PFL-DSSE：用于配电系统状态估计的个性化联合学习方法

IF 6.9 2区工程技术 Q2 ENERGY & FUELS

CSEE Journal of Power and Energy Systems Pub Date : 2024-07-24 DOI:10.17775/CSEEJPES.2023.08830

Huayi Wu;Zhao Xu;Jiaqi Ruan;Xianzhuo Sun

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引用次数: 0

摘要

基于集中式框架的数据驱动型主动配电系统状态估算（DSSE）框架已得到广泛应用。然而，由于原始测量数据聚集在数据中心，它面临着潜在的数据隐私泄露挑战。在 DSSE 的分散训练框架中，提出了一种基于联合学习的个性化 DSSE 方法（PFL-DSSE）。实验验证证实，PFL-DSSE 能有效、高效地维护数据机密性并提高估计精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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来源期刊

CSEE Journal of Power and Energy Systems Energy-Energy (all)

CiteScore

11.80

自引率

12.70%

发文量

389

审稿时长

26 weeks

期刊介绍： The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.