Design, simulation and implementation of an intelligent MPPT using a ZVCS resonant DCDC converter

Maximum Power Point Trackers (MPPT) are widely used in PV applications. This is because the Maximum Power Point (MPP) of a solar panel varies with the irradiation and temperature so MPPT is required to adjust the PV power point on the maximum state. On the other hand a DC-DC converter should be used to overcome variations in PV panel output power and draw the maximum power. In this paper, an intelligent Maximum Power Point Tracker (MPPT) is designed on base of Artificial Neural Networks (ANN). The ANN controller receives temperature, voltage and current of PV panel and solar radiation as input signals. In the next step it estimates the optimum duty cycle of converter for maximum available power. To optimize efficiency of the system, a Full bridge Series-Parallel DC Converter was designed. Soft switching operation using Zero Current Switching (ZCS) and Zero Voltage Switching (ZVS) technologies is employed to decrease the losses and optimize the efficiency of converter. To evaluate the performances of proposed intelligent MPPT, a prototype is implemented and experimental results are presented. © 2012 IEEE