南方电网技术2026，Vol.20Issue(3)：8-18,31,12.DOI:10.13648/j.cnki.issn1674-0629.2026.03.002
基于BWO优化VMD和KELM的柔性直流输电线路短路故障定位方法
MMC-HVDC Transmission Line Short-Circuit Fault Location Method Based on BWO Optimized VMD and KELM
摘要
Abstract
Aiming at the lack of accuracy of traveling wave head calibration and the performance of intelligent location model affected by parameters,a short-circuit fault location method based on the beluga whale algorithm(BWO)is proposed to optimize variable mode decomposition(VMD)and kernel extreme learning machine(KELM)for MMC-HVDC transmission lines.Firstly,the BWO is used to optimize the parameters of VMD,combined with wavelet soft threshold denoising method for noise reduction and decomposi-tion of the collected fault signals.Then the arrival moment of the initial traveling wave is calibrated by combining the Hilbert transform(HT).Next,the arrival moments of traveling waves are used as eigenvalues to construct the feature dataset.The KELM localization model is optimized using BWO.Finally,the dataset is substituted into the optimized localization model to achieve fault localization.The results show that the localization model of the method fits 99.4%with high localization accuracy and good robust-ness.The proposed method is highly tolerant to noise and transition resistance,and the localization error is within 500m.关键词
柔性直流输电线路/变分模态分解/白鲸算法/核极限学习机/故障定位Key words
MMC-HVDC transmission lines/variable mode decomposition(VMD)/beluga whale algorithm(BWO)/kernel extreme learning machine(KELM)/fault location分类
信息技术与安全科学引用本文复制引用
赵岩,王梓毅,徐天..基于BWO优化VMD和KELM的柔性直流输电线路短路故障定位方法[J].南方电网技术,2026,20(3):8-18,31,12.基金项目
国家自然科学基金资助项目(51677057) （51677057）
黑龙江省省属高等学校基本科研业务费项目(2025-KYYWF-ZR0606). Supported by the National Natural Science Foundation of China(51677057) （2025-KYYWF-ZR0606）
the Basic Scientific Research Project of Heilongjiang Provincial Colleges and Universities(2025-KYYWF-ZR0606). （2025-KYYWF-ZR0606）
