# Application of Artificial Intelligence in Wind Power Systems
## 7 Discussion  
A critical observation from the literature and the data reviewed is that AI is increasingly being used to optimize the design of wind turbines, with particular attention being paid to rotor blades, tower structures and foundations. The aerodynamic shape of rotor blades has been consistently identified as critical to wind turbine performance, as optimized blade geometry can lead to significant improvements in energy harvesting and efficiency. It was found that, by applying computational fluid dynamics (CFD) simulations in conjunction with AI-driven design iterations, annual energy production can be increased by 6% to 15% compared with conventional approaches. Despite the differences in reported performance gains, several studies consistently indicate that neural networks and evolutionary algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO), have been shown to be effective in optimizing multivariable design objectives. These objectives include maximizing aerodynamic efficiency, minimizing structural mass and ensuring robust mechanical integrity. The importance of local wind conditions has also taken center stage. AI-based models can integrate site-specific meteorological, topographical and environmental data to adapt the turbine design to local wind conditions. Such an approach departs from the traditional 'one size fits all' design philosophy and instead utilizes advanced machine learning techniques--namely convolutional neural networks (CNNs), support vector machines (SVMs) and hybrid algorithms--to identify the wind speed distributions, turbulence intensities and wind directions characteristic of each site. This capability has further underlined the usefulness of AI in site selection and land use optimization based on geographic information systems (GIS). Here, multi-criteria decision analysis can be coupled with machine learning to identify the most suitable areas for the installation of wind farms. In the operational phase, one of the biggest challenges is the volatile nature of wind energy, which makes it difficult to stabilize the electricity grids. AI-based forecasting has already been widely used to predict energy generation several hours or days in advance. Models based on deep neural networks, including long short-term memory (LSTM) and gated recurrent units (GRUs), have been shown to reduce prediction error more effectively than classical statistical or purely meteorological methods. The ability of these AI models to process large, high-frequency data sets emphasizes their utility for short-term operations and real-time management. However, it has been shown that predictive modelling is only one aspect of improving operations. Equally important is the dynamic control of turbine parameters. Sophisticated AI algorithms ensure that wind turbines can respond adaptively to rapidly changing environmental conditions through real-time adjustments to blade pitch, nacelle yaw angle and rotation speed. This approach not only improves the annual energy yield, but also reduces mechanical stress on key components, thereby extending the turbine's service life. In fact, fuzzy logic, neuro-fuzzy controllers and hybrid control strategies (e.g., ANFIS-PI) have been used to provide robust responses for doubly fed induction generators. This shows that AI is capable of increasing energy yield while maintaining grid stability. Maintenance has proven to be one of the most fruitful areas for the use of AI, especially in the context of predictive and condition-based maintenance. As maintenance costs can account for a significant portion of a wind farm's total lifecycle costs, early detection of faults in the gearbox, generator and pitch system can prevent catastrophic component failures and reduce downtime. Several studies have reported that advanced classifiers--random forest (RF), extreme gradient boosting (XGBoost) or extra trees--can detect and classify faults with high accuracy, with detection rates sometimes exceeding 80% for certain fault modes.