# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Wind Turbine Maintenance
Regarding the scope of application of AI in wind turbine maintenance management, according to [PERSON] and [PERSON] [23], 25% of applications focus on optimization of any kind (cost, maintenance, route planning, etc. ), 16% of applications are related to fault detection and the same number to decision making. Planning and scheduling waste is the focus of 9% of applications, condition monitoring 8%, maintenance 8%, etc. The most widely utilized AI techniques for wind turbine maintenance include artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), fuzzy logic, statistical methods, and decision-making techniques. Among these, ANN and its variants stand out as the most versatile, as they are applicable to monitoring, optimization, data prediction, and decision-making tasks. GA and PSO are primarily employed for optimization and decision-making, as these algorithms were specifically designed to optimize systems with multiple variables. Fuzzy logic is predominantly used for decision-making and risk mitigation, incorporating factors such as cost and component reliability. Statistical methods are mainly applied for maintenance and fault prediction, leveraging large datasets to generate accurate estimates. The detection of faults in the pitch system of wind turbines plays a crucial role in the efficient and reliable operation of wind turbines. Therefore, [PERSON] et al. [75] tested sixteen artificial intelligence (AI) classification models for detecting faults in the pitch system of wind turbines. The random forest classifier (RF) and extra treesclassifier (ET) models showed the best performance, while the models with the lowest performance were the K-nearest neighbor classifier (KNN) and linear discriminant analysis (LDA). The average hit effectiveness for the modes \"healthy\" and \"faulty\" was about 80% for most of the developed models. The rotor blades of wind turbines are the most important component of wind turbines that enable the utilization of wind energy. Damage or breakage to the blades has a direct impact on the operation of wind farms. It is therefore extremely important to monitor changes in the blade surface over time. Advances in drone technology and artificial intelligence (AI) make it possible to capture and analyze numerous high-resolution images of rotor blades. However, images of wind turbine blades taken while the rotors were spinning resulted in a complex background in the blade images, with the surface features of the blades being relatively small. In their work, [PERSON] et al. [76] applied a deep learning segmentation method to segment rotor blade images captured by drones and eliminate the influence of the image background. A Siamese convolutional neural network (S-CNN) was used to recognize individual blades captured by drones and compare them with a reference blade image. [PERSON] et al. [77] assessed two models for detecting faults in wind turbine blades. The first employed logistic regression, which proved superior to Naive Bayes, decision trees, and neural networks, showcasing its efficacy in identifying fault-related patterns. The second utilized clustering techniques and achieved higher accuracy and improved data segmentation performance. [PERSON] et al. [78] used data on the predictive maintenance of wind turbines in Taiwan. On this basis, they developed a program for predicting wind turbine failures using a hybrid method that employs machine learning and deep learning. The random forest method is applied to identify features that are highly correlated with failures and to eliminate features with low correlation to maximize the performance of the model. The resulting failure prediction model provides an average prediction accuracy, precision and recall of 99%, 70% and 77%, respectively, for predictions from one to six hours ahead. [PERSON] et al. [79] developed a method for monitoring and detecting anomalies in critical components of a wind turbine, such as the gearbox and the generator. The approach is based on historical SCADA data.