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Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power, which strongly depends on both solar radiation and cell temperature. The PV power oscillations around the maximum power point (MPP) resulting from the conventional methods and complexity of the non-conventional ones are convincing reasons to look for novel MPPT methods. This paper deals with simple Genetic Algorithms (GAs) based MPPT method in order to improve the convergence, rapidity, and accuracy of the PV system. The proposed method can also efficiently track the global MPP, which is very useful for partial shading. At first, a review of the algorithm is given, followed with many test examples; then, a comparison by means Matlab/Simulink© (R2009b) is conducted between the proposed MPPT and, the popular Perturb and Observe (PO) and Incremental Conductance (IC) techniques. The results show clearly the superiority of the proposed controller. Indeed, with the proposed algorithm, oscillations around the MPP are dramatically minimized, a better stability is observed and increase in the output power efficiency is obtained. All these results are experimentally validated by a test bench developed at LIAS laboratory (Poitiers University, Poitiers, France) using real PV panels and a PV emulator which allows one to define a profile insolation model. In addition, the proposed method permits one to perform the test of linearity between the optimal current I mp (current at maximum power) and the short-circuit current I sc , and between the optimal voltage V mp and open-circuit voltage V oc , so the current and voltage factors can be easily obtained with our algorithm.

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This paper presents a novel GA (genetic algorithms) based method to carry out the maximum power point tracking (MPPT) based on the cell model. For that it is necessary to measure the open circuit voltage (Voc) and short circuit current (Isc), then the proposed algorithm gives directly and rapidly the optimal voltage (Vop) so the duty cycle is adjusted. The simulation results are obtained by changing Isc and Voc values. The proposed technique permits to verify the linearity between Vop andVoc and between optimal current (Iop) and Isc. The results are compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (IncCond) algorithms.