Influence of crossover methods used by genetic algorithm-based heuristic to solve the selective harmonic equations (SHE) in multi-level voltage source inverter

Abstract

Genetic Algorithms (GA) has always done justice to the art of optimization. One such endeavor has been made in employing the roots of GA in a most proficient way to determine the switching moments of a cascaded H-bridge seven level inverter with equal DC sources. Evolutionary techniques have proved themselves efficient to solve such an obscurity. GA is one of the methods to achieve the objective through biological mimicking. The extraordinary property of crossover is extracted using Random 3-Point Neighbourhood Crossover (RPNC) and Multi Midpoint Selective Bit Neighbourhood crossover (MMSBNC). This paper deals with solving of the selective harmonic equations (SHE) using binary coded GA specific to knowledge based neighbourhood multipoint crossover technique. This is directly related to the switching moments of the multilevel inverter under consideration. Although the previous root-finding techniques such as N-R or resultant like methods endeavor the same, the latter offers faster convergence, better program reliability and wide range of solutions. With an acute algorithm developed in Turbo C, the switching moments are calculated offline. The simulation results closely agree with the hardware results.