Wind turbines have been deployed in both offshore and onshore locations around the world, representing a crucial component of renewable energy infrastructure. With the increasing diversity and geographical spread of wind turbine installations comes the significant challenge of understanding and mitigating weather effects on these systems' long-term operational life and performance capabilities. Weather conditions play a fundamental role in determining the power output of wind turbines. During periods of strong wind activity, substantially more electrical energy is generated compared to calm conditions when wind speeds are minimal or insufficient. Consequently, proactive maintenance strategies must be implemented for turbines experiencing prolonged periods of low wind exposure to minimize operational downtime and maintain efficiency standards.
A comprehensive understanding of how various weather patterns and meteorological phenomena affect wind turbine performance is essential for detecting potential mechanical and electrical failures before they occur, enabling timely maintenance interventions. Early detection and prediction of faults can help operators avoid the extremely high costs typically associated with major component failures in wind turbines, which can result in extended outages and expensive repairs.
This paper examines the relationship between weather conditions and wind turbine failures through advanced analytical methods. Principal Component Analysis (PCA) is employed to reduce the dimensionality of complex datasets and identify the most significant features influencing turbine performance. The Seasonal and Trend decomposition using Loess (STL) algorithm is applied to weather data to separate time series information into distinct trend, seasonal, and residual components. Through residual decomposition analysis, wind turbine failures were successfully identified, and early failure prediction was achieved within a timeframe ranging from seven to fifteen days prior to actual failure events, demonstrating prediction precision rates between 0.4 and 0.75.
