基于光伏组件内部参数辨识的故障诊断模型

Fault Diagnosis Model Based on Parameter Identification of PV Modules

摘要:

光伏组件出现故障时,其内部参数应该最能反映其故障特性,基于此通过比较不同故障状况下的光伏组件内部参数特性,提取相应的故障特征；首先通过MATLAB建立光伏组件各故障模型,通过改进量子粒群算法对不同故障类型下的光伏组件进行参数辨识并获取相应的数据,利用数据建立BP神经网络故障诊断模型并对故障类型进行诊断；仿真结果证明所用参数辨识方法可以准确对光伏组件内部参数进行辨识,诊断结果也表明所选取的故障特征是合理的,通过此模型可以准确的对光伏组件故障进行诊断。

Abstract:

When the PV module fails, its internal parameters should reflect the fault characteristics of the PV module. Based on the comparison of the internal parameters of the PV modules under different fault conditions, the corresponding fault characteristics are extracted. First, the fault models of PV modules are established by MATLAB. Quantum particle swarm algorithm is used to identify the PV modules under different fault types and obtain the corresponding data. The BP neural network fault diagnosis model is established by using the data and the fault type is diagnosed. The simulation results show that the parameter identification method can accurately identify the PV modules Parameter identification, the diagnostic results also show that the selected fault feature is reasonable, through this model can accurately diagnose the PV module failure.