Fault Detection System of Photovoltaic Based on Artificial Neural Network

Abstract

Using PV systems, solar energy may be used to create electricity. Every year, the proportion of solar energy in the electric system increases significantly. On the other hand, photovoltaic cells are susceptible to malfunctions that diminish their efficiency and profitability. Due to the severity of the defects, fault detection and diagnosis (FDD) in the PV system have become difficult. Thus, the primary objective of the proposed study is to detect and diagnose particular types of PV system problems using an artificial neural network (ANN). This early operation is more effective for avoiding errors during PV installation and minimizing PV system power losses. A new solar cell model is created in the MATLAB/SIMULINK environment and is used to identify the fault dataset. The solar cell consists of three parallel strings and three series modules, with each module containing 20 series-connected photovoltaic cells. This model determines four parameters (V-load in Volts, I-load in Amps, Irradiance in W/m2, and Temperature in Celsius) under varying situations (five temperature values, three irradiance values, and three-time events), which are repeated for all faults evaluated. In the training and testing phases of the proposed ANN, these four parameters are utilized as effective features. Additionally, this network possesses eight outputs, one for each defect. Using implementations with three different numbers of hidden layers and an identical fault dataset, the performance of the proposed network is tested. The findings of the simulation indicate a considerable proportion of fault detection and diagnosis accuracy, ranging between 95% and 98%. The computed RMSE reveals that the training period yields 0.193% improvement, whereas the testing phase yields 0.365%.