Wind Turbine Power Curve Monitoring Based on Environmental and Operational Data

The power produced by a wind turbine depends on environmental conditions, working parameters, and interactions with nearby turbines. However, these aspects are often neglected in the design of data-driven models for wind farms' performance analysis. In this article, we propose to predict the active power and to provide reliable prediction intervals via ensembles of multivariate polynomial regression models that exploit a higher number of inputs (compared to most approaches in the literature), including operational and thermal variables. We present two main strategies: the former considers the environmental measurements collected at the other wind turbines in the farm as additional modeling information for the turbine under analysis; the latter combines multiple models relative to different operative conditions. We validate our approach on real data from the SCADA system of a wind farm in Italy and obtain a MAE of the order of 1.0% of the rated power of the turbine. Moreover, due to the structure of our approach, we can gain quantitative insights on the covariates most frequently selected depending on the working region of the wind turbines.

Automated summary

This article proposes an ensemble approach based on multivariate polynomial regression models to predict the power production of wind turbines and provide reliable prediction intervals. By considering environmental measurements and multiple models relative to different operative conditions, the effectiveness of the method is validated with low prediction errors.