Evolutionary Design Automation of High Efficiency Series Resonant Converter for Photovoltaic Systems

Abstract

Series resonant converter (SRC) is one of the main parts which has to be designed carefully to be compatible with the general requirements of photovoltaic (PV) grid-connected system. The extensive search space for design parameters of SRC such as resonant tank component values, transformer turn ratio, dead time and switching frequency requires an automated design framework. The proposed framework is developed using genetic algorithm (GA), differential evolution algorithm (DEA), and nondominated sorting genetic algorithm (NSGA-II) for the multi-objective optimization of SRC circuit while accomplishing zero voltage switching (ZVS). Regulating the output voltage while minimizing the losses of MOSFET, diode and transformer are the design objectives. FEM is applied to subdivide the single phase transformer into simpler and finite parts to calculate flux density value according to the optimized design parameters. Amongst these evolutionary algorithms, NSGA-II achieved the best optimization performance considering various operation modes and required specifications. Simulation results are also provided to validate the efficient and robust automated design of SRC to be used in photovoltaic systems.