Parameter estimation of photovoltaic cell and module models relied on metaheuristic algorithms including artificial ecosystem optimization

Due to the nonlinear characteristic of the power-voltage (P–V) and current–voltage (I–V) relationship of the photovoltaic systems, building accurate mathematical models of photovoltaic cell and module is essential for validation and optimization performance of photovoltaic systems. However, determination of the unknown parameters of photovoltaic cell and module models is a complex nonlinear optimization problem that requires effective solving methods. This paper proposes an approach of parameter estimation of photovoltaic cell and module models based on artificial ecosystem-based optimization (AEO). The AEO is a new developed metaheuristic algorithm taken idea of the mechanisms of ecosystem consisting of production, consumption and decomposition. The advanced feature of AEO is the diversity of exploration and exploitation mechanisms without special control parameters. The performance of AEO is discussed in two photovoltaic cell models and two photovoltaic module models. Its performances compared to five algorithms consisting of backtracking search algorithm (BSA), cuckoo search algorithm (CSA), equilibrium optimizer (EO), genetic algorithm (GA) and particle swarm optimization (PSO) as well as the previous methods. The numerical results show that AEO has given the better performance than BSA, CSA, EO, GA and PSO for the problem of parameter estimation of photovoltaic models in terms of aspects such as reaching the lower error between the experimental and estimated values and having the better statistical results in several runs than the above methods. Thus, AEO can be an effective method for estimating the unknown parameters of photovoltaic cell and module models.