摘 要
电力系统作为现代社会的重要基础设施，其安全稳定运行对经济发展和人民生活具有重要意义，然而故障不可避免地会对系统的正常运行造成影响，因此研究高效的故障诊断与恢复策略显得尤为关键。本研究旨在针对复杂电力系统中的故障问题，提出一种基于多源信息融合的故障诊断方法，并设计相应的快速恢复策略以提升系统的韧性。研究通过结合数据驱动与知识驱动技术，构建了包含特征提取、模式识别及决策支持的综合诊断框架，同时引入图论优化算法改进传统恢复方案，显著提高了故障定位精度和恢复效率。实验结果表明，所提方法能够在多种工况下实现高准确率的故障诊断，并有效缩短停电时间，为实际工程应用提供了可靠的技术支撑。此外，本研究创新性地将人工智能技术与电力系统特性深度融合，突破了传统方法在复杂场景下的局限性，为未来智能电网的发展奠定了理论基础。总体而言，该研究不仅提升了电力系统的故障应对能力，还为相关领域的技术创新提供了有益借鉴。
关键词：电力系统故障诊断；多源信息融合；快速恢复策略；数据驱动与知识驱动；图论优化算法
Abstract
As a critical infrastructure of modern society, the secure and stable operation of power systems plays a significant role in economic development and people's daily lives. However, faults inevitably affect the normal operation of the system, making the study of efficient fault diagnosis and recovery strategies particularly crucial. This research focuses on fault issues in complex power systems and proposes a fault diagnosis method based on multi-source information fusion, along with a corresponding rapid recovery strategy to enhance system resilience. By integrating data-driven and knowledge-driven technologies, a comprehensive diagnostic fr amework is constructed, encompassing feature extraction, pattern recognition, and decision support. Meanwhile, graph theory optimization algorithms are introduced to improve traditional recovery schemes, significantly enhancing the accuracy of fault location and recovery efficiency. Experimental results demonstrate that the proposed method achieves high-accuracy fault diagnosis under various operating conditions and effectively reduces outage time, providing a reliable technical support for practical engineering applications. Additionally, this study innovatively integrates artificial intelligence technology with the characteristics of power systems, overcoming the limitations of traditional methods in complex scenarios and laying a theoretical foundation for the development of future smart grids. Overall, this research not only improves the fault response capability of power systems but also offers valuable references for technological innovation in related fields.
Keywords: Power System Fault Diagnosis；Multi-Source Information Fusion；Rapid Recovery Strategy；Data-Driven And Knowledge-Driven；Graph Theory Optimization Algorithm
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电力系统故障诊断与恢复的研究背景 1
（二）故障诊断与恢复策略的研究意义 1
（三）国内外研究现状分析 1
（四）本文研究方法与技术路线 2
二、电力系统故障诊断关键技术研究 2
（一）故障诊断的基本原理与方法 2
（二）数据驱动的故障特征提取技术 3
（三）基于人工智能的故障识别算法 3
（四）故障诊断技术的优化与改进 4
三、电力系统恢复策略设计与优化 4
（一）恢复策略的基本框架与原则 4
（二）快速恢复的关键技术与实现路径 5
（三）基于多目标优化的恢复方案设计 5
（四）恢复策略的仿真验证与评估 6
四、实际案例分析与应用研究 6
（一）典型电力系统的故障案例分析 6
（二）故障诊断与恢复的实际应用流程 7
（三）案例中恢复策略的效果评估 7
（四）应用中存在的问题与改进建议 7
结 论 8
致 谢 9
参考文献 10
