Meta-heuristic Optimization of the Neuro-Fuzzy MPPT Controller for PV Systems Under Partial Shading Conditions

Document Type : Original Article


1 Department of Physics, Faculty of Sciences, The University of Ngaoundere, Cameroon

2 Department of Physics, Higher Teacher Training College of Bertoua, The University of Bertoua, Cameroon



The main challenge of photovoltaic (PV) systems is to extract the maximum power from the array, especially when it is partially shaded and subjected to variable weather conditions (sunshine and temperature). To address this challenge, this manuscript proposes a new method based on the Neuro-Fuzzy- Particle Swarm Optimization (NF-PSO) combination. The NF method is used here because it allows an automatic generation of fuzzy rules, and we inject the PSO meta-heuristic at the input of the Neuro-fuzzy to find an optimal gain allowing not only to convert the real input values into fuzzy quantities and to readjust the dynamics of the fuzzy rules by reducing the power losses (oscillations), this combination also provides a simple and robust MPPT scheme to manage efficiently the partial shading, and its convergence to the global maximum power point (GMPP) is independent of the initial conditions of the search process. To confirm the NF-PSO as a viable MPPT option a comprehensive evaluation is performed against two other methods, namely the cuckoo algorithm and the original Neuro-Fuzzy. The simulation results of the system confirmed the better performance of this method in terms of speed with a response time of 0.044s, efficiency with 99.94%, and especially in terms of oscillation reduction with practically a negligible oscillation rate compared to the NF and the Cuckoo algorithm.


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