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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1485369
This article is part of the Research Topic Data-Driven Approaches for Efficient Smart Grid Systems View all 13 articles

A Multi-task Learning Based Line Parameter Identification Method for Medium-voltage Distribution Network

Provisionally accepted
Xuebao Jiang Xuebao Jiang Chenbin Zhou Chenbin Zhou *Qi Pan Qi Pan *Liang Wang Liang Wang *Bowen Wu Bowen Wu *Yang Xu Yang Xu *Kang Chen Kang Chen *Liudi Fu Liudi Fu *
  • Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China

The final, formatted version of the article will be published soon.

    Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don't fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness.

    Keywords: line-parameter identification, Multi-task learning, Mixture of experts, Medium-voltage distribution system, Graph attention network

    Received: 23 Aug 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Jiang, Zhou, Pan, Wang, Wu, Xu, Chen and Fu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Chenbin Zhou, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Qi Pan, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Liang Wang, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Bowen Wu, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Yang Xu, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Kang Chen, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China
    Liudi Fu, Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Suzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.