光伏直流串联电弧故障区域识别

摘 要：光伏直流系统中串联电弧无法被保护装置检测并切断，电弧将持续燃烧并产生高温，对光伏系统安全运行造成极大危害。针对此研究Cassie电弧模型，将电极间距变量引入该模型，分析电极间距与电弧稳定燃烧关系。搭建光伏串联电弧仿真模型，采集稳定燃烧时不同位置下的电流数据，通过分析电弧故障特性，确定电弧故障特征向量，搭建概率神经网络故障识别模型，引入BP神经网络作为对照，将串联电弧类别与区域对应，实现串联电弧故障区域识别。结果表明：改进型Cassie电弧模型能够表征不同电极间距下的动态过程与稳定燃烧状态；在相同数据下，概率神经网络模型识别率高于BP神经网络，能够准确定位串联电弧故障区域。

Photovoltaic DC series arc fault area identification

The series arc in the photovoltaic DC system cannot be detected and cut off by the protection device, the arc continue to burn and generate high temperatures, which cause great harm to the safe operation of the photovoltaic system. Aiming at which, Cassie arc model is analyzed, the electrode spacing is introduced into the model, and the relationship between electrode spacing and arc stable combustion is analyzed. Then, the photovoltaic series arc simulation model is established, and the current data in different regions during steady combustion is obtained, feature vectors are determined by analyzing the arc fault characteristics, a probabilistic neural network model is built to identify the arc fault, and BP neural network model is established as comparison, the series arc category corresponds to the area to realize the identification of the series arc fault area. The results show that the improved Cassie arc model can be used to characterize the dynamic process and stable combustion state in different areas; under the same data, the recognition rate of the probabilistic neural network model is higher than the BP neural network, and the series arc fault area can be accurately located.