Open AccessSliding mode type‐2 neuro‐fuzzy power control of grid‐connected DFIG for wind energy conversion system
Author(s) -
Moradi Hassan,
AlinejadBeromi Yousef,
Yaghobi Hamid,
Bustan Danyal
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0066
Subject(s) - doubly fed electric machine , control theory (sociology) , wind power , mode (computer interface) , computer science , grid , control (management) , ac power , engineering , electrical engineering , voltage , mathematics , artificial intelligence , geometry , operating system
This study presents an adaptive sliding mode type‐2 neuro‐fuzzy controller for power control of doubly fed induction generators (DFIGs). DFIG‐based wind turbine system is variable‐speed constant‐frequency wind energy conversion system. In this proposed control scheme, in order to enhance its performance, sliding mode control (SMC) theory is used for online training the parameters of type‐2 fuzzy system membership functions. To regulate the antecedent and consequent part parameters, the SMC adaptive technique is used according to the controller inputs. These inputs are active and reactive power errors and their time derivative, which applied to the structure of T2NF system. The proposed controller employs an interval type‐2 fuzzy system because of the uncertainties of the wind speed and variation in parameters in the wind power conversion system. The simulations carried out for a DFIG‐based 1.5 MW wind turbine. The results of simulation are compared with the classical proportional–integral controller to confirm the effectiveness of this control scheme. This comparison is carried out between the cut‐in and rated region wind speeds. The results of simulation show that the proposed control scheme has better performance to track the peak power and is more robust to machine parameter variations.