Topology change aware distributed state estimation based on unsupervised bipartite graph-enabled causality-inspired sparse learning

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Lin, Zhiping and Hu, Weihao and Cao, Di and Zhao, Pengfei and Abulanwar, Sayed and Huang, Qi and Chen, Zhe (2026) Topology change aware distributed state estimation based on unsupervised bipartite graph-enabled causality-inspired sparse learning. IEEE Transactions on Industrial Informatics. pp. 1-12. ISSN 1551-3203 (https://doi.org/10.1109/TII.2025.3642943)

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Abstract

Topology changes in a distribution network are common due to planned reconfigurations and unintentional switching events during practical operations. Topology changes make it challenging for existing optimization- and learning-based distributed system state estimation methods to maintain accuracy. This difficulty arises from the lack of accurate structural information for the new topology and the absence of labeled data (recorded state variables) for model retraining. To this end, this article proposes an unsupervised-on-target learning-based state estimation method for the distribution network after topology changes without relying on the topology information and labeled data. In particular, a bipartite graph learning (BGL) method with rank constraints is first designed to learn the representation of each topology with a restricted set of measurements. Then, the Euclidean distance is employed to select the best-matched source domain historical topology according to the representation learned by the BGL. To extract invariant causal structures across the two topologies, a causality-inspired sparse structure learning for domain adaptation network is further designed. It relaxes the correlations between the selected historical and new topologies into an associative structure, represented by attention scores derived from the proposed inter- and intravariable attention networks. This allows the leverage of the causality to enhance the state estimation performance of the distribution network after topology changes without relying on accurate topology information and recorded labels used for training. The comparison results on two standard IEEE test systems validate the efficacy of the proposed method.

ORCID iDs

Lin, Zhiping, Hu, Weihao, Cao, Di, Zhao, Pengfei, Abulanwar, Sayed ORCID logoORCID: https://orcid.org/0000-0002-3396-4020, Huang, Qi and Chen, Zhe;
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