  This paper presents a model predictive control (MPC)-based online real-time
adaptive control scheme for emergency voltage control in power systems. Despite
tremendous success in various applications, real-time implementation of MPC for
control in power systems has not been successful due to its online
computational burden for large-sized systems that takes more time than
available between the two control decisions. This long-standing problem is
addressed here by developing a novel MPC-based adaptive control framework which
(i) adapts the nominal offline computed control, by successive control
corrections, at each control decision point using the latest measurements, (ii)
utilizes data-driven approach for prediction of voltage trajectory and its
sensitivity with respect to control using trained deep neural networks (DNNs).
In addition, a realistic coordination scheme among control inputs of static var
compensators (SVC), load-shedding (LS), and load tap-changers (LTC) is
presented with a goal of maintaining bus voltages within a predefined
permissible range, where the delayed effect of LTC action is also incorporated
in a novel way. The performance of the proposed scheme is validated for IEEE
9-bus as well as 39-bus systems, with $\pm 20\%$ variations in nominal loading
conditions. We also show that the proposed new scheme speeds up the online
computation by a factor of 20 bringing it down to under one-tenth the control
interval, making the MPC-based power system control practically feasible.
