# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Application of Artificial Intelligence in the Design of Wind Turbines
#### 3.1.1 Design Process  
The potential for using artificial intelligence (AI) to optimize the design of wind turbines is enormous. AI can improve various aspects of the design process, leading to more efficient and cost-effective wind turbines. The area of wind turbine design with the greatest potential for AI is the aerodynamic design of the rotor blades, as it directly influences the efficiency and energy output of a wind turbine. Using AI techniques and computational fluid dynamics (CFD), the shapes of the turbine blades can be optimized to minimize drag and turbulence while maximizing the energy yield of the turbine. AI can capture complex non-linear aerodynamic effects that may be overlooked in conventional design approaches, enabling more accurate modelling of the wind flow around the turbine blades. This is important for improving the efficiency of wind turbines under a wide range of environmental conditions, as it helps designers to select the best configurations for specific sites. [PERSON] et al. [21] report in their study that AI-optimized blades have a 15% better lift-to-drag ratio compared with conventional designs, based on CFD simulations. This leads to a 12% increase in performance in normal wind conditions. To test these results, a wind farm in a windy area was selected. Over a period of 6 months, the turbines with optimized blades generated 18% more power than those with conventional blades. According to [22; 23], the efficiency increase based on simulations is as high as 10-15%. AI can speed up the design process by enabling rapid simulations and evaluations of multiple design configurations. For example, advanced AI models can create new designs with improved aerodynamic properties in a fraction of the time compared with traditional methods. [PERSON] and [PERSON] [24] used CFD and ANN to optimize the aerodynamic design of a blade. To this end, they analyzed a large number of blade design variants (40,750 2D profiles). Among the optimal designs, a maximum performance increase of about 8% was found. The use of the ANN model required a relatively small number (163) of CFD analyses, resulting in a total calculation time of about 30 h. Without the use of the ANN model, 733 CFD evaluations would be required, resulting in an optimization time of approximately 135 h using the same computer resources. The integration of artificial intelligence into existing engineering tools improves the overall design process and enables more comprehensive optimization, taking into account factors as diverse as structural integrity, cost and environmental impact. In the structural optimization of wind turbines, AI can help in the development of strong and lightweight structures. This can reduce material and labor costs and improve the durability of turbines, making wind energy more competitive with other forms of energy. Deep learning models can predict the dynamic responses of floating wind turbine platforms, ensuring stability in the deep sea. For example, LSTM networks predict the tension of mooring lines under different wave loads [25]. In the generative planning of wind turbines, the designers first determine the most important design parameters, e.g., turbine output, materials, installation location, wind speed, noise, production costs and environmental impact. Then algorithms, often based on machine learning and evolutionary algorithms, are used to generate a variety of variants of the potentially optimal design. All structural parts of a wind turbine can be optimized, e.g., the rotor blades, the wind turbine tower, the wind turbine foundation, the transmission elements, the electrical generator and others. The most common aim of optimization is to increase the efficiency of the wind turbine (rotor blades, generator) or to reduce the amount of material used (tower, foundation) in order to keep material costs as low as possible, but also to facilitate transport and installation.