An Artificial-Neural-Network-Based Direct Power Control Approach for Doubly Fed Induction Generators in Wind Power Systems

The inherent complexity of wind energy systems has necessitated the development of sophisticated control methodologies to optimize operational efficiency. Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of energy conversion. This study focuses on the critical generator control block, where precise power management is essential to maintaining system stability and preventing operational disruptions. This research introduces an innovative ANN-based Direct Power Control (DPC) approach for a Doubly fed induction generator (DFIG) integrated into a wind power system, introducing a dual-MLP approach for precise power regulation. The proposed DPC-ANN controller proved effective in mitigating current ripples and achieving a near-unity power factor, indicating substantial improvement in power quality. Moreover, the spectrum harmonic analysis revealed that the controller yielded the lowest stator current total harmonic distortion of 1.29%, significantly outperforming traditional DPC-PI (2.76%) and DPC-Classic (2.24%) approaches. The proposed technique was rigorously implemented and validated using a real-time simulator (OPAL-RT) and MATLAB/Simulink (2020–2022) environment, specifically tested under a step wind profile. The real-time experimental validation highlights the practical applicability of this approach, bridging the gap between theoretical ANN-based control and real-world wind energy system implementation. These findings reinforce the potential of intelligent control strategies for optimizing renewable energy technologies, paving the way for more efficient and adaptive wind turbine control solutions.