PROJECT TITLE :

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

ABSTRACT:

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding style for maximizing power yield. Based mostly on site-specific wind profile information and therefore the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum potency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are employed in conjunction with the finite component methodology to optimize the machine style utilizing the limited accessible information for the positioning-specific wind profile and generator operating conditions. A response surface methodology in the surrogate model is developed to formulate the look objectives and constraints. Besides, the machine tests and potency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.


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