DFIG machine design for maximizing power output based on surrogate optimization algorithm

Zheng Tan, Xueguan Song, Wenping Cao, Zheng Liu, Yibin Tong

Research output: Contribution to journalArticle

Abstract

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.
Original languageEnglish
Pages (from-to)1154-1162
Number of pages9
JournalIEEE Transactions on Energy Conversion
Volume30
Issue number3
Early online date1 Apr 2015
DOIs
Publication statusPublished - Sep 2015

Bibliographical note

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

Keywords

  • doubly fed induction generator
  • operating conditions
  • particle swarm optimization
  • power loss
  • rewinding
  • surrogate model
  • wind power generation

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