Inter-area oscillations in time delayed power systems :a kalman time compensator and a model predictive control approach (2018)
Due to high costs in facilities and devices, power systems usually works close to their stability limits. To address the stability problems, automatic voltage regulators (AVR) and power system stabilizers (PSS) have been employed successfully but limited to local phenomena with few contributions in global dynamics in power systems. Then, the raise of wide area monitoring systems (WAMS) emerges as the path for the improvement of global dynamics (inter-area oscillations) performance using remote feedback controller. Hence, a new challenge emerges: maintain stability in a closed loop control with time delays inherent to WAMS: time delays in communications from measurements to the control centre, and extra delays from the control centre to actuators. The research was motivated by the need for modernization of power systems capable of dealing with such control difficulties in centralised WAMS for damping inter-area oscillations in power systems. The general idea of the approach is to splitting the time-delay power system control problem in two parts: the time compensation and the control of nonlinear power system. The control problem is solved by a model predictive control (MPC) with terminal cost and constraint set to handle complexities due to: nonlinearities of the power system, the large scale nature of the problem, uncertainties and multiple dynamic feature. The compensation is made by a database based time compensation solution relying on the most updated available state of the system, and the nature of solution provided by the MPC for the forward delay. The approach works in a superior hierarchic level, hence creating a decentralised coordinated strategy that manages slow global dynamics and fast local dynamics as well avoiding bad interactions between different dynamics. The integrated proposed approach is called General Network Latencies Tolerant Model Predictive Control (GNLT-MPC). The approach has been successfully tested in nonlinear time simulations