基于机器学习算法的电力系统故障诊断
摘要
本研究聚焦于机器学习算法在电力系统故障诊断中的应用。介绍了机器学习算法的基本原理和常用算法，如支持向量机、随机森林和神经网络等，这些算法为电力系统故障诊断提供了强大的分析工具。详细阐述了电力系统故障诊断的基本概念，强调了故障诊断在保障电力系统安全、稳定运行中的关键作用。在分析机器学习算法在电力系统故障诊断中的挑战时，我们发现数据质量和可用性、特征选择、模型训练和推理以及算法适应性和可扩展性是主要难题。为了解决这些问题，本研究提出了基于机器学习算法的电力系统故障诊断方法，包括严格的数据预处理和校验机制、先进的特征选择技术和自动化方法以及优化模型训练和推理过程。这些方法旨在提高故障诊断的准确性、实时性和鲁棒性。特别地，关注于提高算法的适应性和可扩展性。探讨集成学习、迁移学习等策略，以增强模型对复杂多变故障模式的处理能力。通过对比实验和案例分析，我们验证了所提方法的有效性和优越性。总之，本研究为电力系统故障诊断提供了一种基于机器学习算法的新思路和方法，对于提高电力系统的故障诊断能力、保障电力系统的安全稳定运行具有重要意义。
关键词：机器学习算法；电力系统；特征选择；模型训练
Abstract
This study focuses on the application of machine learning algorithms in power system fault diagnosis. Introduced the basic principles and commonly used algorithms of machine learning, such as support vector machines, random forests, and neural networks, which provide powerful analytical tools for power system fault diagnosis. This article elaborates on the basic concepts of power system fault diagnosis and emphasizes the crucial role of fault diagnosis in ensuring the safe and stable operation of the power system. When analyzing the challenges of machine learning algorithms in power system fault diagnosis, we found that data quality and availability, feature selection, model training and inference, as well as algorithm adaptability and scalability are the main challenges. To address these issues, this study proposes a power system fault diagnosis method based on machine learning algorithms, including strict data preprocessing and verification mechanisms, advanced feature selection techniques and automation methods, as well as optimized model training and inference processes. These methods aim to improve the accuracy, real-time performance, and robustness of fault diagnosis. Specifically, it focuses on improving the adaptability and scalability of algorithms. Explore strategies such as ensemble learning and transfer learning to enhance the model's ability to handle complex and variable fault modes. Through comparative experiments and case analysis, we have verified the effectiveness and superiority of the proposed method. In summary, this study provides a new approach and method for fault diagnosis in power systems based on machine learning algorithms, which is of great significance for improving the fault diagnosis ability of power systems and ensuring their safe and stable operation.
Keywords： Machine learning algorithms; Power system; Feature selection; Model training
目 录
摘要 I
Abstract II
一、绪论 1
（一）研究背景及意义 1
（二）国内外研究现状 1
（三）研究目的及内容 2
二、电力系统故障诊断基础 3
（一）机器学习算法概述 3
（二）电力系统故障诊断的基本概念 4
三、机器学习算法在电力系统故障诊断中的挑战 5
（一）数据质量和可用性 5
（二）特征选择 5
（三）模型训练和推理 6
（四）算法适应性和可扩展性 6
四、基于机器学习算法的电力系统故障诊断方法 7
（一）严格的数据预处理和校验机制 7
（二）先进的特征选择技术和自动化方法 7
（三）优化模型训练和推理过程 8
（四）提高算法的适应性和可扩展性 8
结 论 10
参考文献 11
