The article proposes a nonlinear optimal (H-infinity) control method for a wind power generation system comprising a two-mass drivetrain and a Doubly-Fed Induction Generator (DFIG). Comparing to the 6-th order dynamic model of the DFIG, the state-space model of the considered power generation unit is extended after including in it the dynamics of the drivetrain. To solve the associated control problem, the dynamic model of the power generation unit undergoes approximate linearization around a temporary operating point which is recomputed at each time-step of the control algorithm. The linearization procedure relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the linearized state-space description of the power unit, an H-infinity controller is developed. This stands for the solution of the optimal control problem for the wind power system under model uncertainty and external perturbations. For the computation of the controller's feedback gain an algebraic Riccati equation is repetitively solved at each iteration of the control method. Moreover, with the use of Lyapunov analysis the global asymptotic stability of the control scheme is proven. Finally, to implement state estimation-based control without the need to measure the entire state vector of the power unit, the H-infinity Kalman Filter is used as a robust observer.

Nonlinear optimal control for wind power generators comprising a multi-mass drivetrain and a DFIG

Rigatos G.;Siano P.;
2019-01-01

Abstract

The article proposes a nonlinear optimal (H-infinity) control method for a wind power generation system comprising a two-mass drivetrain and a Doubly-Fed Induction Generator (DFIG). Comparing to the 6-th order dynamic model of the DFIG, the state-space model of the considered power generation unit is extended after including in it the dynamics of the drivetrain. To solve the associated control problem, the dynamic model of the power generation unit undergoes approximate linearization around a temporary operating point which is recomputed at each time-step of the control algorithm. The linearization procedure relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the linearized state-space description of the power unit, an H-infinity controller is developed. This stands for the solution of the optimal control problem for the wind power system under model uncertainty and external perturbations. For the computation of the controller's feedback gain an algebraic Riccati equation is repetitively solved at each iteration of the control method. Moreover, with the use of Lyapunov analysis the global asymptotic stability of the control scheme is proven. Finally, to implement state estimation-based control without the need to measure the entire state vector of the power unit, the H-infinity Kalman Filter is used as a robust observer.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4726604
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