Cost Efficiency and Predictive Asset Management of Wind Turbines Through ANN Surrogate Modeling with Slotted Blade

Horizontal axis wind turbines (HAWTs) are essential for renewable energy production, making the optimization of their aerodynamic performance crucial for enhancing both operational efficiency and long-term asset management. This study investigates passive flow control methods, specifically the integration of slotted blades, to manage flow separation and improve wind rotor efficiency. Using a Design of Experiments (DoE) methodology, the study optimizes design variables to achieve superior aerodynamic performance. Given the high computational costs associated with traditional computational fluid dynamics (CFD) simulations, an artificial neural network (ANN)-based surrogate model is developed to predict CFD outputs more efficiently, facilitating faster identification of optimal design configurations. The analysis reveals that the distance from the suction side (Ds) is the most influential factor, accounting for 80% of the aerodynamic performance. The optimized slotted blade achieves a maximum lift-to-drag ratio of 23.0, reflecting a 12% improvement over the baseline blade at an angle of attack of 14°. This approach underscores the effectiveness of integrating slotted blades in enhancing turbine performance and its potential role in predictive maintenance and intelligent asset management. By stream lining the design and optimization processes, the ANN surrogate model supports more efficient maintenance practices and improves the overall sustainability and competitiveness of wind energy systems. The advanced slotted blade boosts aerodynamic efficiency, minimizes maintenance demands, increases asset durability, and decreases operational expenses, offering an economical solution for wind energy applications.