摘 要
随着能源转型和智能化技术的快速发展，电力系统运行面临着日益复杂的挑战，传统优化方法已难以满足现代电网对高效性、稳定性和经济性的需求。为此，本研究基于大数据分析技术，探索其在电力系统运行优化中的应用潜力，旨在提升系统的整体性能并实现资源的最优配置。研究通过构建多源数据融合框架，整合发电、输电、配电及用电环节的数据流，并采用机器学习算法对电力负荷预测、故障诊断以及调度优化等关键问题进行建模与求解。实验结果表明，所提出的方法能够显著提高负荷预测精度，降低系统运行成本，并增强对突发事件的响应能力。此外，本研究创新性地引入了实时数据驱动的动态优化策略，突破了静态模型在适应复杂工况方面的局限性，为电力系统的智能化升级提供了理论支持和技术路径。总体而言，该研究不仅验证了大数据分析在电力系统优化中的有效性，还为未来相关领域的研究奠定了坚实基础。
关键词：电力系统优化；大数据分析；机器学习；负荷预测；动态优化策略
Abstract
With the rapid development of energy transition and intelligent technologies, the operation of power systems is facing increasingly complex challenges. Traditional optimization methods are no longer sufficient to meet the modern grid's demands for efficiency, stability, and economy. To address this issue, this study explores the application potential of big data analytics in power system operation optimization, aiming to enhance the overall performance of the system and achieve optimal resource allocation. By constructing a multi-source data fusion fr amework, the study integrates data streams from generation, transmission, distribution, and consumption phases and employs machine learning algorithms to model and solve key problems such as load forecasting, fault diagnosis, and dispatch optimization. The experimental results demonstrate that the proposed method significantly improves load forecasting accuracy, reduces system operational costs, and enhances responsiveness to emergencies. Furthermore, this research innovatively introduces a real-time data-driven dynamic optimization strategy, overcoming the limitations of static models in adapting to complex operating conditions and providing theoretical support and technical pathways for the intelligent upgrading of power systems. Overall, this study not only validates the effectiveness of big data analytics in power system optimization but also lays a solid foundation for future research in related fields.
Keywords: Power System Optimization；Big Data Analysis；Machine Learning；Load Forecasting；Dynamic Optimization Strategy
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电力系统运行优化的研究背景 1
（二）大数据分析在电力系统中的意义 1
（三）国内外研究现状与发展趋势 1
（四）本文研究方法与技术路线 2
二、大数据分析技术在电力系统中的应用 2
（一）数据采集与预处理的关键技术 2
（二）数据挖掘算法的选择与优化 3
（三）实时数据流分析的实现路径 3
三、电力系统运行优化模型构建 4
（一）基于大数据的负荷预测模型 4
（二）优化调度模型的设计与验证 5
（三）故障诊断与恢复策略分析 5
四、案例分析与优化效果评估 6
（一）实际电力系统的数据案例分析 6
（二）优化方案的实施与调整策略 6
（三）优化效果的量化评估与反馈 7
结 论 8
致 谢 9
参考文献 10
