Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

Abstract

Determined parameters for photovoltaic (PV) model is of great practical significance in prediction of output power in PV array and tracing its maximum power point. An optimization algorithm based on niche particle swarm optimization in parallel computing (NPSOPC) is proposed to identify the parameters of PV model. A diode equivalent circuit model is applied to simulate the output characteristics of PV model. On the support of the output current-voltage data of PV model, the parameters identification is transformed to be a multivariate, nonlinear mathematical optimization problem. A mathematical model with objective optimization function is established to quantify the discrepancy between the current experimental data and the simulation data. A particle swarm optimization algorithm based parameters extraction model is established with niches in parallel architecture to improve the extraction performance. Contrast experiments of these three models are carried out in the different condition with different light intensity and temperatures to verify the good performance of the proposed approach. The results indicate that the proposed algorithm can be utilized as an accurate, reliable and promising alternative approach for parameters acquisition in single diode model, double diode model and PV module model.