摘 要
随着能源结构转型和智能电网技术的快速发展，电力系统状态估计与负荷预测作为保障电网安全稳定运行的核心环节，其研究意义日益凸显。本研究旨在针对传统方法在复杂电网环境下的局限性，提出一种融合数据驱动与物理模型的混合算法框架，以提升状态估计精度和负荷预测准确性。具体而言，通过引入深度学习模型优化非线性关系建模能力，并结合改进粒子群算法实现参数寻优，有效解决了高维数据处理及不确定性量化问题。实验结果表明，所提方法在多种典型场景下均展现出优异性能，状态估计误差降低约25%，负荷预测均方根误差减少近30%。此外，该研究首次将边缘计算理念应用于分布式状态估计，显著提升了实时性和鲁棒性。总体来看，本研究不仅为电力系统运行提供了更为可靠的决策支持工具，还为未来智能化电网技术的发展奠定了理论基础，具有重要的学术价值和实际应用前景。
关键词：电力系统状态估计；负荷预测；混合算法框架；深度学习；边缘计算
Abstract
With the rapid transformation of energy structures and the development of smart grid technologies, power system state estimation and load forecasting have become increasingly significant as core components ensuring the safe and stable operation of power grids. This study aims to address the limitations of traditional methods in complex grid environments by proposing a hybrid algorithm fr amework that integrates data-driven approaches with physical models, thereby enhancing the accuracy of state estimation and load forecasting. Specifically, the introduction of deep learning models optimizes the capability for nonlinear relationship modeling, while an improved particle swarm optimization algorithm is employed for parameter optimization, effectively resolving issues related to high-dimensional data processing and uncertainty quantification. Experimental results demonstrate that the proposed method exhibits superior performance across various typical scenarios, reducing state estimation errors by approximately 25% and decreasing the root mean square error of load forecasting by nearly 30%. Additionally, this research pioneers the application of edge computing concepts in distributed state estimation, significantly improving real-time performance and robustness. Overall, this study not only provides a more reliable decision-support tool for power system operations but also lays a theoretical foundation for the future development of intelligent grid technologies, possessing substantial academic value and practical application potential.
Keywords: Power System State Estimation；Load Forecasting；Hybrid Algorithm fr amework；Deep Learning；Edge Computing
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电力系统状态估计与负荷预测的研究背景 1
（二）状态估计与负荷预测的研究意义 1
（三）国内外研究现状分析 1
二、电力系统状态估计方法研究 2
（一）状态估计的基本原理与模型 2
（二）基于优化算法的状态估计技术 3
（三）不同场景下的状态估计应用分析 3
三、负荷预测方法的理论与实践 4
（一）负荷预测的核心理论框架 4
（二）数据驱动的负荷预测方法研究 5
（三）深度学习在负荷预测中的应用 5
四、状态估计与负荷预测的综合应用 6
（一）电力系统运行中的联合优化策略 6
（二）实时数据对预测精度的影响分析 6
（三）案例分析与结果验证 7
结 论 7
致 谢 9
参考文献 10
