Effective Fault Diagnosis in Grid Connected Photovoltaic Systems Using Multiscale PCA based Artificial Neural Network Technique

Grid Connected Photovoltaic (GCPV) sys-tems has been a rising research area in the industry fields. Therefore the high reliability, performance and safety operation of GCPV systems has become a high priority. Thus, it is important of developing an intel-ligent fault detection and diagnosis method that aims at increasing the efficiency of these systems. Therefore, the present study proposes an enhanced intelligent fault diagnosis approach. In the developed procedure, the measured normal and faulty data are applied together to extract the relevant feature, reduce the impact of noise, and then special features are fed to a neural network classifiers. To do that, a Multiscale Principal Component Analysis (MSPCA)-based Artificial Neural Network (ANN) method is proposed to provide the relia-bility and safety of the GCPV systems. From the GCPV measurements, features are appropriately extracted and scaled through multiscale principal component analysis. The ANN classifier is used in classifying twenty-one faults that can occur in GCPV systems operating under different working conditions. The diagnosis results show that the developed MSPCA-based ANN method not only able to detect faults, but also can effectively distinguish between different kinds of faults.