摘 要
随着电力系统的规模不断扩大和复杂性持续提升，传统故障诊断方法已难以满足现代电网对高精度、快速响应和智能化运维的需求。为此，本文提出了一种基于大数据技术的电力系统故障诊断与预警系统设计方法，旨在通过整合多源数据和先进算法实现对电力系统潜在故障的精准识别与提前预警。研究首先构建了涵盖设备运行状态、环境参数及历史故障记录的多维度大数据集，并采用数据清洗与特征提取技术优化数据质量。在此基础上，引入深度学习模型结合时序分析方法，以挖掘隐藏在海量数据中的故障模式和关联关系。实验结果表明，该系统能够显著提高故障诊断的准确性，同时将预警时间提前至故障发生前数小时，为运维人员提供了充足的决策支持。此外，本文创新性地提出了自适应阈值调整机制，有效解决了不同场景下诊断模型泛化能力不足的问题。总体而言，本研究不仅为电力系统故障诊断领域提供了新的技术路径，还为大数据驱动的智能电网建设奠定了理论与实践基础，具有重要的工程应用价值和广阔的发展前景。
关键词：电力系统故障诊断；大数据技术；深度学习；预警系统；自适应阈值调整机制
ABSTRACT
With the continuous expansion of the scale and increasing complexity of power systems, traditional fault diagnosis methods are struggling to meet the modern grid's requirements for high precision, rapid response, and intelligent operation and maintenance. To address this challenge, this paper proposes a design method for a power system fault diagnosis and early warning system based on big data technology, aiming to achieve precise identification and advanced warning of potential faults by integrating multi-source data and advanced algorithms. The study first constructs a multidimensional big data set that includes equipment operating conditions, environmental parameters, and historical fault records, and optimizes data quality through data cleaning and feature extraction techniques. On this basis, a deep learning model combined with time-series analysis is introduced to mine fault patterns and correlations hidden in massive datasets. Experimental results demonstrate that the proposed system can significantly enhance the accuracy of fault diagnosis while advancing the warning time to several hours before fault occurrence, providing sufficient decision support for operation and maintenance personnel. Additionally, this paper innovatively proposes an adaptive threshold adjustment mechanism, effectively resolving the issue of insufficient generalization capability of diagnostic models under different scenarios. Overall, this research not only provides a new technical approach for the field of power system fault diagnosis but also lays a theoretical and practical foundation for the construction of smart grids driven by big data, showcasing significant engineering application value and broad development prospects.
Keywords: Power System Fault Diagnosis; Big Data Technology; Deep Learning; Early Warning System; Adaptive Threshold Adjustment Mechanism
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 大数据技术在电力系统中的意义 1
1.3 国内外研究现状与发展趋势 2
1.4 本文研究方法与技术路线 2
第2章 基于大数据的故障数据采集与处理 3
2.1 电力系统故障数据的特点分析 3
2.2 数据采集技术与传感器部署策略 3
2.3 数据预处理方法与质量评估 4
2.4 异常数据检测与清洗算法研究 4
2.5 数据存储与管理架构设计 5
第3章 故障诊断模型的设计与优化 6
3.1 故障诊断的核心理论基础 6
3.2 基于机器学习的诊断模型构建 6
3.3 深度学习在故障诊断中的应用 7
3.4 模型性能评估与优化策略 7
3.5 实时诊断系统的实现方案 8
第4章 预警系统的设计与实现 9
4.1 预警系统的基本功能需求 9
4.2 风险评估与预警指标体系构建 9
4.3 基于大数据的预测模型设计 10
4.4 预警信息生成与发布机制 10
4.5 系统集成与测试验证 11
结论 12
参考文献 13
致 谢 14
