Power Electronic Equipment Fault Diagnosis Algorithm Based on Deep Reinforcement Learning

This study proposes a power electronic equipment fault diagnosis model based on deep reinforcement learning (DRL), aiming to improve the accuracy and real-time performance of power electronic equipment fault diagnosis. By designing appropriate state space, action space, and reward function, the deep reinforcement learning model can autonomously learn the operating characteristics of the equipment and make accurate judgments under different failure modes. Experimental results show that the accuracy of the deep reinforcement learning model is significantly higher than that of the traditional method in the diagnosis of various fault types (such as normal operation, overload fault, open circuit fault and short circuit fault). The advantages of deep reinforcement learning are particularly prominent when dealing with complex faults. The model also demonstrates high efficiency in terms of response time, being able to make fault diagnosis decisions in a shorter time and reduce equipment downtime. In addition, the model has good stability in long-term operation and can continuously optimize its fault diagnosis strategy, showing strong adaptive ability. Despite this, deep reinforcement learning models still rely on large amounts of data and computing resources, and performance can be further optimized in the future through data enhancement, model optimization, and improved computing efficiency. This study shows that the fault diagnosis method based on deep reinforcement learning has broad application prospects and can provide efficient and intelligent fault detection and repair solutions for power systems.