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Dynamic state estimation (DSE) accurately tracks the dynamics of a power system and provides the evolution of the system state in real-time. This paper focuses on the control and protection applications of DSE, comprehensively presenting different facets of control and protection challenges arising in modern power systems. It is demonstrated how these challenges are effectively addressed with DSE-enabled solutions. As precursors to these solutions, reformulation of DSE considering both synchrophasor and sampled value measurements and comprehensive comparisons of DSE and observers have been presented. The usefulness and necessity of DSE based solutions in ensuring system stability, reliable protection and security, and resilience by revamping of control and protection methods are shown through examples, practical applications, and suggestions for further development. [ABSTRACT FROM AUTHOR]

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]

This paper investigates the development and application of a nonlinear adaptive intelligent controller with superior disturbance-rejection capability for a doubly-fed-induction-machine driven flywheel energy storage system (FESS), to mitigate the intermittency in wind power injection, as well as enhance the transient stability of the connected multimachine power system thereby isolating the grid power from fluctuations. Intelligent supervisors for the rotor-side-converter of the FESS have been constructed using a fuzzy rule set. The consequent part in every rule includes a wavelet function for function learning. The novel normalized gradient descent algorithm with adaptive learning rate has been used to derive the update laws for unknown controller parameters. The control law has been derived by minimizing the predictive performance index. Convergence of the developed algorithm is guaranteed as proven via the Lyapunov stability method. Realistic wind speed profile has been used to test the efficacy of the developed control scheme. The sizing strategy has been devised for optimal sizing of FESS for its efficient utilization. Modified WSCC nine-bus test system has been used for the nonlinear time-domain simulations and novel indices have been used for evaluation of controller performance against its predecessors. $\boldsymbol{OP}{\text{5600}}$ Real-Time-Digital-Simulator has been used to demonstrate the real-time implementation of the suggested scheme. [ABSTRACT FROM AUTHOR]