Artificial Neural Network based control for PV/T panel to track optimum thermal and electrical power

As solar energy is intermittent, many algorithms and electronics have been developed to track the maximum power generation from photovoltaic and thermal panels. Following technological advances, these panels are gathered into one unit: PV/T system. PV/T delivers simultaneously two kinds of power: electrical power and thermal power. Nevertheless, no control systems have been developed in order to track maximum power generation from PV/T system. This paper suggests a PV/T control algorithm based on Artificial Neural Network (ANN) to detect the optimal power operating point (OPOP) by considering PV/T model behavior. The OPOP computes the optimum mass flow rate of PV/T for a considered irradiation and ambient temperature. Simulation results demonstrate great concordance between OPOP model based calculation and ANN outputs.

► We establish a state model of PV/T panel.
► We study the effect of mass flow rate on PV/T efficiency.
► A real time PV/T control algorithm is proposed.
► A model based optimal thermal and electrical power operation point is tracked.