Open AccessImproving of Maximum Power Point Tracking for Photovoltaic Systems Based on Swarm Optimization Techniques
Author(s) -
Abbas H. Miry,
Ali Hussien Mary,
Mohammed Hussein Miry
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/518/4/042003
Subject(s) - photovoltaic system , maximum power point tracking , maximum power principle , control theory (sociology) , computer science , renewable energy , matlab , genetic algorithm , mathematical optimization , engineering , voltage , mathematics , control (management) , inverter , electrical engineering , artificial intelligence , operating system
The photovoltaic system considers one of the important renewable sources of energy that using solar cell. Limitation and crisis of other energy resource make the photovoltaic system is growing. This motivated the researchers to improve and maximize renewable energy sources effectiveness. The optimal operating point is located in the PV voltage curve of the solar cell which is called Maximum Power Point (MPP). This point changes in nonlinear form with varying of solar incandescence, temperature and solar cell properties. Recently more methods are developed to get optimal value of MPPT and one of these methods is the Extremum-Seeking Control (ESC) which is based on filter operation. In this paper different optimization methods are presented such as Genetic Algorithm (GA), Grey Wof Optimizer (GWO) and Ant Lion Optimizer (ALO). They are used as a tuning tool for ESC controller parameters to improve the MPPT performance. All methods are tested in the MATLAB environment, the result shows that the effectiveness of swarm optimization, especially GWO in term the efficiency and speed of converging.