综合智慧能源2025，Vol.47Issue(9)：1-9,9.DOI:10.3969/j.issn.2097-0706.2025.09.001
电力系统高危N-k故障的高危断面辨识与保护配置
Critical section identification and protection configuration for high-risk N-k faults in power systems
摘要
Abstract
To address issues such as diverse N-k cascading fault scenarios,complex fault propagation paths,and difficulties in determining protection strategy implementation targets,a critical section identification and protection configuration model that integrates XGBoost with Bayesian hyperparameter optimization is proposed in power systems.By constructing a high-risk N-k fault set and randomly simulating cascading faults under load rates ranging from 0.1～10.0,a cascading fault dataset was developed using line load rates as inputs and residual load as the target.The Bayesian optimization algorithm was used to fine-tune the hyperparameters of the XGBoost model,selecting the optimal parameter combination.The protection resource allocation strategies for high-risk N-k fault scenarios were then identified.Simulation results on the IEEE 39-bus system showed that for 88%of high-risk N-k fault scenarios,adjusting the power flow carrying capacity of three critical section lines enabled the system's residual load to remain above 80%.关键词
连锁故障/断面辨识/贝叶斯超参数优化/数据驱动/XGBoost算法/机器学习Key words
cascading faults/section identification/Bayesian hyperparameter optimization/data-driven/XGBoost algorithm/machine learning分类
能源科技引用本文复制引用
黄子书,蔡晔,孙溶佐,谭玉东..电力系统高危N-k故障的高危断面辨识与保护配置[J].综合智慧能源,2025,47(9):1-9,9.基金项目
国家自然科学基金项目(52277076) （52277076）
长沙市杰出创新人才项目(kq2306011) （kq2306011）
湖南省研究生科研创新项目(CX20240783)National Natural Science Foundation(52277076) （CX20240783）
Outstanding Innovation Talent Project of Changsha(kq2306011) （kq2306011）
Hunan Province Graduate Student Research and Innovation Project(CX20240783) （CX20240783）
