Feasibility of Artificial Neural Network for Maximum Power Point Estimation of Non Crystalline-Si Photovoltaic Modules

Abstract

Solar cell markets are growing favorably. The emerging non crystalline silicon (c-Si) technologies are starting to make significant in-roads into solar cell markets. The most of the artificial neural network (ANN) have been used in maximum power points tracking applications for c-Si solar cell technology. However, the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in current-voltage characteristic. In this reason, the investigation of feasibility using neural networks is very important for different solar cell technologies to increase the efficiency of photovoltaic (PV) systems. The paper investigates three different ANN structures, such as radial basis function (RBF), adaptive neurofuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN) for identification the optimum operating voltage of non c-Si PV modules. These ANN models have been trained and verified for double junction amorphous Si (2j a-Si), triple junction amorphous Si (3j a-Si), Cadmium Indium Diselenide (CIS) and thin film Cadmium Telluride (CdTe) solar cell technologies. The results show that the flexibility of training process, the simplicity of network structure and the accuracy of validation error are important factors to select a suitable ANN model.