A Self Adaptive Fuzzy Logic MPPT Controller Based on Genetic Algorithm

Daniele Menniti(1*), Anna Pinnarelli(2), Nicola Sorrentino(3), Giovanni Brusco(4), Alessandro Burgio(5)

(1) University of Calabria - Department of Mechanics, Energetics and Management, Via P. Bucci - Arcavacata di Rende (CS), Italy
(2) University of Calabria - Department of Mechanics, Energetics and Management, Via P. Bucci - Arcavacata di Rende (CS), Italy
(3) University of Calabria - Department of Mechanics, Energetics and Management, Via P. Bucci - Arcavacata di Rende (CS), Italy
(4) University of Calabria - Department of Mechanics, Energetics and Management, Via P. Bucci - Arcavacata di Rende (CS), Italy
(5) University of Calabria - Department of Mechanics, Energetics and Management, Via P. Bucci - Arcavacata di Rende (CS), Italy
(*) Corresponding author


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Abstract


A photovoltaic generator needs to be controlled in order to extract the maximum power from the primary energy source nonlinearly depending on the atmospheric operating conditions. For years, research has focused on various maximum power point tracking algorithms to draw the maximum power of the solar array and many methods have been proposed in literature; the most famous and easiest to implement are the “Perturb & Observe” method (P&O) and the “Incremental Conductance” (InCo) method. One of the computational methods, which have demonstrated good performance under different atmospheric operating conditions, is the maximum power point tracking technique based on fuzzy logic, which gives better converge speed and improves the tracking performance with minimum oscillation. Asits performance mainly depends on the domain of input and output fuzzy variables, this paper proposes a Genetic algorithm to automatically choose such domains. Its performance has been evaluated simulating it on a grid connected photovoltaic generation system implemented in a Matlab/Simulink environment. The simulation results show that the proposed maximum power point tracking controller is more efficient than conventional ones when the photovoltaic system is subjected to variations in atmospheric operating conditions.
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Keywords


Photovoltaic; MPPT; Grid-Connected; Fuzzy Logic; Genetic Algorithm

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References


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