Turbine-Level Possible Power Prediction using Farm-Wide Spatial Information through Similarity-Based Multivariate Gaussians

Abstract Wind farms usually comprise several turbines of the same type in proximity to one another. Therefore, similarities exist between the power production of specific turbines within the wind farm over time. Considering this, it is possible to find a way to express the similarity between turbines and exploit their properties to find a formulation of the expected behavior of a turbine. With this estimation of possible power output, one can analyze the losses generated by curtailments or transients with a higher precision. Based on this, a probabilistic model is proposed that can be used to calculate energy losses due to maintenance or environmental reasons. On top of that, heavy deviations in the behavior of specific wind turbines can be detected on high-frequency (1-second) data. Overall, the goal of this work is to predict possible power on high-frequency SCADA data using a statistical white-box modeling approach. The presented method is based on a probabilistic framework, constructing a system of linear combinations that permits analytically tracking the expected behavior of a turbine, even if it is non-operational for a specific amount of time. The methodology includes two parts, the first one is the use of data-driven power curves, and the second one consists of an inferential framework based on the environmental conditions of the farm. Results show that the method presented performs better than the manufacturer’s power curves under specific wind speeds and wind directions.