摘 要
随着电力系统规模的不断扩大和复杂性的持续提升，传统故障诊断与预测方法已难以满足现代电网对可靠性和安全性的要求，而大数据分析技术为解决这一问题提供了新的思路和手段本研究旨在利用大数据分析技术，构建一种高效、精准的电力系统故障诊断与预测模型，以提高电力系统的运行效率和安全性研究中采用多种先进的大数据处理方法，包括数据清洗、特征提取、机器学习算法优化等，并结合实际电力系统的运行数据进行建模和验证通过对比分析不同算法的性能表现，最终确定基于深度学习的卷积神经网络模型在故障诊断与预测任务中具有最佳效果实验结果表明，该模型能够准确识别电力系统中的潜在故障类型，并提前预警可能发生的异常情况，其诊断准确率超过95%，预测精度较传统方法提升约20%此外，本研究还提出了一种自适应数据预处理框架，有效解决了电力数据中存在的噪声干扰和不平衡分布问题，显著提升了模型的鲁棒性和泛化能力总体而言，本研究不仅为电力系统故障诊断与预测提供了一种创新的技术方案，也为大数据分析技术在其他工业领域的应用拓展了思路
关键词：电力系统故障诊断；大数据分析；深度学习；卷积神经网络；自适应数据预处理
ABSTRACT
With the continuous expansion of power system scale and increasing complexity, traditional fault diagnosis and prediction methods are becoming insufficient to meet the reliability and safety requirements of modern power grids. Big data analytics technology offers new approaches and tools to address this challenge. This study aims to leverage big data analytics to construct an efficient and precise fault diagnosis and prediction model for power systems, thereby enhancing operational efficiency and safety. Advanced big data processing techniques, including data cleaning, feature extraction, and optimization of machine learning algorithms, were employed in conjunction with real-world operational data from power systems for modeling and validation. Through comparative analysis of the performance of various algorithms, it was determined that a deep-learning-based convolutional neural network model achieves the best performance in fault diagnosis and prediction tasks. Experimental results demonstrate that the proposed model can accurately identify potential fault types in power systems and provide early warnings for possible anomalies, achieving a diagnostic accuracy exceeding 95% and improving prediction precision by approximately 20% compared to traditional methods. Additionally, this study proposes an adaptive data preprocessing fr amework that effectively addresses issues of noise interference and imbalanced distribution in power system data, significantly enhancing the robustness and generalization capability of the model. Overall, this research not only provides an innovative technical solution for fault diagnosis and prediction in power systems but also expands the application prospects of big data analytics in other industrial domains.
Keywords: Power System Fault Diagnosis; Big Data Analysis; Deep Learning; Convolutional Neural Network; Adaptive Data Preprocessing
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 电力系统故障诊断的研究背景 1
1.2 大数据分析在电力系统的意义 1
1.3 国内外研究现状与挑战 1
1.4 本文研究方法与技术路线 2
第2章 大数据技术在电力系统中的应用基础 3
2.1 电力系统大数据的特点与分类 3
2.2 数据采集与预处理的关键技术 3
2.3 常用大数据分析方法概述 4
2.4 数据驱动的电力系统建模方法 4
2.5 应用基础对故障诊断的支持 5
第3章 故障诊断技术的理论与实践 6
3.1 故障诊断的基本原理与框架 6
3.2 基于机器学习的故障识别方法 6
3.3 数据驱动的特征提取与模式识别 7
3.4 实时监测与异常检测技术 7
3.5 典型案例分析与效果评估 8
第4章 故障预测技术的发展与优化 9
4.1 故障预测的核心算法与模型 9
4.2 时间序列分析在预测中的应用 9
4.3 深度学习在故障预测中的优势 10
4.4 预测精度提升的技术路径 10
4.5 预测结果的验证与改进策略 11
结论 12
参考文献 13
致 谢 14
