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基于振荡模态辨识的负阻尼振荡故障源定位方法

刘芳 彭宇恒 刘乾易 林思思 李勇 刘康志

电力系统保护与控制2025,Vol.53Issue(9):60-69,10.
电力系统保护与控制2025,Vol.53Issue(9):60-69,10.DOI:10.19783/j.cnki.pspc.240503

基于振荡模态辨识的负阻尼振荡故障源定位方法

Fault source location method for negative damping oscillations based on oscillation mode identification

刘芳 1彭宇恒 1刘乾易 1林思思 1李勇 2刘康志3

作者信息

  • 1. 中南大学自动化学院,湖南长沙 410083
  • 2. 湖南大学电气与信息工程学院,湖南长沙 410082
  • 3. 千叶大学电气与电子工程系,日本千叶263-8522
  • 折叠

摘要

Abstract

Large-scale,multi-regional,highly coupled interconnected power systems are increasingly facing challenges such as strong perturbation responses,nonlinear time-varying behavior,and complex oscillation modes.To extract oscillation information under fault conditions from the massive data accumulated by wide-area measurement systems(WAMS)and to achieve fault source localization,a negative damping oscillation fault source location method based on oscillation mode identification is proposed.First,a data-driven mode identification(DDMI)method is proposed to identify modal parameters of low-frequency oscillations with negative damping,thereby filtering out the dominant modal signals.Next,based on damping torque analysis,the calculated formula of generator oscillation energy in different oscillation modes is defined.Furthermore,the oscillation signals for different modes are reproduced using the identified modal parameters to compute the dominant mode oscillation energy,which enables the localization of the oscillation-causing generator.Finally,simulations are conducted on a New England power system model with wind power integration to verify the effectiveness of the DDMI method.The results show that the proposed localization method can accurately locate the fault source under multi-machine fault scenarios.

关键词

振荡模态辨识/振荡定位/阻尼转矩分析/振荡能量

Key words

oscillation mode identification/oscillation localization/damping torque analysis/oscillation energy

引用本文复制引用

刘芳,彭宇恒,刘乾易,林思思,李勇,刘康志..基于振荡模态辨识的负阻尼振荡故障源定位方法[J].电力系统保护与控制,2025,53(9):60-69,10.

基金项目

国家自然科学基金项目资助(62373373) (62373373)

湖南省自然科学基金重点项目资助(2024JJ3033) (2024JJ3033)

湖南省科技创新计划项目资助(2022RC3051)This work is supported by the National Natural Science Foundation of China(No.62373373). (2022RC3051)

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