Improving wind farm flow models by learning from operational data

Abstract

This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond that contained in the training data set, which has a number of advantages. To assure a wide applicability of the method - also including existing assets - learning here is purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes, and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model.