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In this letter, we consider the privacy‐preserving strategy of the distributed state estimator over wireless sensor networks. Aiming at an eavesdropper who can intercept the data transmitted on the communication channel, we design a simple preserving scheme to encode the transmitted data by using system dynamics and history estimates. To analyze the privacy‐preserving performance, we use the gap between the values deciphered by the eavesdropper and decoded by the sensor as the privacy index to evaluate the degree of robust privacy protection. Finally, we provide some simulations to illustrate the effectiveness of the proposed preserving schemes. [ABSTRACT FROM AUTHOR]

With increasing installations of grid-connected power electronic converters in the distribution network, there is a new trend of using distributed control in a cyber layer to coordinate the operations of these power converters for improving power system stability. However, cyber-attacks remain a threat to such distributed control. This paper addresses the cyber-attack detection and a countermeasure of distributed electric springs (ESs) that have emerged as a fast demand-response technology. A fully distributed model-based architecture for cyber-attack detection in the communication network is developed. Based on a dynamic model of ES with consensus control, a local state estimator is proposed and practically implemented to monitor the system. The estimator is fully distributed because only local and neighboring information is necessary. A countermeasure for the distributed ESs to ride through the cyber-attack and maintain regulatory services in a microgrid is demonstrated successfully. Experimental results are provided to verify the effectiveness of the proposed cyber-attack detection method and its ride-through capability.

This paper considers finite-time distributed state estimation for discrete-time nonlinear systems over sensor networks. The Round-Robin protocol is introduced to overcome the channel capacity constraint among sensor nodes, and the multiplicative noise is employed to model the channel fading. In order to improve the performance of the estimator under the situation, where the transmission resources are limited, fading channels with different stochastic properties are used in each round by allocating the resources. Sufficient conditions of the average stochastic finite-time boundedness and the average stochastic finite-time stability for the estimation error system are derived on the basis of the periodic system analysis method and Lyapunov approach, respectively. According to the linear matrix inequality approach, the estimator gains are designed. Finally, the effectiveness of the developed results are illustrated by a numerical example. [ABSTRACT FROM PUBLISHER]

In this paper, distributed state estimators are designed for periodic systems with sensor nonlinearities and successive packet dropouts. In consideration of large quantity of sensor work in harsh environment, the sensor nonlinearities are considered. By using the Bernoulli processes, a new model is introduced to describe the randomly occurred packet dropouts in transmission of neighbors’ information. In terms of property of periodic systems, N -periodic distributed state estimators are proposed to estimate the target plant. Then sufficient conditions are established to ensure that the augmented estimation error system is globally asymptotically stable with the prescribed l 2 − l ∞ performance index γ in average sense. Finally, a numerical example is utilized to illustrate the effectiveness of the proposed new method. [ABSTRACT FROM AUTHOR]