The Crowdsourced IoT Service (CIS) market is inherently different from other service markets, e.g., web services and cloud. The CIS market is dominated by transient services as both consumers and providers are dynamic in space and time. Consumer requests are usually long-term and demand continuity in service provision. We propose a novel egalitarian transient service composition framework from the CIS market perspective. We apply a Dynamic Bayesian Network to model the dynamic service provision behavior of the providers. The proposed framework transforms the composition of transient services into a multi-objective temporal optimization, i.e., providing continuous services to the maximum number of consumers, and minimizing the consumers' cost of service usages over a long-term period. We incorporate a Pareto-based genetic algorithm to enable the fair distribution of services among the consumers. Experimental results prove the efficiency of the proposed approach in terms of continuo
Multiple microgrid (multi-MG) clusters based on virtual synchronous generator (VSG) control can preferably incorporate renewable energies. Nevertheless, the power angle stability (PAS) of VSG-controlled multi-MG clusters is easy to be influenced by grid faults. In this paper, resistive superconducting fault current limiters (R-SFCLs) are introduced to deal with this PAS problem. It is designed to install the R-SFCLs at the point of common coupling (PCC) of each MG in the clusters. The transient stability mechanism based on power balance and swing equation is analyzed to clarify the impacts of the R-SFCLs. Through MATLAB, a simulation model of three VSG-controlled MGs with R-SFCLs is built, and different fault scenarios are imitated for checking the performance behaviors of the R-SFCLs. From the simulation results, the R-SFCLs can visibly suppress the overcurrent inrush and assist the MGs in the clusters to fulfill the fault-ride through (FRT). Moreover, the energy dissipation of the R-
This research article presents an adaptive auto-reclosing scheme to preserve the network stability in post-fault scenarios for the power grids equipped with synchronous-based distributed generations (SBDGs). Based on the precise fault location information, the proposed adaptive reclosing strategy will classify all the fault events in either reclosing or block reclosing zones. Furthermore, the proposed intelligent auto-reclosing scheme will ensure safe and stable network operation by preventing hazardous futile reclosing attempts against persistent faults in forestry or densely populated areas due to serious safety concerns. The swift fault removal by the associated protective devices (PDs) will assist in ensuring the stable operation of the power grid after the fault clearance. If the fault is in the reclosing zones, the adaption of the proposed scheme will permit the reclosing at an appropriate instant to preserve the network stability against momentary faults. However, it will also e
Abstract
In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which is subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network (NN) state observer is constructed. To guarantee the transient and steady-state tracking performance, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is featured by user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method, with the combination of the adaptive NN technique and vector-backstepping design tool, an adaptive neural output feedback trajectory tracking control scheme with predefined performance is developed Referring to our developed control scheme, uncertainties can be reconstructed only by utilizing the posit