Early fault diagnosis of transformers based on relative deterioration analysis

Abstract

This paper aims to enhance the fault diagnosis accuracy of oil-immersed power transformers by proposing a fault prediction framework based on CECNN-Bi-LSTM and an improved Decision Tree model. The framework introduces a Channel Equalization Module (CE-Block) within the CNN to activate suppressed channels, significantly boosting prediction accuracy. Additionally, the Decision Tree model is improved using the trace distance function to strengthen classification performance. The approach involves decomposing dissolved gas concentration data using Variational Mode Decomposition (VMD) to extract key features, then applying the CECNN-Bi-LSTM model to analyze the time-series data of dissolved gases for high-precision combined predictions. A Generative Adversarial Network (GAN) is used to balance fault classification data, enhancing robustness. Finally, the Entropy Weight-based Relative Degradation Degree (EM-RDA) evaluation standard increases classification feature dimensions and the improved Decision Tree accurately classifies transformer operating states. Simulation and case study results show that the proposed method achieves over 97% diagnostic accuracy, effectively predicting and preventing potential faults in transformers, thereby ensuring the stable operation of the power system.