Open AccessRobust Neural Control of the Dual Star Induction Generator Used in a Grid-Connected Wind Energy Conversion System
Author(s) -
Hamza Mesai-Ahmed,
Abderrahim Bentaallah,
António J. Marques Cardoso,
Youcef Djeriri,
Imed Jlassi
Publication year - 2021
Publication title -
mathematical modelling and engineering problems/mathematical modelling of engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.080301
Subject(s) - maximum power point tracking , control theory (sociology) , robustness (evolution) , induction generator , wind power , computer science , maximum power principle , vector control , grid , stator , converters , artificial neural network , photovoltaic system , control engineering , engineering , induction motor , voltage , inverter , mathematics , artificial intelligence , control (management) , electrical engineering , biochemistry , chemistry , geometry , gene
This paper presents a field-oriented control (FOC) of a dual star induction generator (DSIG) applied in a grid-connected wind energy conversion system. Currently, the dual star induction machine (DSIM) is increasingly used among multiphase machines. The machine has two star-connections, sharing the same stator offset, by an electrical angle of 30° and fed by two parallel converters. Maximum power point tracking (MPPT) is illustrated in a first stage, in order to extract a maximum of power under fluctuating wind speed. In a second stage, vector control of a DSIG with FOC is described. Finally, voltage oriented control (VOC) is used to ensure the power factor unity on the grid side. The main contribution of the presented paper is the application of a simple architecture of an artificial neural network (ANN) controller in order to improve the robustness and stability of the system, especially against the parameter change. In comparison with the conventional control, which is known by its sensitivity, the proposed neural MPPT with neural FOC (NMPPT-NFOC) presents better performance under normal and abnormal conditions. The robustness and effectiveness of the proposed control has been validated through illustrative simulation results with different functional zones, and for fixed and variable wind speed.