A Hybrid Artificial Intelligence-Driven and Genetic Algorithm-Based Optimization Framework for Enhancing Wind Turbine Performance with Structurally Defective Blades in Support of the Sustainable Development Goals (SDGs)

This study aims to enhance the performance of horizontal-axis wind turbines with structurally defective blades through a hybrid Artificial Intelligence-driven and Genetic Algorithm-based optimization framework. The research developed Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System models to predict power output and vibration levels. The models were integrated into a Genetic Algorithm to determine optimal pitch angles and rotational speeds. The framework resulted in maximized power generation and minimized vibration. The findings demonstrate that the combined models outperform traditional methods because they capture complex nonlinear interactions and support real-time control. This integration of science and technology concepts contributes to improving the operational efficiency and reliability of wind turbines while supporting the Sustainable Development Goals through renewable energy advancement.