent sub-module fault have the greatest influence on the transmission quality of transmission systems. However, there is few research on fault diagnosis and location for multiple sub-modules(SM) in a single arm at present. A deep bidirectional Long Short Term Memory (Bid-LSTM) network is proposed in this paper, which is used to diagnose and locate the faults of different sub-modules. which can deal well with the prediction problem with sequence dependence. In addition, because the Bid-LSTM model contains many hyperparameters, which seriously affects the prediction performance of BiD-LSTM, an optimal fruit fly optimization (FOA) algorithm based on the composite chaotic mapping of 3-dimensional Logistic-Sine (3d-CCFOA), which searches the optimal value of related hyperparameters to ensure the accuracy of classification. 3d-CCFOA has good chaos characteristics, which enables FOA to avoid falling into local optimal solution effectively and obtains an effective global optimal solution. Then, the MMC simulation system of RT-TAB is used to simulate the open circuit faults of different independent sub-modules of one bridge arm and collect relevant fault data. The analysis process and experimental results adequately indicate that this method can effectively and accurately diagnose and locate the faults of independent sub-modules. Meanwhile, this method also provides a reference diagnosis strategy for multi-bridge arm fault diagnosis and multi-sub-module location.http://www.sciencedirect.com/science/article/pii/S2352484722013877MMCSub-modularFault diagnosis and locationBid-LSTM3d-CCFOA |
| spellingShingle | Bo Xu Cui Li Huipeng Li Ruchun Ding Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks Energy Reports MMC Sub-modular Fault diagnosis and location Bid-LSTM 3d-CCFOA |
| title | Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks |
| title_full | Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks |
| title_fullStr | Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks |
| title_full_unstemmed | Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks |
| title_short | Fault diagnosis and location of independent sub-module of three-phase MMC based on the optimal deep BiD-LSTM networks |
| title_sort | fault diagnosis and location of independent sub module of three phase mmc based on the optimal deep bid lstm networks |
| topic | MMC Sub-modular Fault diagnosis and location Bid-LSTM 3d-CCFOA |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722013877 |
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