基于拐点密集区凹凸波动特性的直流配网故障检测方法

A DC distribution network fault detection method based on bump fluctuation at a dense inflection point

基于模块化多电平换流器(modular multilevel converter,MMC)的柔性直流配电网具有不存在换相失败等优点。但换流器存在低惯性、弱阻尼等特性,导致故障电流上升速度快且峰值高,对系统危害极大。针对直流配电网发生单极接地故障难以准确选择故障馈线的问题,提出了一种基于拐点密集区凹凸波动特性的直流配网故障检测方法。首先,利用变分模态分解算法对各馈线零模电流进行分解,选取特征分量。然后,计算特征分量的二阶导数,选择拐点密集区并进行归一化处理,得到各馈线的凹凸波动性。最后,判定凹凸波动性与其他馈线相异的线路为故障线路。仿真结果表明,所提方法能够快速识别故障馈线,且受过渡电阻、采样频率、数据窗和噪声等因素影响小。

Flexible DC distribution networks based on a modular multilevel converter(MMC)have the advantage of no commutation failure.However,because of low inertia,weak damping and other characteristics of the converter,the fault current rises quickly and has a high peak,which is extremely harmful to the system.It is difficult to accurately select the fault feeder when a single-pole ground fault occurs in the DC distribution network.Thus a fault detection method for a DC distribution network based on the fluctuation characteristics of concavity and convexity(FCCC)of the inflection point dense area(IPDA)is proposed.First,a variational mode decomposition algorithm is used to decompose the zero-mode current of each feeder and select the feature components.Then,the second derivative of the feature component is calculated,the IPDA is selected,and the normalization process is carried out to obtain the FCCC of each feeder.Finally,the feeders that are different from the other feeders of the FCCC are determined to be faulty feeders.Simulation results show that the proposed method can quickly identify faulty feeders,and is less affected by factors such as transition resistance,sampling frequency,data window and noise.