Abstract
The aerodynamic interaction between wind turbines grouped in wind farms results in wake-induced power loss and fatigue loads of wind turbines. To mitigate these, wind farm control should be able to account for those interactions, typically using model-based approaches. Such model-based control approaches benefit from computationally fast, linear models and therefore, in this work, we introduce the Dynamic Flow Predictor. It is a fast, control-oriented, dynamic, linear model of wind farm flow and operation that provides predictions of wind speed and turbine power. The model estimates wind turbine aerodynamic interaction using a linearized engineering wake model in combination with a delay process. The Dynamic Flow Predictor was tested on a two-turbine array to illustrate its main characteristics and on a large-scale wind farm, comparable to modern offshore wind farms, to illustrate its scalability and accuracy in a more realistic scale. The simulations were performed in SimWindFarm with wind turbines represented using the NREL 5 MW model. The results showed the suitability, accuracy, and computational speed of the modeling approach. In the study on the large-scale wind farm, rotor effective wind speed was estimated with a root-mean-square error ranging between 0.8% and 4.1%. In the same study, the computation time per iteration of the model was, on average, 2.1 × 10 − 5 s. It is therefore concluded that the presented modeling approach is well suited for use in wind farm control.
Highlights
The wind energy market has been growing rapidly at a rate of 16% throughout the past decade, reaching 539,123 MW of global installed capacity in 2017 [1]
In approaches of wind farm control with the objective of the maximization of the total power, it is crucial to consider the aerodynamic interaction of wind turbines in the model employed for control [12,13]
This work introduces the Dynamic Flow Predictor (DFP), a control-oriented, linear dynamic wind farm flow and operation model suited for model predictive control of wind farms
Summary
The wind energy market has been growing rapidly at a rate of 16% throughout the past decade, reaching 539,123 MW of global installed capacity in 2017 [1]. Modern wind turbines are complex machines with sophisticated control systems. Single turbine control systems are well developed and most of the modern machines are, at least to some extent, optimized in the areas of aerodynamics [2], aeroelasticity [3], and control [4]. When grouped in wind farms, the individual optimal operation does not necessarily coincide with the overall optimum, mainly due to the aerodynamic interaction between the wind turbines. The control objectives can be augmented to include multiobjective optimization that simultaneously aims to reduce the fatigue loads of wind turbines [9,10,11]. In approaches of wind farm control with the objective of the maximization of the total power, it is crucial to consider the aerodynamic interaction of wind turbines in the model employed for control [12,13]
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