# 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
Neural networks (NNs), including deep neural networks and convolutional neural networks (CNNs), offer another advantage in aerodynamic optimization: they can learn high-dimensional mapping functions that approximate fluid dynamic responses (e.g., lift, drag, and pressure distributions) for multiple blade geometries [30]. When trained with either numerical simulation data or empirical wind tunnel data, these models can perform rapid assessments of aerodynamic performance without the need to repeatedly invoke full-order CFD solvers. Therefore, NNs reduce the computational burden by acting as surrogates or meta-models. Once a neural network surrogate is created, gradient-based or gradient-free optimizers can be used on these learned representations to identify optimal blade shapes [27; 30]. This approach accelerates the design cycle, especially when a large parameter space (e.g., multi-point aerodynamic conditions, different Reynolds numbers, or advanced composite materials) must be considered. However, NNs require carefully curated and sufficiently large training data sets. If the training data are sparse or not representative of the entire aerodynamic domain, extrapolation errors and model inaccuracies may occur. In practice, a combined or hybrid approach is often chosen. Researchers have integrated neural networks as surrogate models in genetic algorithms or PSO systems to reduce the computational burden of aerodynamic blade calculations [22; 30]. Such hybridizations exploit the global search capabilities of evolutionary algorithms together with the high-speed evaluations of machine-learned surrogate models. Studies have shown that, when the neural network models accurately capture the aerodynamic nonlinearity and the GA or PSO components preserve the robustness of the search, the overall design time can be significantly reduced--sometimes by more than 40%--with negligible trade-offs in the quality of the blade geometries found [22; 27; 30]. In summary, genetic algorithms are well suited for comprehensive global exploration but can be computationally expensive, particle swarm optimization offers faster convergence at the risk of being locally stagnant, and neural networks enable fast aerodynamic evaluations that depend on the amount and accuracy of available training data. The optimal choice of these methods depends on the complexity of the blade's parameter space, the underlying aerodynamic modelling fidelity, and the computational resources required to achieve a highly accurate blade design. [PERSON] et al. [31] argue that the optimal design of a wind turbine is influenced by various factors, such as blade profile, number of blades, power factor and tip speed ratio (TSR). They investigated different AI techniques, such as support vector machine (SVM), relevance vector machine (RVM) and genetic programming (GP), for the optimal design of wind turbines and came to the conclusion that the TSR is crucial for the design of wind turbines. In addition to the rotor blades, the tower also plays an important role in wind turbines. The base diameter, the upper diameter and the tube wall thickness of tubular steel towers are design parameters that strongly influence two contradictory optimization objectives: mass and top deflection. In addition, the solutions must fulfil the requirements of natural frequency, stress and buckling. In their work, [PERSON] et al. [32] use convolutional neural network (CNN), back propagation neural network (BPNN) and support vector machine (SVM) algorithms to optimize the tower. They found that the CNN algorithm proved to be the best. [PERSON] et al. [33] presented a method to determine the optimal design of steel towers for wind turbines using artificial neural networks (ANNs) and the MOPSO algorithm. The optimization approach aimed to achieve three design goals: maximizing structural reliability, minimizing structural mass and maximizing wind energy utilization. Artificial intelligence can help to design a cost-effective and structurally stable foundation for wind turbines.