Fault Diagnosis of Wind Turbines Using Machine Learning

Wind energy significantly contributes to renewable energy, with wind turbines as the core components. However, turbines face various faults, including generator heating, mains failure, feeding, air cooling, and excitation faults, leading to prolonged downtime and costly repairs, ultimately decreasing efficiency and profit margins for wind farms. This paper outlines a methodology for fault diagnosis and predictive maintenance of wind turbines using a dataset with turbine parameters and fault types. The dataset includes variables such as wind speed, rotation speed, power, temperature, and inverter mean and variances. The various machine learning methodology involves data preprocessing (cleaning, normalization, feature selection), followed by statistical analysis and pattern recognition to diagnose faults and predictive maintenance needs. A case study of a single turbine demonstrates the potential of this approach to enhance wind farm efficiency and profitability.