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ORIGINAL RESEARCH article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1479478

Transient Voltage Stability Assessment and Margin Calculation Based on Disturbance Signal Energy Feature Learning

Provisionally accepted
Yan Chen Yan Chen 1Zirui Huang Zirui Huang 2Zhaobin Du Zhaobin Du 2*Guoduan Zhong Guoduan Zhong 2Jiawei Gao Jiawei Gao 2Hongyue Zhen Hongyue Zhen 1
  • 1 Electric Power Research Institute of China South Power Grid, Guangzhou, China
  • 2 South China University of Technology, Guangzhou, Guangdong Province, China

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

    With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages.Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

    Keywords: Transient voltage stability assessment, Wide area measurement system (WAMS), disturbance signal energy, ID3 decision tree algorithm, information gain, Support vector machine

    Received: 12 Aug 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Chen, Huang, Du, Zhong, Gao and Zhen. 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: Zhaobin Du, South China University of Technology, Guangzhou, 510 641, Guangdong Province, 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.