摘要
随着电力系统的日益复杂化和智能化，电力设备的可靠运行成为保障电网稳定的关键因素。为提高故障预测的准确性并降低维护成本，本研究基于机器学习技术展开电力设备故障预测方法的探索。研究以提升预测模型性能为核心目标，通过整合多种数据源（如传感器数据、历史维修记录及环境参数），构建了一套适用于复杂工况的故障预测框架。具体而言，采用深度学习算法对非线性特征进行提取，并结合传统机器学习方法优化模型泛化能力。实验结果表明，所提方法在故障预测精度上较现有技术提升了约15%，同时具备较强的实时性和鲁棒性。此外，研究创新性地引入了迁移学习策略，有效解决了小样本场景下的模型训练难题，显著增强了模型在不同设备类型间的适应性。最终结论显示，基于机器学习的故障预测方法能够为电力系统提供更为精准和高效的运维支持，为智能电网的发展奠定了重要基础。该研究不仅验证了机器学习技术在电力设备领域的应用潜力，还为后续相关研究提供了有价值的参考方向。
关键词：电力设备故障预测；机器学习；深度学习
Abstract
With the increasing complexity and intelligence of power systems, the reliable operation of power equipment has become a key factor in ensuring grid stability. To enhance the accuracy of fault prediction and reduce maintenance costs, this study explores fault prediction methods for power equipment based on machine learning technologies. Focusing on improving the performance of prediction models, the study constructs a fault prediction fr amework suitable for complex operating conditions by integrating multiple data sources, such as sensor data, historical maintenance records, and environmental parameters. Specifically, deep learning algorithms are employed to extract nonlinear features, while traditional machine learning methods are combined to optimize the model's generalization capability. Experimental results demonstrate that the proposed method improves fault prediction accuracy by approximately 15% compared to existing technologies, with strong real-time performance and robustness. Additionally, the study innovatively incorporates transfer learning strategies to effectively address model training challenges in small-sample scenarios, significantly enhancing adaptability across different equipment types. The final conclusions indicate that machine-learning-based fault prediction methods can provide more precise and efficient operation and maintenance support for power systems, laying an important foundation for the development of smart grids. This research not only verifies the application potential of machine learning technologies in the field of power equipment but also provides valuable reference directions for subsequent related studies.
Keywords：Power Equipment Fault Prediction; Machine Learning; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力设备故障预测的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 研究方法与技术路线 2
二、机器学习在电力设备故障预测中的应用基础 2
(一) 电力设备故障预测的基本原理 2
(二) 常见机器学习算法概述 3
(三) 数据预处理与特征提取方法 3
三、面向电力设备的机器学习模型构建与优化 4
(一) 模型选择与适用性分析 4
(二) 数据驱动的模型训练与验证 5
(三) 模型性能评估与优化策略 5
四、实验设计与案例分析 6
(一) 实验数据集与环境配置 6
(二) 故障预测实验结果分析 6
(三) 模型对比与改进方向探讨 7
结 论 8
参考文献 9
随着电力系统的日益复杂化和智能化，电力设备的可靠运行成为保障电网稳定的关键因素。为提高故障预测的准确性并降低维护成本，本研究基于机器学习技术展开电力设备故障预测方法的探索。研究以提升预测模型性能为核心目标，通过整合多种数据源（如传感器数据、历史维修记录及环境参数），构建了一套适用于复杂工况的故障预测框架。具体而言，采用深度学习算法对非线性特征进行提取，并结合传统机器学习方法优化模型泛化能力。实验结果表明，所提方法在故障预测精度上较现有技术提升了约15%，同时具备较强的实时性和鲁棒性。此外，研究创新性地引入了迁移学习策略，有效解决了小样本场景下的模型训练难题，显著增强了模型在不同设备类型间的适应性。最终结论显示，基于机器学习的故障预测方法能够为电力系统提供更为精准和高效的运维支持，为智能电网的发展奠定了重要基础。该研究不仅验证了机器学习技术在电力设备领域的应用潜力，还为后续相关研究提供了有价值的参考方向。
关键词：电力设备故障预测；机器学习；深度学习
Abstract
With the increasing complexity and intelligence of power systems, the reliable operation of power equipment has become a key factor in ensuring grid stability. To enhance the accuracy of fault prediction and reduce maintenance costs, this study explores fault prediction methods for power equipment based on machine learning technologies. Focusing on improving the performance of prediction models, the study constructs a fault prediction fr amework suitable for complex operating conditions by integrating multiple data sources, such as sensor data, historical maintenance records, and environmental parameters. Specifically, deep learning algorithms are employed to extract nonlinear features, while traditional machine learning methods are combined to optimize the model's generalization capability. Experimental results demonstrate that the proposed method improves fault prediction accuracy by approximately 15% compared to existing technologies, with strong real-time performance and robustness. Additionally, the study innovatively incorporates transfer learning strategies to effectively address model training challenges in small-sample scenarios, significantly enhancing adaptability across different equipment types. The final conclusions indicate that machine-learning-based fault prediction methods can provide more precise and efficient operation and maintenance support for power systems, laying an important foundation for the development of smart grids. This research not only verifies the application potential of machine learning technologies in the field of power equipment but also provides valuable reference directions for subsequent related studies.
Keywords：Power Equipment Fault Prediction; Machine Learning; Deep Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 电力设备故障预测的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 研究方法与技术路线 2
二、机器学习在电力设备故障预测中的应用基础 2
(一) 电力设备故障预测的基本原理 2
(二) 常见机器学习算法概述 3
(三) 数据预处理与特征提取方法 3
三、面向电力设备的机器学习模型构建与优化 4
(一) 模型选择与适用性分析 4
(二) 数据驱动的模型训练与验证 5
(三) 模型性能评估与优化策略 5
四、实验设计与案例分析 6
(一) 实验数据集与环境配置 6
(二) 故障预测实验结果分析 6
(三) 模型对比与改进方向探讨 7
结 论 8
参考文献 9
