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

## TL;DR

This paper introduces a new method to predict and warn about wear in ultra-high voltage energy devices using advanced signal processing and prediction techniques.

## Contribution

The novel ISGMD-WP algorithm and enhanced I-Informer network improve leakage current analysis and prediction for early fault detection.

## Key findings

- The ISGMD-WP algorithm achieves an EIDC of 1.95 under intense noise, effectively decomposing leakage current.
- The I-Informer network predicts faults with MAE of 0.02538 and RMSE of 0.03175 in long-term predictions.
- The method enables timely warnings for lightning arrester deterioration across multiple columns.

## 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.