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.

As wind energy continues to play a significant role in the global energy mix, ensuring the efficiency, robustness, and stable operation of Wind Energy Conversion Systems (WECS) has become a critical concern. This highlights the need for advanced control strategies to enhance power regulation, improve dynamic performance, and ensure reliable grid integration under variable wind conditions. Among the proposed approaches, Direct Power Control (DPC) has attracted significant attention for its simplicity and fast dynamic response, making it suitable for real-time implementation in grid-connected applications. However, despite its advantages, conventional DPC suffers from several limitations, including high power ripples, increased Total Harmonic Distortion (THD) in currents and voltages, and variable switching frequency, which can significantly affect overall system performance and power quality. To address these limitations, this paper proposes an Advanced Predictive Direct Power Control (APDPC) strategy for grid-connected WECS based on a permanent magnet synchronous generator (PMSG). The PMSG is selected for its high efficiency, compact structure, and operational flexibility, enabling robust and coordinated control of both the Generator-Side Rectifier and the Grid-Side Inverter. The proposed APDPC is implemented for both converters within a unified control framework, ensuring seamless power flow regulation. Unlike the conventional DPC approach, at each sampling period, three voltage vectors are directly selected based on the predicted evolution of active and reactive powers, without the need for complex control loops, hysteresis controllers, switching table, or exhaustive evaluation of all candidate switching states. The corresponding application times of these vectors are subsequently optimized using a multi-objective cost function that simultaneously minimizes the instantaneous errors in active and reactive powers on both sides of the system. To assess its effectiveness, the proposed control strategy was validated through MATLAB/Simulink® simulations under both constant and variable wind speed conditions. The results clearly demonstrate a substantial enhancement in dynamic performance compared with conventional DPC, including a 37% faster response, complete elimination of overshoot and undershoot, and a 61% reduction in steady-state error. In addition to these dynamic improvements, the proposed APDPC significantly enhances power quality, achieving reductions of approximately 40–45% in torque and power ripples, along with up to a 50% reduction in the THD of the grid current and grid voltage. These findings are further reinforced by comparative analysis with recent control strategies, highlighting its superior tracking accuracy and overall performance. Finally, real-time implementation on the dSPACE DS1104 platform confirms the practical feasibility and effectiveness of the proposed approach under realistic operating conditions.