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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. [ABSTRACT FROM AUTHOR]

An adaptive decentralized control scheme is proposed for real-time control of oscillatory dynamics and overall stability improvement of an interconnected power system. A standard framework of continuous-time (CT) infinite-horizon optimal control paradigm is defined and an extended online actor-critic (AC) algorithm based on policy iteration is used for its solution. The AC structure uses neural networks (NNs) whose weights are updated adaptively. The proof for the convergence of the scheme guarantees the system stability. The unobservable internal states of the synchronous machine are estimated using a numerically stable, swift and relatively accurate decentralized dynamic state estimator (DDSE) based on spherical-radial cubature rule. Applicability of the developed scheme has been demonstrated on a benchmark power system (IEEE 2.2, 16 machine 68 bus system) model via nonlinear time-domain simulations. Multi-processor technology based scaled laboratory setup was used for controller performance validation in real-time. [ABSTRACT FROM AUTHOR]