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In this paper, we present a new algorithm for decentralized state estimation in multi-area power systems. The proposed method features significantly reduced communication between areas when compared to the existing gossip-based Gauss-Newton approach. We achieve this by confining gossip iterations associated with a specific state variable to only those areas which actually observe that state variable...
We present an innovative approach to distributed estimation which features time- and coefficient-selective updates of parameter estimates, thereby offering a significant reduction in energy consumption in the sensor nodes. The proposed approach is based on the principles of set-membership adaptive filtering (SMAF), which allows for selective updates of parameter estimates. It also employs the principle...
This paper proposes selective update and cooperation strategies for parameter estimation in distributed adaptive sensor networks. A set-membership filtering approach is employed that results in reduced complexity for updating parameter estimates at each network node, a significant reduction in information exchange between cooperating nodes, and an optimal strategy to obtain consensus estimates. The...
This paper proposes a clustering approach to parameter estimation in distributed sensor networks. The proposed approach is an alternative to the conventional centralized and decentralized approaches. This is made possible by the unique adaptive estimation architecture, U-SHAPE, stemming from set-membership adaptive filtering. At the expense of a slightly degraded mean-square error performance (comparing...
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