摘 要
电力系统作为现代社会的重要基础设施，其稳定运行对经济发展和社会生活至关重要。然而，电力系统结构复杂、规模庞大且运行环境多变，故障难以避免，传统故障诊断与预警方法存在诸多局限，如依赖专家经验、实时性差等。为此，本文基于机器学习开展电力系统故障诊断与预警研究，旨在提高故障诊断准确性与预警及时性。首先构建包含多种电力设备运行状态数据的样本库，涵盖正常及故障状态下不同特征参数，为后续分析奠定基础。接着选用支持向量机、神经网络等典型机器学习算法进行训练，通过对比不同算法性能确定最优模型。在此基础上，提出融合多源异构数据的故障诊断框架，该框架能够综合电气量、非电气量等多维度信息，有效提升故障识别能力。同时，引入深度学习技术建立预警模型，实现对未来可能发生的故障提前预判。实验结果表明，所提方法在故障诊断准确率方面较传统方法有显著提升，达到95%以上，预警提前时间平均延长至30分钟，极大增强了电力系统的安全性与可靠性，为电力系统智能化运维提供新思路。
关键词：电力系统故障诊断 机器学习 支持向量机
Abstract
The power system, as a critical infrastructure of modern society, plays an indispensable role in economic development and social life. However, due to its complex structure, large scale, and variable operating environment, faults are inevitable, and traditional fault diagnosis and early warning methods have many limitations, such as reliance on expert experience and poor real-time performance. To address these issues, this study focuses on fault diagnosis and early warning in power systems based on machine learning, aiming to improve the accuracy of fault diagnosis and the timeliness of warnings. A sample database containing operational status data from various power equipment is first constructed, covering different characteristic parameters under both normal and fault conditions, thereby laying the foundation for subsequent analysis. Subsequently, typical machine learning algorithms such as Support Vector Machines (SVM) and neural networks are selected for training, and the optimal model is determined by comparing the performance of different algorithms. On this basis, a fault diagnosis fr amework that integrates multi-source heterogeneous data is proposed, which can comprehensively incorporate multidimensional information including electrical and non-electrical quantities, effectively enhancing fault recognition capabilities. Meanwhile, deep learning techniques are introduced to establish early warning models, enabling the prediction of potential future faults. Experimental results show that the proposed method significantly improves the accuracy of fault diagnosis compared to traditional methods, achieving over 95%, with the average early warning time extended to 30 minutes. This greatly enhances the safety and reliability of power systems and provides new insights for intelligent operation and maintenance of power systems.
Keyword:Power System Fault Diagnosis Machine Learning Support Vector Machine
目 录
引言 1
1电力系统故障诊断基础 1
1.1电力系统故障类型分析 1
1.2故障诊断技术现状综述 2
1.3机器学习在故障诊断中的优势 2
2数据获取与预处理方法 3
2.1故障数据采集技术 3
2.2数据清洗与特征提取 3
2.3数据标注与样本构建 4
3机器学习算法应用研究 4
3.1常用机器学习算法比较 4
3.2算法模型训练与优化 5
3.3模型评估与性能分析 6
4故障预警系统设计实现 6
4.1预警指标体系建立 6
4.2实时监测与预警机制 7
4.3系统集成与应用案例 7
结论 8
参考文献 9
致谢 10
