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作 者:王彬[1] 赵来军[1] 宋晓燕 杨明[1] 孙岩洲[1] Wang Bin;Zhao Laijun;Song Xiaoyan;Yang Ming;Sun Yanzhou(Henan Polytechnic University,Jiaozuo,Henan 454000,China;Electric Power Research Institute of Henan Electric Power Company,Zhengzhou 450052,China)
机构地区:[1]河南理工大学,河南焦作454000 [2]河南省电力公司电力科学研究院,郑州450052
出 处:《机电工程技术》2023年第11期240-245,共6页Mechanical & Electrical Engineering Technology
基 金:国家自然科学基金资助项目(U1804143)。
摘 要:利用高低频暂态信息对谐振接地系统单相接地故障选线的方法存在提取暂态信息困难、对暂态信号利用不充分、随着接地电阻增大判据阈值模糊等问题。针对提取暂态信息困难,利用了麻雀搜索算法对变分模态分解(VMD)关键参数进行优化,优化后的VMD能够准确分解线路零序电流;针对能量法中因有功分量较小,对暂态信号利用不充分,准确性不高的问题,根据低频衰减分量相似度和高频零序电荷与电压的关系,构成双重判据;针对判据阈值模糊问题,利用化后的支持向量机(SVM)分类器对判据特征量进行分类,实现选线。通过模拟不同接地电阻、不同故障合闸角3种情况下的单相接地故障仿真和现场数据验证验证了该方法可行性,通过和其他选线方法的对比,该选线方案准确率高达99.45%,优于其他方法,且选线准确度受噪声、故障合闸角等因素影响十分微弱。There are many problems in line selection of single-phase grounding fault in resonant grounding system,such as difficulty in fault feature extraction and inaccurate line selection of small fault closing Angle,etc.To solve these problems,a line selection method based on variational mode decomposition(VMD)and support vector machine(SVM)after parameter optimization is presented.Firstly,the sparrow search algorithm is used to optimize the key parameters of VMD and SVM.The optimized VMD decomposed the line zero sequence current,and formed a double criterion according to the relationship between low frequency attenuation component similarity and high frequency zero sequence charge and voltage.Then,the optimized SVM classifier is used to classify the criterion features and realize line selection.The feasibility of the proposed method is verified by simulation of a single-phase grounding fault under three conditions,such as different grounding resistance and different fault closing angle,and field data verification.By comparison with other route selection methods,the accuracy of the proposed route selection scheme is as high as 99.45%,which is superior to other methods,and the accuracy of line selection is very little affected by noise,fault closing Angle,and other factors.
关 键 词:谐振接地系统 故障选线 变分模态分解 麻雀搜索算法 支持向量机
分 类 号:TM743[电气工程—电力系统及自动化]
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