Research on artificial intelligence-based fault location and self-healing control methods for distribution networks
基于人工智能的配电网故障定位与自愈控制方法研究

摘要

配电网故障的快速定位与高效自愈是保障电力系统稳定运行的关键。传统方法存在定位耗时、自愈响应滞后等问题，人工智能技术的引入为解决这些难题提供了新路径。通过构建基于深度学习的故障特征识别模型，可精准提取故障信号中的关键信息，实现故障类型与位置的快速判定；结合强化学习算法优化自愈控制策略，能根据电网拓扑结构与负荷状态动态调整恢复方案，缩短故障恢复时间。这一研究为提升配电网的可靠性与韧性提供了技术支撑，对保障电力持续供应具有重要意义。

Abstract

Rapid fault location and efficient self-healing of distribution networks are crucial for ensuring the stable operation of power systems. Traditional methods have problems such as time-consuming location and delayed self-healing responses, and the introduction of artificial intelligence technology provides a new path to solve these problems. By constructing a fault feature recognition model based on deep learning, key information in fault signals can be accurately extracted to achieve rapid determination of fault types and locations; combining reinforcement learning algorithms to optimize self-healing control strategies can dynamically adjust recovery plans according to the grid topology and load status, shortening the fault recovery time. This research provides technical support for improving the reliability and resilience of distribution networks and is of great significance for ensuring continuous power supply.