Wind energy has many advantages because it does not pollute and is an inexhaustible source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is developed for Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT). The stator winding is directly coupled with the main network, whereas a Back-to-Back converter is installed to connect its rotor to the grid. The proposed control scheme is composed of Recurrent High Order Neural Network (RHONN) trained with the Extended Kalman Filter (EKF), which is used to build-up the DFIG models. Based on such identifier, the High Order Sliding Mode (HOSM) using Super-Twisting (ST) algorithm is synthesized. To show the potential of the selected scheme, a comparison study considering the NHOSM, Conventional Sliding mode (CSM), and the HOSM control is done. To ensure maximum power extractions and to protect the system, the Maximum Point Power Tracking (MPPT) algorithm and the h control are also implemented. Simulation results demonstrate the effectiveness of the proposed scheme for enhancing robustness, reducing chattering, and improving quality and quantity of the generated power.

This study proposes a real‐time sliding mode field oriented control for a doubly‐fed induction generator (DFIG)‐based wind turbine prototype connected to the grid. The proposed controller is used to track the desired direct current (DC) voltage reference at the output of the DC link, to maintain constant the grid power factor at the step‐up transformer terminals controlled by the grid side converter, and to force independently the stator active and reactive power to track desired values through the rotor currents controlled by the rotor side converter. This control scheme is based on a recurrent high‐order neural network (RHONN) identifier trained on‐line by an extended Kalman filter. The RHONN is used to approximate the DC link and the DFIG mathematical models. The adequate approximation helps to calculate the exact equivalent control part of the sliding mode controller and to eliminate the effects of disturbances and unknown dynamics appearing in the grid, which improves the robustness of the control scheme. This controller is experimentally validated on a 1/4 HP DFIG prototype and tested for variable wind speed to track a time‐varying power reference and to extract the maximum power from the wind, under both balanced and unbalanced grid conditions.

In this study, a Sliding Mode (SM) methodology combined with a robust H∞ control scheme (SM-H∞) was proposed to control the stator active and reactive power generated by the Doubly Fed Induction Generator (DFIG). The purpose of the proposed controller is to improve the DFIG stator active and reactive power tracking performances by reducing chattering phenomena under variable wind speed, which provides major drawbacks of conventional SM controllers. The H∞ technique was used to define the SM attractive control part, which helps to reduce chattering phenomena and improves robustness in the presence of parameter variations and wind speed changing. The DFIG stator was directly connected to the grid and, its rotor was linked to the grid through a back-to-back converter. The proposed approach was tested using Matlab/Simulink and a comparison with the conventional SM and the SM fuzzy logic controllers was carried out. The results of simulation illustrated an effectiveness of the proposed SM-H∞ controller even in the presence of the DFIG parameter variations and speed changing compared with the other techniques.

In this article, a neural sliding-mode linearization controller is proposed to regulate the generated active and reactive power for each distributed energy resource in a microgrid. The developed controller is based on recurrent high-order neural network identification, trained online with an extended Kalman filter learning algorithm. Based on such neural identification, adequate models of the microgrid generation units are obtained even in the presence of grid disturbances, which helps the proposed controller to reject disturbances, to ensure stability, and to operate the renewable energy sources under different grid scenarios. The proposed microgrid is composed of a wind power system, a solar power system, a battery bank, and a load demand. In addition, the microgrid under study is interconnected to an IEEE nine-bus system. The whole system is simulated in real time using the Opal-RT (OP5600) simulator. Real-time simulation results illustrate the effectiveness of the proposed control scheme to achieve trajectory tracking of the distributed energy resources active and reactive power even in the presence of grid disturbances.

Wind energy has many advantages, because it does not pollute and is an inexhaustible source of energy. The Double Fed Induction Generator (DFIG) is one of the most important generator used for Horizontal Axis Wind Turbine (HAWT). The DFIG stator is linked directly to the grid, whilst the DFIG rotor is coupled via an electronic converter. In this paper, a discrete-time Neural Sliding Mode (N-SM) Field Oriented Control (FOC) for DFIG stator active and reactive powers control is proposed. The adaptive nature of the proposed controller improves the transient response as compared with discrete-time Conventional Sliding Mode Control (C-SMC) for DFIG parameters variations and disturbances. The proposed controller is based on a Recurrent High Order Neural Network (RHONN), using an Extended Kalman Filter (EKF) as training law. The RHONN works as an identifier to obtain an accurate model, which is robust against disturbances and parameter variations. A comparison between C-SMC and N-SMC is included. The validity and the effectiveness of the proposed controller are tested using Matlab/Simulink simulation results.