The analysis of blackouts, which can inevitably lead to catastrophic damage to power grids, helps to explore the nature of complex power grids but becomes difficult using conventional methods. This brief studies the vulnerability analysis and recognition of key nodes in power grids from a complex network perspective. Based on the ac power flow model and the network topology weighted with admittance, the cascading failure model is established first. The node electrical centrality is further pointed out, using complex network centrality theory, to identify the key nodes in power grids. To effectively analyze the behavior and verify the correctness of node electrical centrality, the net-ability and vulnerability index are introduced to describe the transfer ability and performance under normal operation and assess the vulnerability of the power system under cascading failures, respectively. Simulation results of IEEE 30-bus and IEEE 57-bus test cases show that the key nodes can be effectively identified with high electrical centrality, the resultant cascading failures that eventually lead to a severe decrease in net-ability, verifying the correctness and effectiveness of the analysis.

This paper proposes a novel filter, which is based on the event-triggered schedule and the cubature Kalman filter (CKF), to perform the nonlinear remote state estimation in the wireless sensor network with communication constraints. For the purpose of relieving the communication burden between sensors and the remote filter, the event-triggered schedule is utilized to determine whether the current observations should be sent. To deal with the non-Gaussian property due to the nonlinear transformation, the third-degree spherical-radial cubature rule is adopted to provide the accurate estimation. Moreover, the stochastic stability of the designed event-triggered CKF (ETCKF) is analyzed deriving the relationships among the estimation performance, the communication rate and the design parameter. Therefore, the proposed ETCKF can effectively balance the communication burden and the estimation accuracy. Finally, the numerical simulation on IEEE-39 bus system and unmanned aerial vehicles remote attitude monitoring system are performed to verify the practicability of ETCKF.

Accurate real-time state estimation plays an important role in distributed generation (DG) networks. In order to establish a reliable communication link for estimation, an information-centric networking (ICN) was employed with the development of smart grids technology. However, the widespread adoption of DG network boosts the amount of data transmitted through ICN, which is now the real-time performance bottleneck of estimation. By fully considering the nonlinearity of DG system, this paper develops an event-trigger particle filter (ET-PF) to relieve the communication burden and achieve an appropriate estimation accuracy. An arrival rate guaranteed event-trigger strategy is established for saving the bandwidth at first, which selects only the observations containing innovational information for the purpose of filtering. When the observation is not triggered, an ET-PF filtering algorithm is further proposed, making full use of the information from the prior event-trigger strategy, to enhance the performance of estimation. The design-oriented guaranteed arrival rate, specifically designed by ET-PF in statistical sense, is beneficial to design ICN infrastructure. Finally, its feasibility and performance is demonstrated using the standard IEEE 39-bus system with phasor measurement units.

This study employs the Caputo-Fabrizio fractional derivative to determine the model of fractional-order RC circuits with arbitrary voltage input which can be widely used in a variety of electrical systems. Analog circuit implementation of fractional-order RC circuits defined by Caputo-Fabrizio fractional derivative is presented and verified by comparing with the model proposed in this work. For the purpose of judging whether the fractional-order model defined by the Caputo-Fabrizio derivative is practical, the comparison experiments are carried out. By using Laplace transform, the analytical solutions of fractional-order RC circuits based on the Caputo-Fabrizio derivatives with constant and periodic voltage sources are deduced. Fractional-order model of RC circuits with arbitrary input are also calculated using the convolution formula. The correctness of the derivation of the model using the Caputo-Fabrizio derivative is verified. Through discussing the impedance model of capacitor in frequency domain, the analog realization of fractional capacitor based on the Caputo-Fabrizio derivative is derived. The fractional-orders of the RC circuits models defined by the Caputo and Caputo-Fabrizio fractional derivatives are fitted respectively through repeated charging and discharging experiment data. The fractional-order models based on the Caputo and Caputo-Fabrizio derivatives, and the integer-order model are all compared with the experiment data.

The development of distributed generation (DG) raises high requirement on accurate real-time state estimation with phasor measurement unit (PMU) for wide-area measurement systems (WAMS). Although the particle filter (PF) is among the best in estimation performance, its computation burden is truly heavy towards smart sensors of current generation. Besides, the infrastructure for PMU data communication is confronted with the challenges of the amount of data boosted by the high sampling frequency of PMUs and booming grid size. This paper proposes an event-trigger heterogeneous nonlinear filter (ET-HNF) to solve these problems. A master-slave nonlinear filtering structure is designed for WAMS by taking full advantage of computation power both at generator node and estimation center. The local slave filter is established at generator node to perform the event-trigger strategy based on low computational unscented transformation. Moreover, a Monte Carlo based filter served as the master, which improves estimation center’s filtering performance by cooperating with the center slave. Simulation results verify the feasibility and performance of ET-HNF using the standard IEEE 39-bus system with PMUs.

Oscillation issues of permanent magnet synchronous generator (PMSG) wind turbines (WTs) have an increasingly significant impact on grid stability, especially with their increased generation capacity. However, the machine-side model (MSM), involving machine-side converter (MSC) and generator, is generally simplified as an ideal voltage source to comprising the impedance model of a PMSG-WT used for stability analysis, thus causing deficiency in describing the machine dynamics. Besides, the impedance-based method considering the PMSG-WT as a black box only describes the system from the external output characteristics; as such, the shaping effect of the internal controllers on the impedance model has not been fully studied yet. This paper proposes the impedance model of PMSG-WTs consisting of generator, MSC, DC-bus and grid-side converter (GSC) for more accurate stability analysis. Furthermore, based on the General Nyquist Criteria (GNC) and eigenvalue sensitivity analysis method, the shaping effect of the controllers inside GSC and MSC on the PMSG external impedance is quantitatively studied. The influence of the MSM on the system stability is investigated by comparing with the simplified model through extensive simulation studies.