基于网格指纹匹配的光伏阵列电弧故障定位方法

Photovoltaic Array Arc Faults Location Method Based on Grid Fingerprint Matching

考虑到传统的基于电磁辐射(electromagneticradiation,EMR)信号的光伏阵列电弧故障定位方法存在采样条件严苛、定位精度低等问题,提出一种基于网格指纹匹配的电弧故障定位新方法。首先,使用低采样率获取电弧EMR信号,并提取其均方根值作为代表EMR强度的特征指标。然后,利用BP神经网络(backpropagationneural network,BPNN)挖掘辐照度、信号接收距离与电弧EMR信号强度的内在联系,建立预测模型。接着,根据BPNN输出的双天线阵列与电弧间的预测距离,利用三角定位法初步求得电弧所在区域。最后,网格化划分电弧所在区域的光伏组件,生成网格指纹信息,并将预测距离与指纹信息最匹配的网格的中心坐标作为电弧发生位置的最终预测坐标。实验结果表明,所提算法具备良好的定位能力与适应性,对电弧故障定位的平均绝对误差为0.306m,在定位精度与经济性上均优于EMR衰减模型定位法。

The problems of strict sampling conditions and low positioning accuracy exist in the traditional photovoltaic array arc fault location method based on electromagnetic radiation(EMR)signal,accordingly,we propose a new arc fault location method based on grid fingerprint matching.Firstly,the EMR signal of the arc is acquired with a low sampling rate,and its root mean square value is extracted as the characteristic index representing the EMR intensity.Then,BP neu-ral network(BPNN)is adopted to mine the internal relationship among irradiance,signal receiving distance and arc EMR signal intensity,and a prediction model is established.Subsequently,according to the predicted distance between the du-al-antenna array output by BPNN and the arc,the area where the arc is located is preliminarily acquired by using the triangulation method.Finally,the photovoltaic module in the located area is divided into grids to generate grid fingerprint information,and the center coordinate of the grid that most matches the predicted distance and fingerprint information is taken as the final predicted coordinate of the arc occurrence position.The experiment results show that the proposed algo-rithm has good positioning ability and adaptability,and the average absolute error of arc fault location is 0.306 m,which is superior to the EMR attenuation model positioning method in positioning accuracy and economy.