面向在线动态安全分析的高时空分辨率状态估计:研究现状与方法框架OA

Real-time acquisition of system operating states with high temporal and spatial resolution is fundamental to achieving online dynamic security assessment(DSA)for modern power systems.However,traditional state estimation(SE)research faces challenges in data availability,such as insufficient spatiotemporal resolution of heterogeneous measurements.Additionally,methodological limitations persist,including the constraints of single-mechanism models,the difficulty of balancing timeliness and reliability in data-driven approaches,and the challenge of analytically constructing system dynamic models.To address these issues,this paper first reviews the previous research in state estimation and summarizes the key challenges in achieving high-resolution estimation.Then,based on the principle of"physics-based and data-driven combination",a high spatial-temporal resolution SE framework is designed for dynamic security analysis in modern power systems.The paper proposes techniques for constructing,training,improving,and applying high-resolution SE models,considering the specific characteristics of different classical hybrid-driven models.Finally,case studies validate the effectiveness of the proposed high spatiotemporal resolution state estimation techniques.