Data-driven modelling of a doubly fed induction generator wind turbine system based on neural networks

Xiaobing Kong; Xiangjie Liu; Kwang Y. Lee

Data-driven modelling of a doubly fed induction generator wind turbine system based on neural networks

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Author(s): Xiaobing Kong ¹ ; Xiangjie Liu ¹ ; Kwang Y. Lee ²
- Affiliations: 1: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, People's Republic of China;
  2: The Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798-7356, USA
Source: Volume 8, Issue 8, November 2014, p. 849 – 857
DOI: 10.1049/iet-rpg.2013.0391 , Print ISSN 1752-1416, Online ISSN 1752-1424

Received 06/12/2013, Accepted 27/02/2014, Revised 02/02/2014, Published 06/06/2014

In a wind power system, the wind turbine captures wind energy and converts it into electric energy through a coupled rotating generator. This renewable energy conversion system usually consists of a wind turbine, rotor, gearbox and mostly a doubly fed induction generator (DFIG). It is a complex non-linear multi-input multi-output system with many uncertain factors. Meanwhile, the dynamics of the system is quite dependent on the wind velocity. Traditional analytical methods are quite difficult to model such a complex system. The recently developed data-driven method can be a suitable modelling technique for such system. Using a large amount of input–output on-line measurement data from the selected months, neural networks and neuro-fuzzy networks are fully utilised to model the DFIG. Detailed analysis and comparisons with the classical system identification techniques are addressed to show the advantages of the data-driven DFIG modelling approach.

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Data-driven modelling of a doubly fed induction generator wind turbine system based on neural networks

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