Photovoltaic Temperature Estimation Model for Rapid Irradiance Change Conditions in Tropical Regions Using Heuristic Algorithms

The knowledge of module temperature is necessary to implement any energy management technique that requires the prediction of solar power output. Current PV temperature estimation models are generally divided into three categories: Empirical models, Physical Steady State models and Physical Dynamic models. Each of these methods have their own disadvantages. In this paper, particle swarm optimization (PSO) is used to improve the accuracy of a simple physical dynamic model, the two parameter Resistance and Capacitance (RC) circuit model. The effectiveness of the proposed PSO-based parameter estimation for the RC circuit model is verified using an experimental dataset measured from a CIGS PV module at a 1-sec sampling interval. The performance of our proposed RC circuit model is then compared with two empirical models and a physical steady state model, the NOCT-standard model, the Veldhuis model and the Mattei model, respectively. The proposed model provides a significantly better temperature estimation than the NOCT and Mattei models, which do not effectively account for the thermal inertia of a PV module and hence are oversensitive to rapid irradiance variations. The proposed model provides a similar, but slightly better, temperature estimation to that of the Veldhuis model but is significantly easier to implement.