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

Authors:
Syafaruddin;Engin Karatepe;Takashi Hiyama
Affiliations:
Department of Electrical Engineering of Universitas Hasanuddin, 90245 Tamalanrea-Makassar, Indonesia;Department of Electrical and Electronics Engineering of Ege University, 35100 Bornova-Izmir, Turkey;Department of Computer Science and Electrical Engineering of Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan
Venue:
Computers & Mathematics with Applications
Year:
2012

Citing 8
Cited 1

Type-2 fuzzy wavelet networks (T2FWN) for system identification using fuzzy differential and Lyapunov stability algorithm

Applied Soft Computing
New fuzzy wavelet neural networks for system identification and control

Applied Soft Computing
A new approach to fuzzy wavelet system modeling

International Journal of Approximate Reasoning
Design of fuzzy wavelet neural networks using the GA approach for function approximation and system identification

Fuzzy Sets and Systems
Fuzzy wavelet neural network models for prediction and identification of dynamical systems

IEEE Transactions on Neural Networks
Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data

Applied Soft Computing
Fuzzy wavelet networks for function learning

IEEE Transactions on Fuzzy Systems
Wavelet networks

IEEE Transactions on Neural Networks

Identifying the tool-tissue force in robotic laparoscopic surgery using neuro-evolutionary fuzzy systems and a synchronous self-learning hyper level supervisor

Applied Soft Computing

Quantified Score

Hi-index	0.09

Visualization

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.