Enhancing Wind Turbine Performance With Transformer‐Based Predictive Control and Quantum‐Inspired Optimization: A Novel Hybrid Model for Power Prediction and Control

ABSTRACT The research presents a novel approach that integrates Transformer‐based Predictive Control (TPC) and Quantum‐Inspired Optimization (QIO) to improve wind turbine efficiency. The TPC framework employs a Time‐Series Transformer (TST) model to predict turbine power output by learning temporal dependencies in wind speed, direction, and power fluctuations. Meanwhile, QIO—combining Quantum Evolutionary Algorithm (QEA) and Quantum Particle Swarm Optimization (QPSO)—optimizes turbine control parameters to achieve power maximization, stress reduction, and operational safety. Experimental findings show that the proposed TPC model enhances prediction accuracy with a 15% and 12% improvement in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), respectively, compared to traditional approaches. Furthermore, QIO increases turbine efficiency by 18%, reduces mechanical stress by 10%, and strengthens operational reliability. This integrated strategy represents a significant advancement in sustainable wind energy systems, delivering adaptive, intelligent, and efficient control for real‐world turbine operation.