Data-driven Adaptive Model Predictive Control for Wind Farms: A Koopman-Based Online Learning Approach

Publikationstyp
Conference Paper
Date Issued
2022-12
Sprache
English
Author(s)
Dittmer, Antje  
Sharan, Bindu  
Werner, Herbert  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/14669
Start Page
1999
End Page
2004
Citation
IEEE 61st Conference on Decision and Control (CDC 2022)
Contribution to Conference
IEEE 61st Conference on Decision and Control, CDC 2022  
Publisher DOI
10.1109/CDC51059.2022.9992829
Scopus ID
2-s2.0-85146974849
A novel adaptive Koopman based model predictive control (MPC) algorithm for wind farm control is presented. Using a data-driven Koopman approach the highly non-linear wake effects governing wind farm dynamics can be efficiently modelled. An update rule is presented to enable online learning only when new information is available. Moreover, to provide sufficient excitation of all relevant model frequencies in closed loop, a small test signal is superimposed on the control input while the Koopman model is updated. Simulation studies in the WFSim environment illustrate excellent accuracy for wind speed estimation with changing wind speed. In closed loop, the adaptive online update strategy tracks reference farm yield well, considerably outperforming recently presented non-adaptive schemes.