基于参数优化VMD和能量相似度的配电网故障区段定位方法

针对配电网发生单相接地故障时故障特征微弱且易受外界噪声干扰的问题,提出了一种基于参数优化变分模态分解(VMD)与能量相对熵相结合的故障区段定位方法。首先通过鲸鱼优化算法优化后的VMD分解故障零序电流得到若干个反映局部信号特征的本征模态分量(IMF),进而求取各IMF分量的Hilbert边际谱,选取能量最大的分量作为故障零序电流的暂态主频分量,利用能量相对熵衡量相邻检测点暂态主频分量的能量差异,计算比较,确定熵值最大的区段即为故障区段。仿真结果表明,本方法不受故障位置、故障初相角等不同故障条件的影响,且在有噪声干扰时仍可实现高正确率故障区段定位。

Fault section location in distribution network based on parameter optimization VMD and energy similarity

Aiming at the weak fault characteristics and susceptibility to external noise when a sing-phase ground occurs in the distribution network, a fault section location method based on the combination of parameter optimization optimization variational mode decomposition(VMD) and energy relative entropy is proposed. Firstly, the fault zero-sequence current is decomposed by the VMD optimized by the whale optimization algorithm, and several intrinsic mode functions(IMF) reflecting the local signal characteristics are obtained. Then, the Hilbert marginal spectrum of each IMF is obtained, the transient main frequency component of the fault zero sequence current is selected as the largest energy component, the energy difference between the adjacent detection points is represented by energy relative entroy. Through calculation and comparison, the section of maximum entropy is fault section. The simulation results show that the method is not affected by different fault conditions such as fault location, fault initial phase angle, etc, and in the presence of noise interference, high accuracy fault section location can still be achieve.