Scientific Development Research

The Power Transformer Fault Prediction and Health Management (PHM) System based on deep learning overcomes the limitations of traditional methods, such as lagging prediction and passive management under complex operating conditions, thereby significantly improving fault prediction accuracy and health management efficiency. The system relies on a hierarchical architecture and collaborative multi-functional modules to collect multi-dimensional operational data from transformers. Through deep learning algorithms, it performs feature extraction, pattern recognition, and fault prediction model construction to achieve early warning of potential failures. At the same time, by integrating a health status evaluation mechanism, it provides scientific support for the entire life-cycle management of transformers. Practical results show that the system effectively reduces failure rates and maintenance costs, ensures the stable operation of the power system, and plays a crucial role in promoting the intelligent and efficient development of the power industry.

Power transformer; Deep learning; Fault prediction; Health management (PHM) system