摘要
针对高压配电网发生单相接地故障时暂态工频电流分量利用不充分,提出一种基于改进麻雀搜索算法优化变分模态分解(SSA-VMD)和多尺度模糊熵的接地故障选线方法。首先,利用精英反向学习策略提高麻雀搜索算法的种群多样性,利用改进后的SSA对VMD进行迭代寻优,由实验数据可得,优化后的变分模态分解可准确区分各馈线暂态零序电流的工频分量。其次,计算各馈线零序电流工频分量的多尺度模糊熵值,并采用多尺度模糊熵偏均值作为选线判据,选出故障线路。经MATLAB/Simulink仿真结果表明,该方法在大多数故障条件下均可正确选线,可靠性高,具有较强的鲁棒性。
Aiming at the insufficient utilization of transient power frequency current components when single-phase grounding fault occurs in high voltage grounding system.To this end,a fault line selection for grounding fault based on improved SSA-VMD and MFE is proposed.First,the EOBL is used to improve the population diversity of the SSA,with the improved SSA to optimize VMD parameters,which can be obtained from the experimental data,the optimized variational modal decomposition can accurately distinguish the power frequency components of the transient zero-sequence current of each feeder.Secondly,the multi-scale fuzzy entropy value of the power frequency component of the zero-sequence current of each feeder is calculated,and PMMFE is used as line selection criterion then the mistake line is selected.MATLAB/Similink results show that this method can select the right line under most mistake conditions,with high reliability and strong robustness.
作者
陈博帆
孙岩洲
王彬
Chen Bofan;Sun Yanzhou;Wang Bin(Department of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China)
出处
《国外电子测量技术》
北大核心
2023年第1期28-34,共7页
Foreign Electronic Measurement Technology
基金
国家自然科学基金(U1804143)项目资助
关键词
故障选线
精英方向学习策略
麻雀搜索算法
变分模特分解
多尺度模糊熵
fault line selection
elite opposition-based learning
sparrow search algorithm
variational mode decomposition
multi-scale fuzzyentropy
