Randomly switched subsystem-based state estimation for active distribution networks with unobservable conditions

Gao, Shiyuan, Yu, Hao, Li, Peng, Ji, Haoran, Yu, Jiancheng, Zhao, Jinli, Song, Guanyu and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2025. Randomly switched subsystem-based state estimation for active distribution networks with unobservable conditions. IEEE Transactions on Instrumentation and Measurement 74 , 9005514. 10.1109/TIM.2025.3572160

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Abstract

With the high penetration of distributed generators (DGs), state estimation is essential for fast and accurate operational status tracking of active distribution networks (ADNs). However, there are generally unobservable areas in ADNs due to the paucity of measurements. Traditional state estimation methods may encounter difficulty coping with limited measurements. Thus, aiming at the accurate and fast state perception of unobservable ADNs, this article proposes a randomly switched subsystem (RSS)-based state estimation method. First, the unobservable ADN is modeled as the randomly switched ADN (RSADN). Stochastic observability is defined to distinguish the unobservable conditions, which ensures the feasibility of state estimation. Furthermore, considering different operational conditions, the observability enhancement mechanism is designed under limited measurement conditions to make the network observable and reduce the impact of topology switching on the system. Then, the improved unscented Kalman filter (UKF) algorithm is utilized to solve the state estimation problem based on state transformation. The results of case studies confirm that the proposed method can achieve more accurate estimation results compared to other methods under unobservable conditions and exhibit satisfactory computational speed in large-scale systems.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Schools > Engineering
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	0018-9456
Date of First Compliant Deposit:	9 June 2025
Date of Acceptance:	12 May 2025
Last Modified:	19 Jun 2025 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/178908

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