Research on fault diagnosis and self-healing control of power systems based on deep learning
基于深度学习的电力系统故障诊断与自愈控制研究

摘要

本文研究了基于深度学习的电力系统故障诊断与自愈控制技术，提出了一种智能化的解决方案来应对电力系统中日益复杂的故障情形。通过深度学习模型，能够从海量的电力系统数据中自动提取特征，快速准确地识别故障类型与位置，并结合诊断结果实现自愈控制，如自动切换负荷、调整电网拓扑结构等。实验表明，所提出的故障诊断与自愈控制方法相比传统方法在准确性、实时性和自动化水平上具有显著优势，能够有效提升电力系统的稳定性与智能化程度，为智能电网的建设提供了坚实的技术支持。

Abstract

This paper studies fault diagnosis and self-healing control technologies for power systems based on deep learning, and proposes an intelligent solution to address the increasingly complex fault scenarios in power systems. Through deep learning models, it is possible to automatically extract features from massive power system data, quickly and accurately identify fault types and locations, and implement self-healing control based on diagnostic results, such as automatic load switching and adjustment of the power grid topology. Experiments show that the proposed fault diagnosis and self-healing control method significantly outperforms traditional methods in terms of accuracy, real-time performance, and automation level. It can effectively enhance the stability and intelligence of power systems, providing a solid technical support for the construction of smart grids.