Insulation aging and fault diagnosis of high-voltage equipment based on deep Q-networks

Abstract

In response to the escalating demand for electric energy in the world, the maintenance technology for electric power equipment has evolved significantly. This study addresses the critical issue of insulation aging in high voltage equipment through an innovative approach. By integrating advanced Deep Q-networks (DQN) algorithms and intelligent data mining techniques, our proposed diagnostic scheme enables accurate and timely fault detection. The intelligent DQN algorithms process real-time equipment data, providing essential insights into operational status and faults. Implementing this research offers a strategic framework for enhancing the reliability of high-voltage equipment, contributing to stable system operation, and laying the groundwork for future advancements in equipment condition maintenance.