Adaptive vector oriented control of doubly fed induction generator wind turbines using M5P model tree for robust dynamic performance.

Wind energy systems using doubly fed induction generators (DFIGs) rely on vector-oriented control (VOC) to achieve decoupled regulation of active and reactive power. Conventional VOC strategies typically employ proportional-integral (PI) controllers for rotor current control; however, these controllers struggle under nonlinear and variable operating conditions, such as wind speed fluctuations, leading to degraded dynamic performance and power quality. To overcome these limitations, this paper proposes an intelligent VOC approach based on the M5-Pruned model tree (M5P) algorithm, complemented by a comparative study with fuzzy logic controllers (FLC). The M5P-based controller introduces a data-driven mechanism that adapts to system dynamics, ensuring improved accuracy and robustness compared to traditional PI and FLC methods. A detailed MATLAB/Simulink simulation of a grid-connected wind turbine with DFIG evaluates the controllers under varying wind profiles. Results demonstrate that the proposed M5P strategy significantly reduces overshoot and settling time, enhances power regulation, and improves overall system stability. These findings highlight the advantages of machine learning-based control in addressing the shortcomings of conventional VOC techniques for renewable energy applications.