A high degree of direct torque control applied to a grid-connected wind energy system based on a DFIG

Abstract

This paper presents the performances improvement of a doubly fed induction generator (DFIG) driven by a wind turbine (WT) using direct torque control (DTC). However, the major drawbacks related to DTC are high torque/flux ripples that produce mechanical vibration and disagreeable noise. The use of multilevel inverters seems to be an interesting solution. A three-level voltage source (inverter) converter (3LVSI) connected to the rotor side of the DFIG is considered in this paper. The high freedom degree of the voltage vectors selection in the 3LVSI allows a control with minimal torque and flux ripples. In addition, a fuzzy logic approach is introduced, to ensure an intelligent extraction of the energy sweeping the WT blades. A variable adjustment step enables an optimal extraction in a minimum tracking time with significant reduction of oscillations in the steady state. Simulation results obtained using MATLAB/SIMULINK demonstrate the effectiveness of the 3LVSI-DTC control based on Fuzzy MPPT in the wind energy conversion system (WECS).

Multilevel Converter and Fuzzy Logic Solutions for Improving Direct Control Accuracy of DFIG-based Wind Energy System

“…The WECS based on a doubly fed induction generator (DFIG) is now the most frequently utilized in wind farms; it can operate in different modes with variable and wide speed range. Since low power converters are used on the rotor side, the power losses are minimized [4,5]. The DFIG's rotor side control is characterized by controlling two back-to-back converters.…”

Comparative study between PI, FLC, SMC and Fuzzy sliding mode controllers of DFIG wind turbine

“…In fact, wind energy system has high nonlinearity obtained from uncertainties, wind speed turbulence, and the changes in wind system parameters (Krim et al 2018). For this reason, nonlinear controls have been developed to be not very sensitive to wind speed and parameters variation, such as sliding mode controller Benattous, 2011, 2012;Azzouz et al 2019;Bouguerra and Benfdila, 2023), fuzzy logic controller (Belaimeche et al, 2018;Cheikh et al, 2013;Djoudi et al, 2021), Artificial Neural Networks (Benbouhenni, 2020), fuzzy sliding mode control (Benmeziane et al, 2019), H ∞ Controller (Saihi et al, 2020), etc.…”