Hybrid Neural Sliding Mode Control of a DFIG Speed in Wind Turbines
Wind energy (WG) generation industry has taken more concentration of fabrics. The WG promising renewable source of electrical energy generation for the future. This article applied neural network (NN) technique on the sliding mode command (SMC) of a 1.5 MW doubly-fed induction generator (DFIG) wind turbine (WT). In order to command the energy following between the stator of the DFIG and the grid, a proposed command design uses NN method is applied for implementing a neural command low to remove completely the chattering phenomenon on a traditional SMC strategy. The use of this technique provides very satisfactory performance for the DFIG command. The DFIG is tested in association with a WT. The simulation schemes were developed in Matlab/Simulink environment and the simulation results are presented and discussed for the whole system.
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