# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines
### The Latest Technological Advancements in Turbine Design Optimization  
Technological advancements in various fields have led to the concept of interdisciplinarity. The literature also offers multidisciplinary elements in other fields that are different from AI. Wind energy research encompasses a variety of interconnected domains, including material science innovations [76], economic modeling [77], policy development, social acceptance studies, material science innovations, etc. For example, the paper [78] presented interdisciplinary perspectives on the socio-economic and technological factorsthat influence wind energy development. The paper by [PERSON] et al. [79] addressed design directions regarding anchoring systems. Among all the expansion directions of multidisciplinary research on optimizing wind energy processes, the AI direction is the most popular due to the integration of intelligent components in all fields of activity. Thus, revolutionary concepts such as digital twin, dynamic airflow control, wind turbines with active blades and innovative materials, distributed control systems and smart microgrids, and wake steering have been introduced. Figure 1 synthesizes the interdisciplinary approach to optimizing wind energy. This synthesis highlights the importance of integrating modern technologies across different fields. Centered on wind energy, the scheme highlights the connections between interdisciplinary fields (such as innovations in materials science, economic modeling, policy development, and social context) and advanced solutions like digital twin, Internet of Things (IoT) systems, dynamic air control, smart microgrids, smart grids, and distributed control systems. These elements contribute to optimizing wind turbine performance through mechanisms such as active blade control (adaptive blade control) and wake steering (optimizing turbine positioning to reduce interference between them). The authors emphasize that ML is a key tool that integrates these components to identify predictions as close to reality as possible, early defect detection, and wind farm management strategies. The digital twin concept creates a digital replica of wind turbines. This concept allows for the real-time simulation of operational scenarios to prevent defects. By integrating ML models, these systems can identify hidden patterns in the historical data. The patterns determine the adjustment of operating parameters, which reduces long-term wear [80; 81; 82]. Active blade control represents adaptable blades that can change their shape in real time to optimize energy capture. This concept is studied in the literature [83; 84; 85] to identify the materials that reduce weight and increase efficiency, such as carbon fiber composites or nanomaterials. The sensors integrated into the blades allow for real-time monitoring of stresses and vibrations, which generates a considerable volume of study material in the literature. However, searches for \"Active Blade\" and \"Machine Learning\" in Web of Science (WOS) yield no results, indicating that the field has not yet been explored. Additionally, dynamic airflow control represents another important innovation. The turbines have advanced sensor and actuator systems that adjust the blades according to  
Figure 1: Smart technologies and interdisciplinary solutions for optimizing wind energy systems. wind variations. These technologies optimize the angle of attack of the blades and the distribution of aerodynamic forces to maximize energy capture and reduce mechanical stresses. Dynamic airflow control is not explored in the specialized literature in combination with ML elements for the analysis of optimizing wind energy production. Distributed control systems and smart microgrids aim to decentralize turbine control to optimize wind farm energy flow. These systems are integrated with various energy storage elements [86,87]. When combined with blockchain technology for decentralized energy management, these technologies can help manage transactions between producers and consumers.