Paper Info
Title
Decentralized Stability Enhancement of DFIG-Based Wind Farms in Large Power Systems: Koopman Theoretic Approach
Abstract
This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability. The proposed method is designed to control active and reactive power injections via power converters to reduce the oscillations produced by the WF during disturbance conditions. Without prior knowledge of the system model, this approach utilizes the states measurements of the DFIG subsystem for control design. Therefore, a data-driven optimal controller with a decentralized feature is developed. The learning process is based on Koopman operator theory where the unknown nonlinear dynamics of the DFIG is reconstructed by lifting the nonlinear dynamics to a linear space with an approximate linear state evolution. Extended dynamic mode decomposition (EDMD) is then applied to determine the lifted-state space matrices for the proposed Koopman-based model predictive controller (KMPC) design. The effectiveness of the proposed scheme is tested on New England IEEE 68-bus 16-machine system under three-phase fault conditions. The results ascertain the effectiveness of the proposed scheme to enhance the system damping characteristics.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3157747
IEEE ACCESS
Keywords
DocType
Volume
Generators, Damping, Power system stability, Wind turbines, Wind farms, Doubly fed induction generators, Adaptation models, Koopman operator, power system stabilizers, model predictive control, double-fed induction generator, decentralized control
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Ahmed Husham100.34
Innocent Kamwa22512.52
M. A. Abido310616.69
Hussein Supreme400.34