No abstract

This paper presents a new contribution of the nonlinear control technique of electrical energy in a wind energy system. The nonlinear sliding mode technique used to control the powers of the DFIG-Generator is connected to the power grid by two converters (grid side and machine side). The proposed model is validated using tracking and robustness tests with a real wind speed. The control was developed under Matlab/Simulink, and the FPGA in the Loop technique was used to design the DFIG model. By employing a co-simulation, the purpose is to test the controller for the FPGA simulated model or system in its entirety. The results obtained by the cο-simulation show the efficiency of the proposed model in terms of speed and robustness with a rate THD = 0.95, and the proposed model of the sliding mode controller shows a significant improvement in the quality of energy produced by the wind system.

There has always been a high expectation that wind generation systems would capture maximum power and integrate properly with the grid. Utilizing a wind generation system with increased management to meet the growing electricity demand is a clever way of accomplishing this. However, wind power generation systems require a sophisticated, unique, and dependable control mechanism in order to achieve stability and efficiency. To improve the operation of the wind energy conversion method, researchers are continually addressing the obstacles that presently exist. Therefore, it is necessary to know which control can improve the whole system’s performance and ensure its successful integration into the network, despite the variable conductions. This article examines wind turbine control system techniques and controller trends related to the permanent magnet synchronous generator. It presents an overview of the most popular control strategies that have been used to control the PMSG wind power conversion system. Among others, we mention nonlinear sliding mode, direct power, backstepping and predictive currents control. First, a description of each control is presented, followed by a simulation performed in the Matlab/Simulink environment to evaluate the performance of each control in terms of reference tracking, response time, stability and the quality of the signal delivered to the network under variable wind conditions. Finally, to get a clear idea of the effect of each control, this work was concluded with a comparative study of the four controls.

For many academics, it has proven difficult to operate a wind energy conversion system (WECS) under changeable wind speed while also enhancing the quality of the electricity delivered to the grid. In order to increase the effectiveness and performance of the DFIG-based Wind Energy Conversion System, this research suggests an updated model predictive control technique. This study intends to regulate the generator in two ways: first, to follow the reference wind speed with high precision using the rotor side and grid side converters; second, to reduce system error. The suggested approach optimizes a value function with current magnitude errors based on the discrete mathematical model to forecast the converter’s switching state. In this system, the converter switching states are used directly as control inputs. Thus, the converter may be immediately subjected to improved control action. The key advantage of the suggested strategy over current FCS-MPC methods is error reduction. The originality of this research is in the proposal of a cost function that allows for both successful results and computation time minimization. To achieve this, the system is first presented, followed by a description of the predictive control, and then this method is applied to the rotor side control and grid side control. To demonstrate the efficacy and robustness of the suggested technique, a random wind profile was used to examine the system’s performance with a unitary power factor. This was done in order to compare the results with other controls that have been reported in the literature. The simulation results, which were conducted using a 1.5 kW DFIG in the MATLAB/Simulink environment, demonstrate that the FCS-MPC technique is highly effective in terms of speed, accuracy, stability, and output current ripple.

No abstract

No abstract

No abstract

No abstract