# 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
When optimizing the blade profiles, AI algorithms take into account the given parameters and try to minimize drag and turbulence, increase the lift coefficient [26] and maximize the output energy for different environmental conditions. Each design created isthen tested using computational fluid dynamics (CFD) simulations to evaluate its performance in real-world conditions. The simulations may include aerodynamics, structural analysis, noise calculations and other relevant factors. Based on the simulation results, the algorithms create new, improved designs. This process is repeated several times, iteratively improving the geometry of the wings and other structural elements until the optimal solution is achieved. Research shows that AI-powered design can lead to next-generation turbines with improved wind energy utilization, which is key to advancing sustainable energy solutions [22]. For example, a wind turbine called the Birmingham Blade, which was developed by the British company EvoPhase\({}^{+}\) in collaboration with KwikFab (Birmingham, UK), was designed with the help of artificial intelligence. The wind turbine is said to be able to generate seven times more energy in urban conditions than conventional models (not experimentally verified). This design was specifically developed to capture the lower wind speeds typical of urban environments (approx. 3.6 m/s) and to cope with the turbulence caused by the surrounding buildings [27]. The National Renewable Energy Laboratory (NREL), as part of the INTEGRATE project, is developing the next generation of aerodynamic tools for creating 2D airfoils and 3D designs of wind turbine blades using a specialized invertible neural network (INN) architecture that learns the complex relationships between blade airfoil shapes and their associated aerodynamic and structural properties [28]. Genetic algorithms (GAs) and particle swarm optimization (PSO) have been found to be particularly effective in dealing with large search spaces in the design of aerodynamic blades where multiple interdependent variables (e.g., blade chord length, twist distribution, blade shape) need to be optimized simultaneously [22; 23]. Genetic algorithms are based on a population-based approach that performs random selection and recombination of design candidates, enabling a robust global search that prevents premature convergence to local optima [29]. These algorithms allow engineers to incorporate different objective functions--such as maximizing the lift-to-drag ratio while minimizing material consumption--and can thus propose a range of near-optimal designs for different wind conditions. However, the computational cost of GAs can quickly skyrocket when very large populations or extensive generations are required. In the context of rotor blade aerodynamics, the computational cost increases when high-resolution computational fluid dynamics (CFD) simulations need to be integrated into each iteration of the optimization loop [21]. Particle swarm optimization, on the other hand, uses a swarm of solution candidates that exchange velocity and position information with each other, which leads to accelerated convergence in many aerodynamic optimization scenarios [21; 22]. PSO heuristics have been shown to be particularly valuable for wind turbine blade design when fewer design parameters (e.g., limited sets of cross-sectional profiles or simplified chord-twist parameterizations) need to be tuned. Comparative studies have shown that PSO can converge faster than GAs in certain aerodynamic contexts, although it may have a tendency to be trapped in local optima if swarm diversity is not well maintained [29]. In blade design, the fast convergence of PSO can be advantageous when a large number of iterative CFD evaluations are computationally intensive. However, the relative risk of partial convergence may require adjustments such as time-varying inertia coefficients or hybridized swarm intelligence.