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Article

A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction

1
Jiangsu Institute of Metrology, Nanjing 210023, China
2
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(11), 3478; https://doi.org/10.3390/s25113478
Submission received: 20 April 2025 / Revised: 29 May 2025 / Accepted: 29 May 2025 / Published: 31 May 2025
(This article belongs to the Section Electronic Sensors)

Abstract

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.
Keywords: ultra-high voltage energy device; fault warning; leakage current; signal decomposition; feature prediction ultra-high voltage energy device; fault warning; leakage current; signal decomposition; feature prediction

Share and Cite

MDPI and ACS Style

Zhao, P.; Wang, L.; Wei, J.; Wang, Y.; Wu, H. A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction. Sensors 2025, 25, 3478. https://doi.org/10.3390/s25113478

AMA Style

Zhao P, Wang L, Wei J, Wang Y, Wu H. A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction. Sensors. 2025; 25(11):3478. https://doi.org/10.3390/s25113478

Chicago/Turabian Style

Zhao, Pinzhang, Lihui Wang, Jian Wei, Yifan Wang, and Haifeng Wu. 2025. "A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction" Sensors 25, no. 11: 3478. https://doi.org/10.3390/s25113478

APA Style

Zhao, P., Wang, L., Wei, J., Wang, Y., & Wu, H. (2025). A Degradation Warning Method for Ultra-High Voltage Energy Devices Based on Time-Frequency Feature Prediction. Sensors, 25(11), 3478. https://doi.org/10.3390/s25113478

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