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DFIG Damping Controller Design Using Robust CKF-Based Adaptive Dynamic Programming
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DOI：10.1109/TSTE.2019.2910262.png)
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Abstract
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An adaptive, Lyapunov stable, computational optimal control scheme based on a policy iteration algorithm is presented for the damping control of oscillatory dynamics and overall stability improvement of a grid-connected doubly fed induction generator (DFIG) based wind energy conversion system. The proposed controller employs adaptive dynamic programming and uses the online information of estimated internal states, terminal measurements, and controller output to solve the nonlinear algebraic Riccati equation. The unobservable internal states of the DFIG are estimated from terminal measurements (stator current and terminal voltage) using a robust nonlinear dynamic state estimator based on spherical-radial cubature rule. The controller does not require any prior knowledge of the linearized system matrices and hence assumes unknown system dynamics, thereby avoiding the considerable computational burden of system linearization. A detailed model of the DFIG has been considered, and the effectiveness of the proposed controller has been compared with an optimally tuned conventional damping controller and traditional linear quadratic regulator. A scaled laboratory setup using coupled rapid prototyping controller and real-time station has been used to demonstrate the real-time applicability of the developed scheme. A modified IEEE WSCC 9-bus system with DFIG interconnection has also been used as a test system for controller evaluation in the multimachine environment.
Keywords:
Optimal control
Douby fed induction generators
Covariance matrices
Voltage measurement
Power system stability
Stability
Damping
Adaptive control
Kalman filters
State estimation
Lyapunov methods
Iterative methods
Adaptive control
linear quadratic regulator (LQR)
cubature-Kalman-filter (CKF)
dynamic state estimator
damping control
DFIG
optimal control
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