中国电机工程学报2025，Vol.45Issue(7)：2540-2553,中插8,15.DOI:10.13334/j.0258-8013.pcsee.232298
计及连锁故障传播路径的电力系统N-k多阶段双层优化及故障场景筛选模型
N-k Multi-stage Bi-level Optimization and Fault Scenario Screening Model of Power System Considering Cascading Failure Propagation Path
摘要
Abstract
The current power system directly screens for the most hazardous k-level predicted accident scenarios through classical N-k security analysis,neglecting the dynamic propagation process of faults caused by source faults,leading to conservative N-k security analysis results.Therefore,this paper focuses on characterizing the changes in system topology scale and connection relationships during fault propagation,and establishes an N-k multi-stage bi-level and fault scenario screening model considering cascading failure propagation path.The upper layer aims at the maximum load loss,screening k-level source faults,and identifying its failure propagation paths.The lower layer,aiming at the minimum load loss,simulates the process of suppressing the cascading propagation of faults by adjusting generator output and load size.The dual variables are introduced to transform the bi-level model into a single-layer model as a solution.However,the multiplication of dual variables will produce nonlinear terms,resulting in slow convergence speed.Therefore,this paper proposes a two-stage network flow algorithm,which can obtain upper and lower bounds of the dual variables,thereby eliminating the nonlinear terms and reducing the model feasible region,and then solves the proposed model quickly.Finally,the simulation results verify that the method can effectively identify high-risk N-k source fault scenarios and multi-stage fault propagation paths,and the solving speed is increased by 12 times.关键词
连锁故障/网络流模型/多阶段双层优化模型/对偶理论Key words
casding failure/network flow model/multi-stage bi-level optimization model/dual theory分类
动力与电气工程引用本文复制引用
蔡晔,孙溶佐,王炜宇,曹一家..计及连锁故障传播路径的电力系统N-k多阶段双层优化及故障场景筛选模型[J].中国电机工程学报,2025,45(7):2540-2553,中插8,15.基金项目
国家自然科学基金项目(52277076) （52277076）
国家自然科学基金联合基金项目(U1966207).Project Supported by National Natural Science Foundation of China(52277076) （U1966207）
The Joint Funds of National Natural Science Foundation of China(U1966207). （U1966207）
