A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks

The load demand and distributed generation(DG)integration capacity in distribution networks(DNs)increase constantly,and it means that the violation of security constraints may occur in the future.This can be further worsened by short-term power fluctuations.In this paper,a scheduling method based on a multi-objective chance-constrained information-gap decision(IGD)model is proposed to obtain the active management schemes for distribution system operators(DSOs)to address these problems.The maximum robust adaptability of multiple uncertainties,including the deviations of growth prediction and their relevant power fluctuations,can be obtained based on the limited budget of active management.The systematic solution of the proposed model is developed.The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term.Considering the stochastic characteristics and correlations of power fluctuations,the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution.The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network.The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties,which corresponds to an optional active management strategy set for future selection.

Robust fault-tolerant attitude control for satellite with multiple uncertainties and actuator faults

In this paper,the satellite attitude control system subject to parametric perturbations,external disturbances,time-varying input delays,actuator faults and saturation is studied.In order to make the controller architecture simple and practical,the closed-loop system is transformed into a disturbance-free nominal system and an equivalent disturbance firstly.The equivalent disturbance represents all above uncertainties and actuator failures of the original system.Then a robust controller is proposed in a simple composition consisting of a nominal controller and a robust compensator.The nominal controller is designed for the transformed nominal system.The robust compensator is developed from a second-order filter to restrict the influence of the equivalent disturbance.Stability analysis indicates that both attitude tracking errors and compensator states can converge into the given neighborhood of the origin in finite time.To verify the effectiveness of the proposed control law,numerical simulations are carried out in different cases.Presented results demonstrate that the high-precision attitude tracking control can be achieved by the proposed fault-tolerant control law.Furthermore,multiple system performances including the control accuracy and energy consumption index are fully discussed under a series of compensator parameters.

Stabilization of Continuous-Time Systems Against Stochastic Network Uncertainties:Fundamental Variance Bounds

This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative uncertainties.The authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square criterion.In the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication channels.The authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output feedback.For single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square stabilization.For MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.