Resilient WideArea Damping Control Using GrHDP to Tolerate Communication Failures This paper proposes a goal representation heuristic dynamic programming (GrHDP) based resilient widearea damping controller (WADC) for voltage source converter high voltage direct current (VSCHVDC) employing redundant widearea signals as input signals to tolerate communication failure. A supervisory fuzzy logic module (FLM) is proposed and added in the resilient WADC to adjust the learning rate of GrHDP online when encountering communication failure. Moreover, the resilient WADC does not need the accurate model of the power system and has the adaptability to the variation of operation conditions and communication failures. Case studies are conducted in a 10machine 39bus system with one VSCHVDC transmission line. Simulation results show that the resilient WADC can counteract the negative impact of communication failures on control performance under a wide range of system operating conditions.

WideArea Damping Control Using Multiple DFIGBased Wind Farms Under Stochastic Data Packet Dropouts Data dropouts in communication network can have a significant impact on widearea oscillation damping control of a smart power grid with largescale deployment of distributed and networked phasor measurement units and wind energy resources. Remote feedback signals sent through communication channels encounter data dropout, which is represented by the GilbertElliott model. An observerdriven reduced copy (ORC) approach is presented, which uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance where conventional feedback would suffer. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived. The key contribution comes from the analytical derivation of the impact of coupling between the cyber and the physical layer on ORC performance. Monte Carlo simulation is performed to calculate the dispersion of the error bound. Nonlinear timedomain simulations demonstrate that the ORC produces significantly better performance compared to conventional feedback under higher data drop situations.