摘 要
随着电力系统规模的不断扩大和复杂性的持续提升，故障预测与预警已成为保障电网安全稳定运行的重要环节，而传统方法在处理高维、非线性数据时存在局限性，难以满足现代电力系统的需求为此，本文提出了一种基于机器学习的电力系统故障预测与预警方法，旨在通过挖掘海量历史数据中的潜在规律，实现对故障的精准预测和及时预警研究中，首先构建了包含多种特征参数的数据集，并采用主成分分析法进行降维处理以优化输入数据质量随后，引入深度学习模型如卷积神经网络和长短期记忆网络，结合支持向量机等经典算法，建立了多模型融合的预测框架该框架能够有效捕捉时间序列数据中的时空关联特性，并通过集成学习提升整体预测性能。
关键词：电力系统故障预测 多模型融合 深度学习
Abstract
As the scale of the power system continues to expand and the complexity continues to increase, Fault prediction and early warning have become an important link to ensure the safe and stable operation of the power grid, While the traditional methods have limitations in dealing with high-dimensional, non-linear data, To meet the needs of a modern power system, This paper presents a fault prediction and early warning method of power system, Aiming to mine the underlying laws in the massive historical data, To realize the accurate prediction and timely early warning of faults, A dataset containing multiple feature parameters was first constructed, And using principal component analysis to optimize the input data quality subsequently, Introducing deep learning models such as convolutional neural networks and long and short-term memory networks, Combined with classical algorithms such as support vector machines, This fr amework can effectively capture the spatiotemporal correlation characteristics in time series data, And improve the overall prediction performance through integrated learning.
Keyword:Power System Fault Prediction Multi-Model Fusion Deep Learning
目 录
1绪论 1
1.1电力系统故障预测的研究背景 1
1.2基于机器学习方法的意义分析 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2电力系统故障数据的特征提取与处理 2
2.1数据采集与预处理方法 2
2.2特征选择与降维技术应用 3
2.3异常数据检测与清洗策略 3
2.4数据集构建与标注方法 4
2.5数据质量对模型性能的影响 4
3基于机器学习的故障预测模型构建 4
3.1机器学习算法在电力系统的适用性分析 4
3.2常见预测模型的对比与选择 5
3.3模型参数优化与调优方法 5
3.4集成学习在故障预测中的应用 6
3.5模型训练与验证流程设计 6
4故障预警系统的实现与评估 7
4.1预警系统架构设计与功能模块划分 7
4.2实时数据流处理与预测集成 7
4.3预警阈值设定与决策规则制定 8
4.4系统性能评估指标体系构建 8
4.5实验结果分析与改进方向探讨 9
结论 9
参考文献 11
致谢 12
