Open AccessControl for grid‐connected DFIG ‐based wind energy system using adaptive neuro‐fuzzy technique
Author(s) -
Shihabudheen K.V.,
Raju S. Krishnama,
Pillai G.N.
Publication year - 2018
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/etep.2526
Subject(s) - control theory (sociology) , wind power , adaptive neuro fuzzy inference system , computer science , control engineering , neuro fuzzy , fuzzy control system , grid , fuzzy logic , extreme learning machine , intelligent control , energy (signal processing) , engineering , artificial neural network , control (management) , artificial intelligence , geometry , electrical engineering , mathematics , statistics
Summary Smooth operation and control of power electronic converters are essential to ensure wind energy systems in compliance with modern grid codes. This paper proposes an intelligent adaptive control strategy for doubly fed induction generator–based wind energy system using recently proposed extreme learning adaptive neuro‐fuzzy inference system (ELANFIS). ELANFIS is a type of neuro‐fuzzy systems, which combines erudition capabilities of extreme learning machine and unambiguous knowledge of fuzzy systems. In ELANFIS, premise parameters are generated randomly with restraints to house fuzziness and consequent parameters are identified using Moore‐Penrose generalized inverse method. The vector control with proposed ELANFIS control strategy is tested under various contingencies and is able to handle the uncertainties in the wind speed and grid disturbance. The performance of the proposed technique is verified through real‐time digital simulator with hardware in loop configuration.