Artificial intelligence and ANFIS reduced rule for equivalent parameter estimation of PV module on various weather conditions utilized for MPPT

Authors

  • Rati Wongsathan Department of Electrical Engineering, Faculty of Engineering, North-Chiang Mai University 169 Hangdong Rd. Chiang Mai
  • Isaravuth Seedadan Department of Electrical Engineering, Faculty of Engineering, North-Chiang Mai University 169 Hangdong Rd. Chiang Mai

Keywords:

Multi-layered perceptron neural network, radial basis function neural networks, ANFIS, PV module, genetic algorithm

Abstract

      In order to facilitate the most appropriate and robust method for the overall efficiency of photovoltaic (PV), providing a more accurate optimization algorithm to extract the equivalent parameters of PV models prior to installation is necessary. In this paper, a numerical technique based on genetic algorithms (GAs)is performed to determine the equivalent parameters of PV solar module based on single diode model including with the photo current (Iph), the saturated diode current (Is), the series resistance (Rs), the parallel resistance (Rsh), and the ideality factor (n). The measured I-V data from the experiment with the uncertainties arising from measurement noise in various weather conditions is adopted to extract these parameters by GA in order to overcome the local minima trap which occurs in the non-convex optimization problem. The preliminary solutions by GA extraction are used to train the estimation model including multi-layered perceptron neural network (MLPNN), radial basis function neural network (RBFNN), and adaptive neuro-fuzzy inference system (ANFIS)model for all weather conditions. The ANFIS modelremarkably showed the best performance which preserves the RMSE lower than the rest for both the interpolation and extrapolation. However, the complexity causedbynumber of ANFIS model parameters and high computation time makes it unsuitable in the real practice. The ANFIS reduced model is then introduced and designed to select the significant rule node of ANFIS model by keeping an acceptable accuracy. The overall proposed model was used to determine the corresponding maximum power point (MPP)from the I-V characteristic obtained from the estimated parameter whichgenerates the power difference as the input of the fuzzy logic controller (FLC)implementation based the maximum power point tracking (MPPT).

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Published

2017-06-21

How to Cite

Wongsathan, R., & Seedadan, I. (2017). Artificial intelligence and ANFIS reduced rule for equivalent parameter estimation of PV module on various weather conditions utilized for MPPT. Journal of Renewable Energy and Smart Grid Technology, 12(1), 37–56. Retrieved from https://ph01.tci-thaijo.org/index.php/RAST/article/view/90328

Issue

Vol. 12 No. 1 (2017): January - June 2017

Section

Articles

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