# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Wind Turbine Maintenance  
One of the key areas in which artificial intelligence (AI) is expected to make the greatest contribution in wind farms is predictive maintenance. Predictive maintenance uses AI to analyze large amounts of sensor data and historical maintenance records to identify patterns and predict potential turbine failures before they occur. This capability is critical for several reasons, as follows:  
* Minimizing downtime: By detecting problems early, predictive maintenance reduces unplanned outages and ensures turbines operate at maximum efficiency and reliability. * Cost savings: Early detection of failures enables timely repairs and prevents costly damage to critical components. By accurately predicting maintenance needs, AI can help avoid unnecessary maintenance activities and focus resources on critical issues. This leads to significant cost savings and more efficient operations. The National Renewable Energy Laboratory (NREL) states that operation and maintenance (O&M) costs account for about one-third of the total lifecycle costs of wind farms. In other words, they amount to USD 15-27/kW/year for onshore wind farms and USD 40-60/kW/year for offshore wind farms [70]. The study by [PERSON] et al. states that O&M costs account for about 19% of the total LCC for wind farms [71], and [PERSON] et al. [72] highlight that operation and maintenance costs generally constitute approximately 20% to 25% of the total life cycle cost, both of which are values that are lower than those reported by NREL. Despite these differences, these sources emphasize the importance of effective maintenance strategies to increase profitability. Regular inspections and repairs help prevent major outages and costly repairs. Fewer breakdowns mean fewer power outages, which leads to higher revenues. * Improved safety: Automated condition monitoring of wind turbines reduces the need for manual inspections at hazardous or remote sites, reducing the risk of industrial accidents while maintaining operational efficiency. * Extended turbine lifetime: Ultimately, regular maintenance extends the lifetime of wind turbines, meaning the wind turbine produces more clean energy, which in turn contributes to lower greenhouse gas emissions and a positive impact on the environment. According to some analyses, AI can extend the service life of wind turbines by up to 10% [47]. Awareness of the importance of wind turbine maintenance has led to an 87% increase in the number of scientific papers on this topic between 2007 and 2019. As a result of increased research, the LCOE of onshore wind projects has fallen by 45%, while it has fallen by 28% for offshore projects [70]. The application of artificial intelligence (AI) in wind turbine maintenance has evolved over the past decades, with significant progress being made in recent years. Although the first mention of AI in wind turbine maintenance is not clearly documented, a literature review shows that AI applications in wind turbine technology have been used on a larger scale since around 1980 [10]. The emergence of the concept of predictive maintenance has significantly accelerated the application of AI to analyze wind turbine data to predict failures before they occur. In the last decade, most existing studies have utilized signal processing or physics-based numerical models for CBM in the context of turbine condition monitoring, using vibration data in particular for this purpose [15; 73; 74]. [PERSON] and [PERSON] [15] are of the opinion that, despite the increasing use of AI in the wind industry, more traditional techniques, such as those based on signal processing, will continue to complement AI models in this rapid transition. They also emphasize the need to ensure the quality of data and focus on more sophisticated and customized AI algorithms, especially using deep learning and natural language generation techniques for explainable AI.