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Regular Paper | Open Access

PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation

Huayi Wu1 , Zhao Xu2 ( ), Jiaqi Ruan3, Xianzhuo Sun3
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
Department of Electrical and Electronic Engineering, Shenzhen Research Institute, Research Institute of Smart Energy, The Hong Kong Polytechnic University, Hong Kong SAR, China
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
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Abstract

A centralized framework-based data-driven framework for active distribution system state estimation (DSSE) has been widely leveraged. However, it is challenged by potential data privacy breaches due to the aggregation of raw measurement data in a data center. A personalized federated learning-based DSSE method (PFL-DSSE) is proposed in a decentralized training framework for DSSE. Experimental validation confirms that PFL-DSSE can effectively and efficiently maintain data confidentiality and enhance estimation accuracy.

Keywords

Distribution system state estimationpersonalized federated learningprivacy protection

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 5,
September 2024
Pages 2265-2270
DOI: 10.17775/CSEEJPES.2023.08830

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Cite this article:
Wu H, Xu Z, Ruan J, et al. PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation. CSEE Journal of Power and Energy Systems, 2024, 10(5): 2265-2270. https://doi.org/10.17775/CSEEJPES.2023.08830

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Received: 03 November 2023
Revised: 28 December 2023
Accepted: 28 February 2024
Published: 24 July 2024
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).