Data-driven wind turbine aging models

The worsening with age of wind turbine performance is an expected phenomenon, which is practically impossible to estimate a priori. The objective of this study is formulating a method for the estimation of wind turbine performance decline with age, basing on long term SCADA data analysis. Two test cases, featuring in total fifteen 2 MW wind turbines, have been selected because at present there are no similar studies devoted to wind turbines of this size. The study is focused on the operation regime characterized by variable rotor speed and practically fixed pitch and it is based on the analysis of the rotor speed - power and generator speed - power curves through the binning method and through a Support Vector Regression with Gaussian Kernel. The main result is that the average rate of performance decline with age for the considered test cases is in the order of - 0.2% per year, which is compatible with the most recent analysis in the literature based on cumulative data. Furthermore, it is estimated that the gearbox aging does not contribute to the performance decline, while instead the generator aging does.

Automated summary

This study formulates a method for estimating wind turbine performance decline with age based on long term SCADA data analysis, finding that in the considered test cases, the average rate of performance decline with age is approximately -0.2% per year, compatible with recent analyses based on cumulative data. It is also concluded that gearbox aging does not contribute to the performance decline, while generator aging does.