Research on power transformer fault prediction and health management (PHM) system based on deep learning
基于深度学习的电力变压器故障预测与健康管理（PHM）系统研究

摘要

基于深度学习的电力变压器故障预测与健康管理（PHM）系统，可突破传统方法在复杂工况下预测滞后、管理被动的局限，显著提升故障预测精度与健康管理效率。该系统依托分层架构与多功能模块协同，采集变压器多维度运行数据，通过深度学习算法完成特征提取、模式识别与故障预测模型构建，实现潜在故障提前预警；同时结合健康状态评估机制，为变压器全生命周期管理提供科学支撑。实践表明，系统能有效降低故障发生率与运维成本，保障电力系统稳定运行，对推动电力行业向智能化、高效化发展具有重要现实意义。

Abstract

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.