A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction
This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted featur...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3478 |
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| author | Pinzhang Zhao Lihui Wang Jian Wei Yifan Wang Haifeng Wu |
| author_facet | Pinzhang Zhao Lihui Wang Jian Wei Yifan Wang Haifeng Wu |
| author_sort | Pinzhang Zhao |
| collection | DOAJ |
| description | This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault. |
| format | Article |
| id | doaj-art-49c54d052c2d474597c24aaac72b6fbd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-49c54d052c2d474597c24aaac72b6fbd2025-08-20T03:46:38ZengMDPI AGSensors1424-82202025-05-012511347810.3390/s25113478A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature PredictionPinzhang Zhao0Lihui Wang1Jian Wei2Yifan Wang3Haifeng Wu4Jiangsu Institute of Metrology, Nanjing 210023, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaJiangsu Institute of Metrology, Nanjing 210023, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaKey Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, ChinaThis study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault.https://www.mdpi.com/1424-8220/25/11/3478ultra-high voltage energy devicefault warningleakage currentsignal decompositionfeature prediction |
| spellingShingle | Pinzhang Zhao Lihui Wang Jian Wei Yifan Wang Haifeng Wu A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction Sensors ultra-high voltage energy device fault warning leakage current signal decomposition feature prediction |
| title | A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction |
| title_full | A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction |
| title_fullStr | A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction |
| title_full_unstemmed | A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction |
| title_short | A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction |
| title_sort | degradation warning method for ultra high voltage energy devices based on time frequency feature prediction |
| topic | ultra-high voltage energy device fault warning leakage current signal decomposition feature prediction |
| url | https://www.mdpi.com/1424-8220/25/11/3478 |
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