# Application of Artificial Intelligence in Wind Power Systems
## 7 Discussion
These results confirm that data-driven AI models trained on supervisory control and data acquisition (SCADA) streams can detect subtle anomalies in temperature, vibration and torque signals that may not be detectable using traditional threshold-based methods. In addition, drone-based inspections using image segmentation and deep learning have shown promise in assessing the integrity of the rotor blade surface, reducing the need for labor-intensive manual inspections in difficult or remote environments. A final but increasingly relevant aspect is the end-of-life phase of wind turbines. AI-driven methods are known for their potential to optimize decommissioning plans and material recycling processes. These include analytical models that estimate the remaining lifetime of turbines based on operational data, as well as solutions that help dismantle and maximize material recovery from blades, towers and generators. Although such end-of-lifeapplications are not yet widespread, they are likely to become increasingly important as more and more first-generation wind turbines are retired. Despite impressive progress, algorithmic improvements are still essential. Challenges include ensuring data quality, especially for offshore wind turbines where the error rate of sensors is higher, as well as managing heterogeneous data collected by different turbine manufacturers. In addition, the interpretability of black box models is questioned, especially for critical decisions on grid stability or large-scale offshore developments. Cybersecurity risks are also high, as the use of AI and connectivity with the Internet of Things (IoT) can expose wind farms to targeted attacks if robust encryption and intrusion detection protocols are not used. Future advances are expected in hybrid AI, where physics-based modelling is integrated with advanced machine learning to improved generalizability under rare or extreme weather conditions. The increasing use of explainable AI (XAI) methods is expected to improve transparency and compliance, thereby increasing acceptance by grid operators and environmental stakeholders. Multi-agent systems developed for large offshore clusters can further improve synergy between neighboring turbines, optimizing wake control and joint resource management. These ongoing developments will improve operational efficiency, extend system lifetime and improve the economic viability of wind power projects worldwide. Algorithmic biases, data integrity issues, cybersecurity vulnerabilities and the complexity of AI models when integrated into the real world were each cited as potential obstacles. In addition, ethical considerations related to accountability and transparency in algorithmic decision-making remain unresolved. As wind turbine control systems become increasingly autonomous, safety and liability considerations will require additional clarity in the legal framework. Finally, it was emphasized that AI solutions themselves consume significant computing resources, resulting in an additional energy burden and necessitating careful lifecycle analysis of these digital technologies.