Multiphase Matrix Converter Modulation for Wind Energy Systems using Genetic Algorithm

Abstract

A wind energy generation system (WEGS) is considered one of the cleanest forms of renewable energy which leads to a considerable reduction in carbon footprint. Wind turbine-driven induction generators are connected through power converters to feed the grid at a desired voltage and frequency. For this purpose, multiphase induction generators (MPIG) in conjunction with multiphase matrix converters (MPMC) are being explored due to their advantages of higher torque density, greater fault tolerance, and lower current per phase requirement. The multiphase motors can be used with MPMCs to integrate with the three-phase grid. In this work, the modulation of multiphase matrix converters is considered when employed with six-phase machines. A six-phase to three-phase matrix converter (MC) and three-phase to six-phase MC modulation will be presented, allowing the integration of a six-phase induction machine with the three-phase grid. The unity voltage transfer ratio for six to three configurations is its distinguishing feature for which the modulation functions will be shown. Further, the optimal modulation functions will be derived for three to six MCs using the metaheuristic genetic algorithm-based artificial intelligence technique. The work will be supported by analytics and MATLAB-based simulation results.