A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems
The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used f...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5082 |
| _version_ | 1827739250482741248 |
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| author | Sihan Zhou Liang Qin Yong Yang Zheng Wei Jialong Wang Jing Wang Jiangjun Ruan Xu Tang Xiaole Wang Kaipei Liu |
| author_facet | Sihan Zhou Liang Qin Yong Yang Zheng Wei Jialong Wang Jing Wang Jiangjun Ruan Xu Tang Xiaole Wang Kaipei Liu |
| author_sort | Sihan Zhou |
| collection | DOAJ |
| description | The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an <i>F</i><sub>1</sub> score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in <i>F</i><sub>1</sub> score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in <i>F</i><sub>1</sub> score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems. |
| first_indexed | 2024-03-11T02:58:09Z |
| format | Article |
| id | doaj.art-e96d063ecafd4d779b68ee3c140e5382 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:58:09Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e96d063ecafd4d779b68ee3c140e53822023-11-18T08:32:18ZengMDPI AGSensors1424-82202023-05-012311508210.3390/s23115082A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission SystemsSihan Zhou0Liang Qin1Yong Yang2Zheng Wei3Jialong Wang4Jing Wang5Jiangjun Ruan6Xu Tang7Xiaole Wang8Kaipei Liu9Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaState Grid Economic Technology Research Institute Co., Ltd., Beijing 102200, ChinaState Grid Economic Technology Research Institute Co., Ltd., Beijing 102200, ChinaState Grid Economic Technology Research Institute Co., Ltd., Beijing 102200, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaHubei Key Laboratory of Power Equipment & System Security for Integrated Energy, Wuhan 430072, ChinaThe intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an <i>F</i><sub>1</sub> score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in <i>F</i><sub>1</sub> score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in <i>F</i><sub>1</sub> score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.https://www.mdpi.com/1424-8220/23/11/5082fault diagnosisVSG-HVDCconverter stationconverter valvemain circulation bumpensemble learning |
| spellingShingle | Sihan Zhou Liang Qin Yong Yang Zheng Wei Jialong Wang Jing Wang Jiangjun Ruan Xu Tang Xiaole Wang Kaipei Liu A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems Sensors fault diagnosis VSG-HVDC converter station converter valve main circulation bump ensemble learning |
| title | A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems |
| title_full | A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems |
| title_fullStr | A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems |
| title_full_unstemmed | A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems |
| title_short | A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems |
| title_sort | novel ensemble fault diagnosis model for main circulation pumps of converter valves in vsc hvdc transmission systems |
| topic | fault diagnosis VSG-HVDC converter station converter valve main circulation bump ensemble learning |
| url | https://www.mdpi.com/1424-8220/23/11/5082 |
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