# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines
### Wind Power Turbines  
Wind turbines located in a geographic area are known as wind farms. Turbines on land are called onshore, while those at sea are called offshore [12]. [PERSON] [5] discussed how these turbines are arranged relative to one another in terms of array density, which determines production capacity. Consequently, production optimization refers to the physical distribution of turbines and operational parameters. Optimization must also account for minimizing the negative impact on grid stability and the environment [13]. Turbines are primarily categorized into horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs). HAWTs are commonly used for large-scale energy generation because they capture wind energy at higher altitudes. At the same time, VAWTs are utilized in smaller, localized settings where space or wind conditions are unfavorable to the former [14,15]. The performance parameters of wind turbines depend on various design features, including blade configuration and aerodynamics. For example, blade curvature and angle directly influence performance, as seen in studies comparing different blade designs, such as the [PERSON] and Darrieus turbines [15,16]. Modern HAWTs exhibit peak efficiencies through optimized blade designs that increase their ability to harness wind energy over a broader range of wind speeds [17]. Advanced materials and designs, such as blades with specific angles, can further influence the performance by reducing drag and maximizing thrust [18,19]. Utilizing available resources effectively aids in making informed decisions regarding the installation and operation of turbines [20,21]. Maintenance and fault detection influence wind turbine longevity and performance consistency. The literature analyzes various advanced techniques, including ML approaches for predictive maintenance and performance diagnostics [22,23]. [PERSON] et al. [24] and [PERSON] et al. [25] present the integration of self-diagnosis systems to predict potential failures in turbine components, optimizing maintenance schedules and reducing operational costs. Regarding the control of wind turbines, they are studied to minimize structural damage. [PERSON] et al. [26] propose an innovative control system based on active rotary inertia driver (ARID) and type-3 fuzzy logic system (T3-FLS). The authors optimized the management of wind turbine failures through fractional stability theorems. These approaches complement the load degradation reduction strategies described in the paper [27]. Climate and environmental factors impact wind turbine design and productivity. Turbulent wind conditions and extreme weather substantially affect turbine operations. The research has continued to explore structural adaptations and operational strategies to mitigate these challenges, ensuring reliable energy production even under adverse conditions [17; 28]. Achieving the optimization objective is intensely debated in the literature through the following desiderata [29; 30]:  
* Integrating wind power ramp control strategies with the ability to effectively manage fluctuations associated with changing wind conditions;
* Implementing advanced models and ML techniques for selecting wind turbine types based on specific site conditions and expected wind resource characteristics. These methodologies are associated with investments in technology and infrastructure to maximize the farm's efficiency. Most countries are attempting to adopt these sustainable measures to use renewable energy [31; 32]. Based on this plan, large-scale wind power generation requires plans and measures regulated by law at both the technological and environmental levels [33]. This is achievable through collaborations among academics, policymakers, the population, and the industry. This methodology will ensure long-term opportunities for innovation and environmental stewardship. Upholding economic and ecological standards will address the global demand for cleaner energy sources [34; 35]. Technological advances contribute to reducing carbon emissions.