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N. Mohan and T. M. Undeland, Power electronics: converters, applications, and design: John Wiley & Sons, 2007.

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Article

Maximum Power Point Tracking for Solar Photovoltaic System Using Genetic Programming Toolbox for Identification of Physical System

1Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran


Journal of Automation and Control. 2015, Vol. 3 No. 1, 25-28
DOI: 10.12691/automation-3-1-4
Copyright © 2015 Science and Education Publishing

Cite this paper:
Sajjad Aliesfahani, Mehdi Shahbazian. Maximum Power Point Tracking for Solar Photovoltaic System Using Genetic Programming Toolbox for Identification of Physical System. Journal of Automation and Control. 2015; 3(1):25-28. doi: 10.12691/automation-3-1-4.

Correspondence to: Sajjad  Aliesfahani, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran. Email: sajjadaliesfahani@gmail.com

Abstract

Due to the increasing demands for energy in recent years and declining fossil fuel resources and the pollution that these energy sources are created, the solar energy produced by sun, has attracted much attention. Solar cells are elements that convert the solar energy into electricity. One of the challenges that we encounter is to obtain the most of the solar resource potential in various conditions of radiation, temperature and wind speed. A major task is to find an efficient and fast algorithm for tracking the maximum power point. In this paper, a new method for maximum power point tracking of photovoltaic (PV) cell that based on genetic programming toolbox of identification of physical system (GPTIPS), has been proposed. The maximum values of the voltage and the current of the solar cell is predicted by the GPTIPS algorithm and then the optimum duty cycle is produced for the chopper. Which cause maximum power deliver to the load. It was observed that the mean square error is reached to order of 10-5 approximately, that in similar circumstances, the mean square error and offline training time is less than a multi-layer perceptron of neural network. Simulation results show that the proposed GPTIPS approach is superior to the neural network method.

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