Graph Computing-Based WLS Fast Decoupled State Estimation

• Published in: IEEE transactions on smart grid Vol. 11; no. 3; pp. 2440 - 2451
• Main Authors: Yuan, Chen, Zhou, Yuqi, Liu, Guangyi, Dai, Renchang, Lu, Yi, Wang, Zhiwei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Energy management; Graph database; Graphical representations; Jacobian matrices; Matrix decomposition; Modernization; parallel computing; Power systems; State estimation; Storage units; Topology; weighted least square (WLS)
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2019.2955695

Given the fast pace of grid modernization, system states are changing more frequently and rapidly with the high penetration of renewable energy, responsive loads and power electronics interface. To properly monitor the dynamic change of system states and to further improve system operation reliability and robustness, a fast state estimator is required. This paper presents a graph computing-based state estimation. The feasibility of power system graph modeling is first demonstrated. The power system is naturally represented by a graph, in which its nodes serve as both storage units and logic units. Second, a graph computing technique for power system state estimation is presented. The system-level <inline-formula> <tex-math notation="LaTeX">{H} </tex-math></inline-formula> matrix and gain matrix are decomposed into locally formulated node-based matrices, and these node-based matrices are compressed to improve computational complexity. In addition, with graph topology analysis, the efficiency of the system-level gain matrix formulation and storage are further improved. The testing results of IEEE 14-bus system, IEEE 118-bus system, two European systems from MATPOWER, a provincial system in China, an MP-10790 system and an extended IEEE 118-bus*120 system demonstrate the high efficiency of the proposed approach without compromising the accuracy. Its advantages for high-performance computation are further illustrated by comparing it against a commercial EMS.