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Nonlinear MPC for DFIG-based wind power generation under unbalanced grid conditions.
- Source :
-
International Journal of Electrical Power & Energy Systems . Jan2022, Vol. 134, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- • An accurate nonlinear model of DFIG is established for DFIG-based wind powergeneration system under unbalanced grid conditions. • A high-efficiency MPC scheme is constituted for DFIG, in which different objective functions under unbalanced grid conditions are defined for realizing different control targets. • The realization of the linear MPC is facilitated by incorporating the input-output feedback linearization technique. • Simulation results using the proposed controller show that power pulsations are effectively suppressed under unbalanced grid conditions while fulfilling good tracking performance. In a modern power system with a high wind power penetrating rate, the control of the doubly-fed induction generator (DFIG) is quite important to ensure high load-following capability. It can be quite challenging when grid voltage unbalance conditions happened. Model predictive control (MPC) is an advanced control strategy that can optimize the DFIG operation, particularly under unbalanced grid voltage conditions. The detailed modeling in the positive and negative synchronous reference frames resulting from the grid unbalance is presented in nonlinear state-space form. An effective linearized technique, i.e., the input-output feedback linearization method, is adopted in the MPC design. The control law is derived by the optimization of two targets that aim to eliminate the pulsations in the active or reactive power. The simulation results prove that power pulsations are effectively suppressed under unbalanced grid voltage conditions while ensuring good track performance. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01420615
- Volume :
- 134
- Database :
- Academic Search Index
- Journal :
- International Journal of Electrical Power & Energy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 152368435
- Full Text :
- https://doi.org/10.1016/j.ijepes.2021.107416