This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.

This paper proposes an Artificial Neural Network (ANN) based Direct Torque Control (DTC) technique of a doubly fed induction generator (DFIG) used in the wind power generation applications. This new intelligent approach is proposed to improve the classical DTC. The main objective of this intelligent technique is to replace the conventional switching table by a voltage selector based on (ANN) in order to reduce torque and flux ripples. The maximum power point tracking (MPPT) technique is used for maximum power extraction and the pitch control is also presented to limit the generator power at its rated value. The simulations results show the performance and efficiency of the proposed control strategy. These simulations results are confirmed by using the MATLAB/Simulink environment.

The energy generated using a photovoltaic (PV) are depends mainly on meteorological factors such as temperature and solar radiation. Considering the high cost and the low efficiency of a PV system, it must operate at maximum power point (MPP) which changes with the load variations and weather conditions. This article presents the conception of a nonlinear controller for the DC-DC converter to retrieve a maximum power from photovoltaic systems (PV). This is affected via the control of duty cycle of the converter. The backstepping control is used to design the controller. The incremental conductance algorithm provides the output reference voltage PV to the backstepping controller. An integral Action is added in order to render the controller robust against changes of parameters and perturbations. The proposed controller performance is assessed by numerical simulations under changing atmospheric conditions.

The present paper investigates an advanced control system of the wind energy conversion using an Artificial Neural Network (ANN) controller, which is developed in two modes: the offline mode, required for testing different sets of neural network parameters to find the optimal neural network controller (structure, activation function, and training algorithm), and the online mode where the optimal ANN controller is used in the five-phase PMSG based wind turbine system. The required data to generate the ANN model is obtained from a conventional PI controller. The controller inputs are the error between reference and actual speed (e), and the error change (de = e(k)-e(k-1)). As for the ANN output, it is the decreasing or increasing electromagnetic torque (dTe*). The studied neural network controller has been tested and validated using different wind speed values. Results and analysis are presented, and the contribution has been demonstrated.