摘要
针对直流微电网故障信息少、低阻故障下电流上升快而诊断速度要求高的难题,提出一种基于小波滑窗能量和支持向量机的故障诊断方法。该方法在采集本地电流进行小波分解的基础上,计算各级小波系数的相对滑窗能量,从而构造出维数低、区分度高的故障特征向量,结合多分类支持向量机可以实现短路故障、接地故障和正常运行工况的快速准确诊断。基于MATLAB/Simulink的仿真测试结果表明,所提故障诊断方法能够快速准确地识别直流微电网的短路故障和接地故障。
Aiming at the problem of less fault information and fast current rise and high diagnostic speed requirements under low-impedance faults,a fault diagnosis method based on relative wavelet energy with sliding window and support vector machine is proposed.Based on the acquisition of local current for wavelet decomposition,the proposed method calculates the relative sliding window energy of wavelet coefficients at different levels,so as to construct fault characteristic vectors with low dimensionality and high sensitivity.Combined with multi-classification support vector machine,it can realize rapid and accurate diagnosis of short circuit faults,ground faults and normal working conditions.Simulation tests results based on MATLAB/Simulink show that the proposed fault diagnosis scheme can quickly and accurately identify short circuit faults and ground faults of DC microgrids.
作者
石曼伶
罗珍珍
郑鑫龙
粟梅
SHI Manling;LUO Zhenzhen;ZHENG Xinlong;SU Mei(State Grid Zhuzhou Power Supply Company,Zhuzhou 412000,China;State Grid Ningxiang Power Supply Company,Ningxiang 410600,China;School of Automation,Central South University,Changsha 410083,China)
出处
《湖南电力》
2023年第5期49-54,共6页
Hunan Electric Power
基金
国家自然科学基金项目(52177205)
国家自然科学基金项目(61933011)。
关键词
直流微电网
故障诊断
小波变换
支持向量机
DC microgrid
fault diagnosis
wavelet transform
support vector machine
