Robust pseudo-measurement modeling for three-phase distribution systems state estimation

• Published in: Electric power systems research Vol. 180; p. 106138
• Main Authors: Cao, Zhiyuan, Wang, Yubo, Chu, Chi-Cheng, Gadh, Rajit
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2020; Elsevier Science Ltd
• Subjects: Computer simulation; Distributed generation; Electricity distribution; Energy sources; Estimating techniques; Feeders; Gradient boosting tree method; Iterative methods; Measurement; Metering; Phase distribution; Pseudo-measurement modeling; Robustness (mathematics); Simulation; State estimation; Three-phase distribution system state estimation; Pseudo-measurement modeling; Gradient boosting tree method; Three-phase distribution system state estimation
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2019.106138

•A three-phase distribution system state estimation framework that relies on pseudo-measurement modeling.•Usage of end-user data improves performance of pseudo-measurement model and state estimation.•2 years of real-world data from multiple sources are used to show practicality of the system.•Performance was investigated using IEEE 13- and 123-bus benchmark systems. Trending integration of distributed energy resources calls for state estimation at the distribution level for providing reliable power systems information. In contrast to transmission systems, distribution systems are sparsely monitored, and consequently difficult to estimate states. To address measurement scarcity problem in distribution systems, this paper proposes a distribution system state estimation framework that relies on robust pseudo-measurement modeling. User-level metering data is used to train gradient boosting tree models for generating pseudo-measurements. A ladder iterative state estimator is then applied on the pseudo-measurements to solve for system states. Simulation studies are performed on the IEEE 13-bus and 123-bus test feeders. Numerical results demonstrate that the proposed state estimation scheme outperform two benchmark approaches in terms of accuracy (error), consistency (error variance) and robustness (high accuracy subject to load changes).