Photovoltaic Cells Modeling via Artificial Neural Square Fuzzy Inference System

1. Ph.D. candidate of Math. Dept., Shahid Bahonar University of Kerman, Kerman, Iran. gholamali.heydari@math.uk.ac.ir. 2 . Assist. Prof. of Electrical Eng. Dept., Shahid Bahonar University of Kerman, Kerman, Iran. a_gharaveisi@uk.ac.ir. 3. Assist. Prof. of Math. Dept., Shahid Bahonar University of Kerman, Kerman, Iran. mvali@uk.ac.ir. Abstract The present article investigates the application of high order TSK (Takagi Sugeno Kang) fuzzy systems in modeling photo voltaic (PV) cell characteristics. A method has been introduced for training second order TSK fuzzy systems using ANFIS (Artificial Neural Fuzzy Inference System) training method. It is clear that higher order TSK fuzzy systems are more precise approximators while they cover nonlinearities better than zero and first order systems with the same number of rules and input membership functions (MF). However existence of nonlinear terms of the rules’ consequent prohibits use of current available ANFIS algorithm codes as is. This article aims to give a simple method for employing ANFIS over a class of simplified second order TSK systems and applies the proposed method on the nonlinear problem of modeling PV cells. Error comparison shows that the proposed method trains the second order TSK system more effectively.

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