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

Huayi Wu; Zhao Xu; Jiaqi Ruan; Xianzhuo Sun

doi:10.17775/CSEEJPES.2023.08830

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

Regular Papers | 更新时间：2026-10-15

- PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation
- CSEE Journal of Power and Energy Systems Vol. 10, Issue 5, Pages: 2265-2270(2024)
- 作者机构：
  
  1. Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University,Hong Kong SAR,China
  2. Department of Electrical and Electronic Engineering, Shenzhen Research Institute, Research Institute of Smart Energy, The Hong Kong Polytechnic University,Hong Kong SAR,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.08830
  CLC：
- Published：01 September 2024
- 稿件说明：
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Huayi Wu, Zhao Xu, Jiaqi Ruan, et al. PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation[J]. CSEE Journal of Power and Energy Systems, 2024, 10(5): 2265-2270.
DOI：

Huayi Wu, Zhao Xu, Jiaqi Ruan, et al. PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation[J]. CSEE Journal of Power and Energy Systems, 2024, 10(5): 2265-2270. DOI： 10.17775/CSEEJPES.2023.08830.

摘要

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.

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Keywords

references

6. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, Communication-efficient learning of deep networks from decentralized data. Presented at the Proceedings of the 20th International Conference on Artificial Intelligence and Statistics . [Online ] . Available: https://proceedings.mlr.press/v54/mcmahan17a.html.

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