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基于雪消融优化算法的交直流混联电网连锁故障风险评估

王英英 李勇 李会新 陈磊 李逸飞 陈红坤

电力系统保护与控制2025,Vol.53Issue(4):37-47,11.
电力系统保护与控制2025,Vol.53Issue(4):37-47,11.DOI:10.19783/j.cnki.pspc.246173

基于雪消融优化算法的交直流混联电网连锁故障风险评估

Snow ablation-based risk assessment method for cascading failures in an AC-DC hybrid power grid

王英英 1李勇 1李会新 1陈磊 2李逸飞 2陈红坤2

作者信息

  • 1. 国家电网有限公司华中分部,湖北 武汉 430077
  • 2. 武汉大学电气与自动化学院,湖北 武汉 430072
  • 折叠

摘要

Abstract

Aiming at the risk assessment problem of cascading failures in AC-DC hybrid power grid at different stages of evolution,this paper proposes a cascading failure risk assessment method based on the snow ablation optimization(SAO)algorithm.First,the dominant factors influencing cascading failures in the initial,development and deterioration stages of an AC-DC hybrid power grid are described,and a two-level index system reflecting the evolutionary characteristics of cascading failures is established.Then,a combination weighting strategy for risk assessment indices based on the SAO algorithm is formulated,and the risk assessment process for cascading failures is determined.Finally,a simulation model of an AC-DC hybrid power grid based on an improved IEEE 30-bus system is constructed using the Matlab/Simulink platform.The differences in the weights of the indices between the proposed method and the traditional method are compared,and the effects of different levels of DC converter station blocking on the cascading failures are revealed.The results show that the proposed method can accurately reflect the dominant factors at different stages of evolution of cascading failures of the AC-DC hybrid power grid,and the assessment sensitivity of the high-risk lines can be improved by 12.67%compared with the traditional method,verifying the reasonableness and effectiveness of the proposed method.

关键词

交直流混联电网/连锁故障/雪消融优化算法/风险评估/组合赋权策略

Key words

AC-DC hybrid power grid/cascading failure/snow ablation optimization(SAO)algorithm/risk assessment/combination weighting strategy

引用本文复制引用

王英英,李勇,李会新,陈磊,李逸飞,陈红坤..基于雪消融优化算法的交直流混联电网连锁故障风险评估[J].电力系统保护与控制,2025,53(4):37-47,11.

基金项目

This work is supported by the Science and Technology Project of State Grid Corporation of China(No.52140023000M). 国家电网有限公司科技项目资助(52140023000M) (No.52140023000M)

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