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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1419830

Refined identification of key parameters of power system synthesis load model based on improved butterfly algorithm

Provisionally accepted
Zongyao Wang Zongyao Wang Gaoyang Yan Gaoyang Yan *Yi Rong Yi Rong Han Wang Han Wang
  • Nanchang Institute of Technology, Nanchang, China

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

    With the improvement of power grid simulation accuracy requirements, the existing typical load model parameters can no longer meet the accuracy requirements and become the short board that restricts the stable operation of power system. This paper mainly proposes an improved butterfly optimization algorithm based on population optimization and dynamic strategy (PODSBOA) for commonly used synthesis load model (SLM) parameters to realize the refined and personalized identification of SLM key parameters:The results show that, in the 2s load data experiment, the identification error is 0.02, the identification accuracy is 4.09, and the convergence time of PODSBOA is 12.048s. In the 5s load data experiment, the identification error is 0.013, the identification accuracy is 6.65, and the convergence time of PODSBOA is 23.405s, the identification errors in the two sets of experiments are reduced by 0.02023-0.06443 compared with other algorithms. The comparison results of different load model parameter identification algorithms show that the improved PODSBOA proposed in this paper has high recognition accuracy and fast convergence speed, and solves the problem of low accuracy and instability of the identification results of the existing identification schemes.

    Keywords: grid simulation accuracy, improved butterfly algorithm, Synthesis load model, Key parameters, refined identification

    Received: 19 Apr 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Wang, Yan, Rong and Wang. 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: Gaoyang Yan, Nanchang Institute of Technology, Nanchang, 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.