Energy distance approach for the condition monitoring of multi turbines using distributional studies.

Abstract

This study presents the energy distance non-parametric approach for condition monitoring of multiple wind turbines within the same wind farm. The main objective of this work is to monitor the performance of wind turbines by comparing them against each other within the same farm. Traditional methods often rely on parametric assumptions, including normality, which are rarely satisfied in real-world applications. In contrast, the energy distance approach is fully non-parametric, does not assume normality, and is effective for distributions of any shape or location. It enables comparison at the distributional level without requiring explicit assumptions. To validate the proposed approach, SCADA data from a wind farm in Portugal was analyzed. Four wind turbines, including the unit operating in a fault condition, were comparatively analyzed using multiple parameters through the application of the Energy Distance method. The results demonstrate that this method provides a robust means of detecting faults earlier than traditional approaches, as faulty turbines show clear deviations from healthy turbine states. The analysis considered multiple parameters, including generated power, wind speed, hydraulic oil temperature, gearbox oil temperature, generator bearing temperature 2, gearbox bearing temperature, and others. Overall, the results of the study clearly indicate that when comparing the four wind turbines using the energy distance method, the turbine with a fault exhibits a significantly larger energy distance relative to all other turbines. This distinct difference effectively distinguishes the faulty turbine from the healthy ones. These findings confirm that the energy distance approach, based on distributional comparisons, is a robust, efficient, and easily interpretable method for the condition monitoring of wind turbines.