An Improved Teaching Learning Based Optimization Algorithm for Simulating the Maximum Power Point Tracking Controller in Photovoltaic System | ||||
| The Egyptian International Journal of Engineering Sciences and Technology | ||||
| Article 2, Volume 21, EIJEST, Vol. 21, 2016, December 2016, Page 9-18 PDF (489.31 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/eijest.2016.97179 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| A Fathya* 1; I Zidan2; D Amer2 | ||||
| 1Electrical Power and Machine Dept., Faculty of Engineering, Zagazig University, Egypt | ||||
| 2Computer Engineering Dept., Faculty of Engineering, Zagazig University, Egypt | ||||
| Abstract | ||||
| and essential issue nowadays. Each PV module has its own specific characteristics and its own maximum power point (MPP). This maximum power point varies according to the change in temperature and solar irradiation. Therefore, it is important to use a maximum power point tracker (MPPT) in the PV system; to guarantee a maximum output power from solar module under varying conditions. There are many algorithms used to perform controller function that are conventional and meta-heuristic methodologies, in this paper a proposed metaheuristic algorithm based on an improved teaching-learning based optimization algorithm (ITLBO) is presented and investigated to track the MPP extracted from the PV system under variable operating conditions. The proposed algorithm gives the available maximum power under non-uniform solar irradiation. The obtained results are compared with those obtained via the conventional perturb and observe (P&O), particle swarm optimization (PSO) and TLBO algorithms. The proposed ITLBO results are more accurate and give fast convergence output power. | ||||
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