Identification of the Parameters of a Photovoltaic Cell Using an Improved Genetic Algorithm (GA) Technique and Particular Swarm Optimization (PSO)

The modeling of a photovoltaic cell predicts the behavior of the cell in various environmental contexts of the real world, the identification of the parameters of the photovoltaic cell is essential to simulate the behavior and to optimize the different characteristics of a photovoltaic cell (better energy management and good operating reliability). In this work, two intelligent algorithms were used and compared for the identification of the parameters of a photovoltaic cell. The proposed approach combines the simplicity of the equations of the explicit method with that of two meta-heuristic methods, namely the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO). These techniques used for the identification of the parameters of the unknown model namely the photos run (Iph), the current of saturation (Is), the resistance series (Rs), and the factor of ideality (A) is last to govern the relation current - voltage of a solar cell. The objective is to create an objective function that aims to solve an optimization problem (find the optimal solution in terms of parameters), The panels studied in this work is the RTC France, and the characteristic curves of the panel are obtained using only the information provided by the manufacturer's datasheet, thus avoiding the need to carry out experimental data. The performances and the precision of the proposed method are evaluated by applying the model to a diode with four unknown parameters, the combination of the explicit equations with Meta-heuristic techniques allows to obtain an excellent performance of optimization and a high precision of estimation of the results. The choice of the parameters PSO and GA is very important for a faster convergence of the algorithm.