New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system

Ahmed Medjber, Abderrezak Guessoum, Hocine Belmili and Adel Mellit

Energy, 2016, vol. 106, issue C, 137-146

Abstract: This work presents a new control strategy to ensure maximum power point tracking for a DFIG (doubly fed induction generator) based WECS (wind energy conversion system). The proposed strategy uses neural networks and fuzzy logic controllers to control the power transfer between the machine and the grid using the indirect vector control and reactive power control techniques. This transfer is ensured by controlling the rotor via two identical converters. The first converter is connected to the RSC (rotor side) and the second is connected to the GSC (grid side) via a filter. The DC (Direct Current) link voltage is controlled by a fuzzy controller. This control strategy is used to control the rotor side currents and to protect the generator by limiting the output current (or voltage).

Keywords: Neural network; Fuzzy logic; Controllers; Maximum power; Wind energy conversion system (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:106:y:2016:i:c:p:137-146

DOI: 10.1016/j.energy.2016.03.026

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