ANN-Based Direct Power Control for Improved Dynamic Performance of DFIG-Based Wind Turbine System: Experimental Validation

Direct power control (DPC) is a widely accepted control scheme utilized in renewable energy applications owing to its several advantages over other control mechanisms, including its simplicity, ease of implementation, and faster response. However, DPC suffers from inherent drawbacks and limitations that constrain its applicability. These restrictions include notable ripples in active power and torque, as well as poor power quality brought on by the usage of a hysteresis regulator for capacity management. To address these issues and overcome the limitations of DPC, this study proposes a novel approach that incorporates artificial neural networks (ANNs) into DPC. The proposed technique focuses on doubly fed induction generators (DFIGs) and is validated through experimental testing. ANNs are employed to recompense for the deficiencies of the hysteresis controller and switching table. The intelligent DPC technique is then compared to three other strategies: classic DPC, backstepping control, and integral sliding-mode control. Various tests are conducted to compare the ripple ratio, current quality, durability, response time, and reference tracking. The validity and robustness of the proposed intelligent DPC for DFIGs are verified through both simulation and experimental results obtained from the MATLAB/Simulink environment and the Real-Time Interface (RTI) of the dSPACE DS1104 controller card. The results confirm that the intelligent DPC outperforms conventional control strategies in terms of stator current harmonic distortion, dynamic response, power ripple minimization, reference tracking accuracy, robustness, and overshoot reduction. Overall, the intelligent DPC exhibits superior performance across all evaluated criteria compared to the alternative approaches.