基于MCS-SBL算法的配电网故障定位方法

Fault Location Method for Distribution Network Based on MCS-SBL Algorithm

配电网拓扑结构复杂,传统方法往往需要大量测点信息且难以实现快速有效的故障定位,本文提出基于少量测点信息的故障定位方法。首先,利用等效原理建立一个欠定的故障节点电压方程;其次,利用多重测量向量模型的贝叶斯压缩感知算法求解方程,根据重构稀疏电流矩阵的非零元素位置求解故障区域,实现故障定位;最后,在IEEE33节点配电系统上进行仿真实验,结果表明,所提方法仅需要少量测点的故障前后正序电压分量便可有效定位故障,计算速度较快,并且基本不受故障类型、过渡电阻的影响,同时适用于单故障和多重故障的场景,具有较强的抗噪能力。

Since the topology of distribution network is complex,the traditional methods usually need the information about a lot of measuring points,and it is difficult to realize a fast and effective fault location.In this paper,a fault location method based on the information about only a few measuring points is proposed.First,an underdetermined voltage equation of the fault node is established according to the equivalent principle.Second,the Bayesian compressed sensing algorithm for a multiple measurement vector model is used to solve the equation,and the position of non-zero elements in the reconstructed sparse current matrix is used to find the fault area,thus realizing the fault location.Finally,a simulation experiment was conducted on an IEEE 33-node distribution system,and results show that the proposed method can effectively locate the fault based on the positive-sequence voltage components of only a few measuring points before and after the fault,and it has a fast calculation speed.In addition,this method is basically unaffected by fault types or transition resistance,and it is suitable for single-and multiple-fault scenarios,with a strong anti-noise capability.