Revolutionizing Wind Energy: A Enhanced Machine Learning Perspective on Wind Turbines Fault Prediction

The creation of renewable energy relies heavily on wind turbines, yet undiscovered defects can reduce its performance. This study uses an ensemble methodology to propose a reliable failure detection and diagnosis (FDD) model for wind turbines. The model combines the benefits of Random Forest, Gradient Boosting, and Support Vector Classifier (SVC), three machine learning techniques. The suggested model was put to the test in three scenarios – the best, worst, and average ones-to determine its dependability. Surprisingly, an accuracy of 0.9963 was attained, demonstrating its exceptional ability to locate and diagnose errors. This comprehensive approach guarantees the FDD system’s longevity across a range of operating conditions while also increasing accuracy. The combined effect of these three models provides a feasible solution to guarantee the longevity and efficiency of wind turbines, paving the door for more resilient and trustworthy renewable energy infrastructures.