Fuzzy wavelet network identification of optimum operating point of non-crystalline silicon solar cells

Abstract

The emerging non-crystalline silicon (c-Si) solar cell technologies are starting to make significant inroads into solar cell markets. Most of the researchers have focused on c-Si solar cell in maximum power points tracking applications of photovoltaic (PV) systems. However, the characteristics of non-c-Si solar cell technologies at maximum power point (MPP) have different trends in current–voltage characteristics. For this reason, determining the optimum operating point is very important for different solar cell technologies to increase the efficiency of PV systems. In this paper, it has been shown that the use of fuzzy system coupled with a discrete wavelet network in Takagi–Sugeno type model structure is capable of identifying the MPP voltage of different non-c-Si solar cells with very high accuracy. The performance of the fuzzy-wavelet network (FWN) method has been compared with other ANN structures, such as radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN). The simulation results show that the single FWN architecture has superior approximation accuracy over the other methods and a very good generalization capability for different operating conditions and different technologies.