Abstract
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.
Highlights
Knowledge of the flow at the rotor disk of each wind turbine in a wind power plant enables several applications, including wind farm control, the provision of grid services, predictive maintenance, the estimation of life consumption, the feed-in to digital twins, and power forecasting, among others.This paper describes a new method to improve a wind farm flow model directly from standard operational data
The former example aims at a verification of the correctness of the identified augmentations, given the known and controllable conditions of the scaled experiments, whereas the latter is meant to offer a first glimpse of the practical applicability of the new method in the field
The aim of this section is to show that, even in the presence of multiple possibly overlapping model terms, the correct improvements to a baseline model can be learned from operational data only
Summary
Knowledge of the flow at the rotor disk of each wind turbine in a wind power plant enables several applications, including wind farm control, the provision of grid services, predictive maintenance, the estimation of life consumption, the feed-in to digital twins, and power forecasting, among others.This paper describes a new method to improve a wind farm flow model directly from standard operational data. The main idea pursued here is to use an existing wind farm flow model to provide a baseline predictive capability; as all models contain approximations and may lack the description of some physical phenomena, the baseline model is improved (or “augmented”, which is the term used in this work) by adding parametric correction terms. These extra elements of the model are learned by using operational data.
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