الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Six-phase induction machines have been widely used in many high-power industrial sectors as well as electric vehicles and wind energy conversion applications. In order to control, model and predict the performance of induction machines, it is crucial to accurately estimate their parameters. Six-phase induction machine model incorporates three sequence circuits, namely, fundamental αβ, secondary xy, and zero sequence circuits. The parameter estimation of the latter two circuits is somewhat challenging since most available sequence circuit modeling approaches incorporate different assumptions yielding a notable deviation between theoretical and experimental currents under fault conditions, where the effect of secondary and zero sequence circuits sound more effective. This thesis employs artificial intelligence-based optimization technique to better estimate the parameters of different sequence circuits by optimizing the error between experimental and estimated current and torque values. Experimental validation to the proposed estimation technique is carried out using a 1kW six-phase induction machine. The effect of winding configuration on the machine parameters has also been considered by investigating two possible winding configurations, namely, dual three-phase and asymmetrical six-phase induction machines.