Gradient-Based Multi-Area Distribution System State Estimation

作     者：Zhou, Xinyang Liu, Zhiyuan Guo, Yi Zhao, Changhong Huang, Jianqiao Chen, Lijun 

作者机构：Natl Renewable Energy Lab Power Syst Engn Dept Golden CO 80401 USA Univ Colorado Dept Comp Sci Boulder CO 80309 USA Univ Texas Richardson Dept Mech Engn Richardson TX 75080 USA Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China IIT Dept Elect & Comp Engn Chicago IL 60616 USA 

出 版 物：《IEEE TRANSACTIONS ON SMART GRID》 (IEEE智能电网汇刊)

年 卷 期：2020年第11卷第6期

页      面：5325-5338页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Renewable Energy Laboratory U.S. Department of Energy [DE-EE-0007998] U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office 

主　　题：State estimation Voltage measurement Reliability Distribution networks Monitoring Real-time systems Distribution system state estimation multi-area state estimation distributed algorithm multi-phase system large system simulation 

摘      要：The increasing distributed and renewable energy resources and controllable devices in distribution systems make fast distribution system state estimation (DSSE) crucial in system monitoring and control. We consider a large multi-phase distribution system and formulate DSSE as a weighted least squares (WLS) problem. We divide the large distribution system into smaller areas of subtree structure, and by jointly exploring the linearized power flow model and the network topology, we propose a gradient-based multi-area algorithm to exactly and efficiently solve the WLS problem. The proposed algorithm enables distributed and parallel computation of the state estimation problem without compromising any performance. Numerical results on a 4,521-node test feeder show that the designed algorithm features fast convergence and accurate estimation results. Comparison with traditional Gauss-Newton method shows that the proposed method has much better performance in distribution systems with a limited amount of reliable measurement. The real-time implementation of the algorithm tracks time-varying system states with high accuracy.