Abstract:It is difficult to successfully detect the false data injection attacks against the linear state estimation based on phasor measurement techniques in power systems.Here,we propose an intelligent method to detect false data injection attacks.First,the auto-encoder is used to extract the features of the power grid measurement data,which is done repeatedly to gradually reduce the feature dimension.Then the finally extracted feature is subjected to supervised learning through the Softmax layer,so as to obtain an attack detection algorithm based on stacked auto-encoders.Second,the attack detection approach is improved through noise reduction to solve the over fitting of auto-encoders.Finally,the proposed method is simulated and verified by IEEE-118 node test system,and the results show that the proposed attack detection method has high computational accuracy and efficiency.

Abstract:The dual-channel bandwidth allocation is studied considering estimation performance.The networked control system is modeled as a Markov jump delay one in order to reduce the influence of time delay on signal transmission.The dual-channel transmission strategy is adopted to improve the data arrival rate and reduce the packet loss.The limited bandwidth is flexibly allocated to two channels to fully exploit the channel capacity.Then a dual-channel quantitative transmission scheme is proposed and a state estimator is designed.The sufficient condition is derived for the time-varying error estimation system to satisfy the H∞ performance, and the gain matrix is given for the estimator.Finally, the effectiveness of the proposed strategy is illustrated by a numeral example.

Abstract:This paper focuses on the study of H∞ state estimation of static neural networks having leakage delay.With the skills to construct Lyapunov-Krasovskii functionals,a state estimator is designed for the estimation of H∞ performance,and the results are derived without applying differentiability on time-varying delays.Finally,a numerical example is provided to demonstrate the effectiveness and advantages of the obtained results.

Abstract:This paper studiesthe distributed state estimation problem of large-scale grid with sensor failures in spoofing attacks.The topological relations are introduced to describe the relationship between distributed sensor nodes.A Bernoulli random sequence is used to describe the spoofing attacks and its random characteristics.This article based on Lyapunov method proves the mean square stability and the H∞ stability of this system.Based on the analysis results and LMI,a distributed state estimator which satisfies the H∞ performance constraint is designed.Finally,the validity of the designed estimator is verified by numerical simulation.

Abstract:Because the state space model contains both the unknown states and the unknown parameters,and they involve the nonlinear product relations,which makes the identification problem more complicated.In order to solve this problem,this paper studies the combined state and parameter estimation methods for canonical state space systems.The interactive estimation theory is used to derive the combined state and parameter estimation algorithms by means of the recursive or iterative scheme.When computing the parameter estimates,the unknown states in the information vector of the identification algorithms are replaced with their estimates,the obtained parameter estimates are used to design the parameter estimates based observer or the parameter estimates based Kalman filtering algorithm to estimate the states of the systems.They form an interactive estimation process (a hierarchical estimation process).Along this line,from the recursive scheme or the iterative scheme,this paper presents the observer based or the Kalman filtering based stochastic gradient (SG) identification algorithm,recursive least squares (LS) identification algorithm,multi-innovation SG algorithm,multi-innovation LS identification algorithm,and the model decomposition based identification methods.Finally,the computational efficiency,the computational steps and the flowcharts of some typical algorithms are discussed.