Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation

• Published in: Energies (Basel) Vol. 11; no. 12; p. 3346
• Main Authors: Kazda, Jonas, Cutululis, Nicolaos Antonio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Alternative energy sources; control; Control algorithms; dynamic flow model; Energy industry; Engineering; Kalman filter; Kalman filters; Laboratories; linear; Offshore; Optimization; Power plants; prediction; Turbines; wind farm; Wind farms; Wind power; Denmark; United States > US
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11123346

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.