Publisher DOI: 10.1109/CDC51059.2022.9992829
Title: Data-driven Adaptive Model Predictive Control for Wind Farms: A Koopman-Based Online Learning Approach
Language: English
Authors: Dittmer, Antje 
Sharan, Bindu 
Werner, Herbert 
Issue Date: Dec-2022
Source: IEEE 61st Conference on Decision and Control (CDC 2022)
Abstract (english): 
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.
Conference: IEEE 61st Conference on Decision and Control, CDC 2022 
ISBN: 978-1-6654-6761-2
Institute: Regelungstechnik E-14 
Document Type: Chapter/Article (Proceedings)
Appears in Collections:Publications without fulltext

Show full item record

Page view(s)

checked on Mar 10, 2023

Google ScholarTM


Add Files to Item

Note about this record

Cite this record


Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.