Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS"

Tarek Boutabba (1) , Said Drid (2) , Mohamed Benbouzid (3)

1 LSPIE / LBMS
2 LSPIE - Laboratoire des Systèmes de Propulsion-Induction Electromagnétique
3 LBMS - Laboratoire brestois de mécanique et des systèmes

Due to scarcity of fossil fuel and increasing demand of power supply, we are forced to utilize the renewable energy resources. Considering easy availability and vast potential, world has turned to solar photovoltaic energy to meet out its ever increasing energy demand. The mathematical modeling and simulation of the photovoltaic system is implemented in the MAT LAB/Simulink environment and the same thing is tested and validated using Artificial Intelligent (Al) Iike AN FIS. This paper presents Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS". The PV array has an optimum operating point to generate maximum power at some particular point called maximum power point (MPP). To track this maximum power point and to draw maximum power from PV arrays, MPPT controller is required in a stand-alone PV system. Due to the nonlinearity in the output characteristics of PV array, it is very much essential to track the MPPT of the PV array for varying maximum power point due to the insolation variation. In order to track the MPPT conventional controller like Adaptive Neuro-Fuzzy "ANFIS" and fuzzy logic controller is proposed and simulated. The output of the controller, pulse generated from PWM can switch MOSFET to change the duty cycle of boost DC-DC converter. The result reveals that the maximum power point is tracked satisfactorily for varying insolation condition.

Mots clés: Photovoltaic; pulse width modulation; proportional integral controller; boost DC-DC; ANFIS; fuzzy logic; MPPT

Domaines: Energie électrique

Tarek Boutabba, Said Drid, Mohamed Benbouzid. Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS". 2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), Mar 2013, Monaco, Monaco. pp.1-7. ⟨hal-00833734⟩