Data-driven characterization of performance trends in ageing wind turbines

Abstract

Abstract The precise quantification of wind turbine long- and short-term performance is crucial to assess the health state of ageing turbines and to evaluate the benefit of maintenance activities. Indeed, during its lifetime, wind turbines can experience a decay in terms of performance (e.g. due to wear) or improvement (e.g. due to technology optimizations). For this reason, we developed an integrated data-driven methodology to characterize the long- and short-term performance trends and performance variability in turbines. The methodology is validated on a synthetic dataset with imposed decay and then tested on a real wind farm operated by Engie Italy and composed of seven turbines for which ten years of SCADA data are collected. We show how this methodology accurately captures the evolution of a turbine’s performance and how it is capable of quantifying the impact of the controller update.