https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 Distributed dynamic state estimation with parameter identification for large-scale systems https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33314 a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification.]]> Wed 10 Oct 2018 13:41:24 AEDT ]]> Dynamic state estimation in power systems using a distributed MAP method https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:26038 Sat 24 Mar 2018 07:26:29 AEDT ]]> Dynamic state estimation for power networks using distributed MAP technique https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28130 a posteriori (MAP) estimation technique, which delivers a globally optimal estimate under certain assumptions. We apply the distributed approach to an IEEE 118-bus system, and compare it with a centralized approach, which provides the optimal state estimate using all the measurements, and with a local state estimation approach, which uses only local measurements to estimate local states. Simulation results show that under different scenarios including normal operation, bad measurements and sudden load change, the distributed approach is clearly more accurate than the local state estimation approach and distributed static state estimation approach. Although the result is a bit less accurate than that by a centralized algorithm, the distributed algorithm enjoys low computational complexity and communication load, and is scalable to large power networks.]]> Sat 24 Mar 2018 07:24:55 AEDT ]]> A distributed MAP approach to dynamic state estimation with applications in power networks https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:22944 Sat 24 Mar 2018 07:15:44 AEDT ]]> Maximum principle for Mckean-Vlasov type semi-linear stochastic evolution equations https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33118 Mon 27 Aug 2018 18:23:55 AEST ]]>