Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction

Developing an accurate model for photovoltaic solar cell and module represents a challenge to improve the overall efficiency of the photovoltaic systems use. Parameter estimation of photovoltaic solar cell and module circuit model is a crucial task that is commonly transformed into an optimisation issue solved by metaheuristic algorithms. Among these algorithms, the particle swarm optimisation has gained great interest due to its structure simplicity and rapid response. However, its major disadvantage lies in the premature convergence. In an endeavour to deal with this problem, an improved mutated particle swarm optimisation algorithm with adaptive mutation strategy is proposed in this paper. The adaptive mutation is introduced to alleviate the premature convergence problem and ensure a suitable trade-off between the explorative and exploitative capabilities over the search process. The proposed algorithm is used to identify the optimal parameters of different photovoltaic models; single diode, double diode, and photovoltaic module. The performance of the used method is firstly evaluated on measured data reported in the literature. Then, the algorithm is tested based on measured data from the laboratory work and from the data sheet of different modules. Experimental results prove that the used algorithm achieves higher accuracy and provides the lowest root mean square error compared to other previously reported parameter extraction algorithms.