Open AccessA New Hybrid Artificial Neural Network Based Control of Doubly Fed Induction Generator
Author(s) -
G. Venu Madhav,
Y. P. Obulesu
Publication year - 2015
Publication title -
international journal of electrical and computer engineering (ijece)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v5i3.pp379-390
Subject(s) - control theory (sociology) , artificial neural network , induction generator , matlab , computer science , doubly fed electric machine , generator (circuit theory) , controller (irrigation) , wind power , pid controller , control engineering , power (physics) , control (management) , ac power , engineering , artificial intelligence , temperature control , physics , electrical engineering , agronomy , quantum mechanics , biology , operating system
In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control technique has been developed for Doubly Fed Induction Generator (DFIG) based wind energy generation system and the performance of the system is compared with NN and PI control techniques. With the increasing use of wind power generation, it is required to instigate the dynamic performance analysis of Doubly Fed Induction Generator under various operating conditions. In this paper, three control techniques have been proposed, the first one is using PI controller, the second one is ANN control, and the third one is based on combination of ANN and PI. The performance of the proposed control techniques is demonstrated through the results, determined by using MATLab/Simulink. From the results it is observed that the dynamic performance of the DFIG is improved with the Hybrid control technique.
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