Analysis of a 100 kW Wind Turbine System

As the global effort to reduce reliance on fossil fuels accelerates, the development of renewable energy technologies has become increasingly critical. Wind energy continues to be a major contributor to the renewable energy sector and is expected to account for 35% of global energy production by 2050. However, enhancing efficiency and reducing the cost of wind energy remains a challenge, especially in lower-wind regions. This paper will explore the application and bio-inspired turbine designs, machine learning techniques, and optimization methods to improve wind turbine efficiency and performance. A comparative analysis between conventional and bio-inspired turbines demonstrates up to 33% improvement in computational fluid dynamics (CFD) and blade element momentum theory (BEMT) simulations. Additionally, AI has shown remarkable potential in wind speed prediction, design optimization, fault detection, and maintenance planning, enhancing the viability of wind energy applications. The transition to renewable energy also improves energy security by reducing dependence on fossil fuels, but it also introduces new challenges, such as large required investments in infrastructure. This study will highlight innovative approaches to improving wind turbine design, maximizing wind energy production, and strengthening energy security.