# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 5 Analyzing Influencing Parameters in Wind Energy with ML
### Detailed Literature Content Review of ML Applied in Wind Energy Optimization Process
[120], the optimization of VAWT was proposed using an ML and optimized torque control based on wind speed estimation [121]. Optimization methods such as neural networks and genetic algorithms reduce computational costs and enhance the performance of diffuser-augmented turbines [122]; \(\circ\) Eight papers present innovative studies, which include new methods and advanced techniques. These studies have proposed solutions, such as integrating an ANN-based framework for power and turbine lifespan prediction. This was made possible through a two-stage control system that extends the lifespan of wind farms with minimal power reduction [123]. Additionally, location-specific wake steering techniques and ML models were introduced to improve energy production and reduce fatigue loads [124]. Flow control in wind farms using reinforcement learning (RL) has demonstrated improvements in energy production and a reduction in computational costs [125; 126]. Using computational fluid dynamics (CFD) and ML, aerodynamic optimization and noise reduction for vertical Savonius rotors bring notable performance improvements [127]. * Twelve papers address the issue of fault detection and maintenance from the perspective of ML and optimization of wind power production processes. These are divided according to the previously used categories as indicated next:A case study by [PERSON] et al. [97] synthesized materials from the literature on fault detection and diagnosis in wind turbines so that the condition of components can be predicted and significant failures can be prevented before they affect performance. The article [97] analyzed various predictive maintenance approaches that use data collected from sensors and supervisory control and data acquisition (SCADA) systems to predict issues before they materialize and to determine the necessary corrective actions; Seven articles present case studies discussing the application of ML IoT technologies for improving the performance and maintenance of wind turbines [128]. The studies explore the use of ML for analyzing turbine manufacturing operations [129], energy prediction [98], fault diagnosis [130; 131], and anomaly detection based on power curves and ensemble learning [132]. Additionally, methods are investigated for classifying imbalanced data for generator fault detection [133] and drivetrain fault diagnosis in turbines through advanced DL techniques combined with signal processing [134]; Four papers are positioned as innovative content articles. These present how ML and advanced technologies are applied to improve the efficiency of wind turbines and fault detection. The studies examine the use of ML for intelligent fault detection and turbine placement optimization [96] and for optimizing sensor node placement for anomaly detection [135]. Additionally, methods for automatically quantifying turbine blade edge erosion based on field images [136], fuzzy reliability assessment, and fault prediction using ML [137] are investigated. ML models address the issue of wind turbine placement by analyzing meteorological data (wind speed and direction, turbulence frequency), topography (terrain height, natural or artificial obstacles), and the interaction between turbines through the \"wake\" effect (the wind shadow area) created by neighboring turbines. Based on this data, ML techniques identify optimal turbine configurations. The goal is to maximize energy production. The results are obtained through simulations and adjustments based on continuous feedback from experimental data. From these considerations, the necessity of a large volume of data that can be analyzed arises. * Seven papers investigate wake effects and airflow control, being divided as listed below: \(\circ\) Three articles are review-type. The study by [PERSON] et al. [138] explores ML applications in wind energy, [PERSON] et al. [139] detail recent advances in ML applications for variable renewable energies, and [PERSON] [140] focuses on ML applications for offshore wind farms; \(\circ\) A case study by [PERSON] et al.