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Distributed state estimation in digital distribution networks based on proximal atomic coordination

Liu, Zhelin, Gao, Shiyuan, Li, Peng, Ji, Haoran, Xi, Wei, Yu, Hao, Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 and Wang, Chengshan 2022. Distributed state estimation in digital distribution networks based on proximal atomic coordination. IEEE Transactions on Instrumentation and Measurement 71 , 9005311. 10.1109/TIM.2022.3193422

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Abstract

With the emerging digitalization technologies represented by edge computing, distribution networks are gradually transforming into digital distribution networks (DDNs). The realization of edge computing drives the distributed operation of DDNs, where multiple areas exchange boundary information through edge computing devices. Benefitting from the data acquisition and computing capacity of edge computing devices, it is feasible to perform accurate and real-time state estimation on the edge side. Aiming at the state perception with edge computing devices in DDNs, this article proposes a distributed state estimation (DSE) method based on the proximal atomic coordination (PAC) algorithm. First, based on convex relaxation optimization, the state estimation model is converted into a positive semidefinite programming (SDP) model to solve the nonconvexity caused by nonlinear measurements, which ensures the accuracy and convergence of state estimation. Then, a DSE method based on the PAC algorithm is proposed to exchange information of each area, which reduces the computation time and realizes the efficient state estimation on the edge side. The model and the effectiveness of the proposed method are numerically demonstrated on the modified PG&E 69-node system and the test case from a practical pilot in Guangzhou, China.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 0018-9456
Date of First Compliant Deposit: 5 October 2022
Date of Acceptance: 4 July 2022
Last Modified: 06 Nov 2023 18:34
URI: https://orca.cardiff.ac.uk/id/eprint/152357

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