A Novel LowComplexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities The smart energy system, viewed as an xe2x80x9cEnergy Internetxe2x80x9d, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter (    S T U K F    ) and modified Bayesxe2x80x99 classificationbased Modified Three Sigma test (    M T S    ), abbreviated as     S F B T    , for smart energy networks. The theoretical analysis and simulations indicate that     S F B T     detects faults with a high accuracy and a low complexity of     O ( n )    .

Power System Dynamic State Estimation Using Smooth Variable Structure Filter With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on realtime monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictorcorrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.