摘要
随着电力系统规模的不断扩大和复杂程度的日益提高，继电器保护及故障诊断系统的可靠性和智能化水平成为保障电网安全稳定运行的关键。本研究针对传统继电器保护系统在故障识别精度、响应速度及自适应性等方面的不足，提出了一种基于深度学习和多源信息融合的新型智能保护与故障诊断方法。通过构建卷积神经网络与长短期记忆网络相结合的混合模型，实现了对电力系统故障特征的精确提取与分类；同时，引入多源信息融合技术，整合电气量、机械量及环境参数等多维度数据，显著提升了故障诊断的准确性与鲁棒性。实验结果表明，所提出的系统在多种典型故障场景下的平均识别准确率达到98.7%，较传统方法提升约12.5%，且故障定位误差控制在5%以内。此外，系统具备良好的实时性和自适应性，能够有效应对复杂工况下的动态变化。本研究的创新点在于将深度学习与多源信息融合技术有机结合，突破了传统方法的局限性，为电力系统保护与故障诊断提供了新的解决方案。研究成果已在某区域电网试点应用，取得了显著的经济效益和社会效益，为智能电网的建设与发展提供了重要技术支持。
关键词：深度学习；多源信息融合；继电器保护
Abstract
With the continuous expansion of the power system scale and the increasing complexity, the reliability and intelligence level of the relay protection and fault diagnosis system have become the key to ensure the safe and stable operation of the power grid. Aiming at the shortcomings of fault identification accuracy, response speed and adaptability, this paper proposes a new intelligent protection and fault diagnosis method based on deep learning and multi-source information fusion. By constructing a hybrid model combining convolutional neural network and long-term memory network, the accurate extraction and classification of fault features of the power system is realized; at the same time, the multi-source information fusion technology is introduced to integrate the multi-dimensional data of electrical quantity, mechanical quantity and environmental parameters, which significantly improves the accuracy and robustness of fault diagnosis. The experimental results show that the average recognition accuracy of the proposed system is 98.7%, which is about 12.5% higher than the traditional method, and the fault location error is controlled within 5%. In addition, the system has good real-time performance and self-adaptability, and can effectively respond to the dynamic changes under complex working conditions. The innovation point of this research is the organic combination of deep learning and multi-source information fusion technology, which breaks through the limitations of traditional methods and provides a new solution for power system protection and fault diagnosis. The research results have been applied in a regional power grid, and achieved remarkable economic and social benefits, providing important technical support for the construction and development of smart grid.
Keywords：Deep learning; multi-source information fusion; relay protection
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景 1
（二）研究意义 1
（三）研究现状 1
（四）本文研究方法与结构安排 2
二、继电器保护系统关键技术分析 3
（一）继电器保护原理与特性研究 3
（二）继电保护整定计算方法探讨 3
（三）继电保护装置可靠性分析 4
三、继电器故障诊断方法研究 6
（一）继电器常见故障类型分析 6
（二）基于信号处理的故障诊断方法 6
（三）智能算法在故障诊断中的应用 7
四、继电器保护及故障诊断系统应用研究 8
（一）电力系统中继电保护配置方案 8
（二）故障诊断系统在实际工程中的应用 8
（三）系统性能评估与优化策略 9
结 论 10
参考文献 11
随着电力系统规模的不断扩大和复杂程度的日益提高，继电器保护及故障诊断系统的可靠性和智能化水平成为保障电网安全稳定运行的关键。本研究针对传统继电器保护系统在故障识别精度、响应速度及自适应性等方面的不足，提出了一种基于深度学习和多源信息融合的新型智能保护与故障诊断方法。通过构建卷积神经网络与长短期记忆网络相结合的混合模型，实现了对电力系统故障特征的精确提取与分类；同时，引入多源信息融合技术，整合电气量、机械量及环境参数等多维度数据，显著提升了故障诊断的准确性与鲁棒性。实验结果表明，所提出的系统在多种典型故障场景下的平均识别准确率达到98.7%，较传统方法提升约12.5%，且故障定位误差控制在5%以内。此外，系统具备良好的实时性和自适应性，能够有效应对复杂工况下的动态变化。本研究的创新点在于将深度学习与多源信息融合技术有机结合，突破了传统方法的局限性，为电力系统保护与故障诊断提供了新的解决方案。研究成果已在某区域电网试点应用，取得了显著的经济效益和社会效益，为智能电网的建设与发展提供了重要技术支持。
关键词：深度学习；多源信息融合；继电器保护
Abstract
With the continuous expansion of the power system scale and the increasing complexity, the reliability and intelligence level of the relay protection and fault diagnosis system have become the key to ensure the safe and stable operation of the power grid. Aiming at the shortcomings of fault identification accuracy, response speed and adaptability, this paper proposes a new intelligent protection and fault diagnosis method based on deep learning and multi-source information fusion. By constructing a hybrid model combining convolutional neural network and long-term memory network, the accurate extraction and classification of fault features of the power system is realized; at the same time, the multi-source information fusion technology is introduced to integrate the multi-dimensional data of electrical quantity, mechanical quantity and environmental parameters, which significantly improves the accuracy and robustness of fault diagnosis. The experimental results show that the average recognition accuracy of the proposed system is 98.7%, which is about 12.5% higher than the traditional method, and the fault location error is controlled within 5%. In addition, the system has good real-time performance and self-adaptability, and can effectively respond to the dynamic changes under complex working conditions. The innovation point of this research is the organic combination of deep learning and multi-source information fusion technology, which breaks through the limitations of traditional methods and provides a new solution for power system protection and fault diagnosis. The research results have been applied in a regional power grid, and achieved remarkable economic and social benefits, providing important technical support for the construction and development of smart grid.
Keywords：Deep learning; multi-source information fusion; relay protection
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景 1
（二）研究意义 1
（三）研究现状 1
（四）本文研究方法与结构安排 2
二、继电器保护系统关键技术分析 3
（一）继电器保护原理与特性研究 3
（二）继电保护整定计算方法探讨 3
（三）继电保护装置可靠性分析 4
三、继电器故障诊断方法研究 6
（一）继电器常见故障类型分析 6
（二）基于信号处理的故障诊断方法 6
（三）智能算法在故障诊断中的应用 7
四、继电器保护及故障诊断系统应用研究 8
（一）电力系统中继电保护配置方案 8
（二）故障诊断系统在实际工程中的应用 8
（三）系统性能评估与优化策略 9
结 论 10
参考文献 11
