Parameter Optimization of Various Solar Cell Models by Neural Network Algorithm

Precise recognition of unknown variables for different types of solar cells is important in design, control, quality, cost estimation, and prediction of solar cell performance. Development of a single solar cell to a set of cells (solar panels) is usually based on a single operating point on the current-voltage characteristic curve. In recent years, a new method to predict cell performance and cell screening by modeling the cell is presented using an equivalent electrical circuit in which each variable corresponds to a physical phenomenon in the solar cell. These analytical models can be represented by a five-variable, seven-variable models, and recently nine-variable model. Due to the nonlinearities and inability of traditional methods in identifying the unknown variables of the system, recently intelligent algorithms have attracted considerable attentions in solving engineering problems. Neural network algorithm (NNA) is a metaheuristic optimization algorithm that is inspired by the function of the neural network of human brain. In this article, the optimum parameter identification technique of a silicon commercial solar cell is used for single diode, two diode, and three diode models. The obtained optimization results of this research are compared with other optimizers in the literature and the surrounding discussions are done. The improvement level reported by the NNA in comparison with the best reported results in the literature for one, two, and three diode models are 0.44, 0.085, and 17.97 percent, respectively. The obtained results of the proposed NNA method have the highest accuracy among the other optimizers in the literature.