Leveraging advanced artificial intelligence techniques for accurate fault prediction in wind turbines

Mechanical defects often risk performance and reliability in Wind Turbines (WT), which hold critical importance in the renewable energy market. Early detection of such issues is invaluable in reducing downtimes and maintenance costs. Conventional monitoring methods are often unable to evaluate most failures as they develop or accurately predict their incidence, forming the basis for this research. Sophisticated artificial intelligence (AI) algorithms have been introduced in this study to enable reliable fault prediction in WT. Hence, the research proposes a unique approach called Extreme Gradient Boosting with Dynamic Skill Optimization (XGBoost-DSO) to analyze historical and real-time data obtained from WT sensors. This approach identifies patterns that indicate upcoming errors, allowing proactive maintenance. The dataset for fault prediction includes various sensor data from WT components, such as vibrations, temperature, performance indicators, and environmental factors. Data per-processing involves min-max normalization, and feature extraction is performed using the Wavelet transform. The results demonstrate that the AI-based fault forecasting system significantly outperforms traditional methodologies, achieving greater accuracy of 97.8%, precision of 96.2%, recall of 98.5%, F1-score of 97.3%, AUC of 96.2%, and MCC of 72.84%. The system ensures faster fault detection and can predict errors several days in advance, enabling preventive maintenance. By integrating sophisticated AI techniques for fault prediction, this study significantly enhances the dependability, efficiency, and sustainability of WT. Future research could explore incorporating real-time adaptive learning models and multi-algorithm integration to further improve predictive accuracy and operational resilience in dynamic environments worldwide. Additionally, expanding the dataset to include more diverse environmental conditions and turbine models could enhance system robustness and reliability.