Automated estimation of topology in smart micro grids (SMGs) has been advocated for the optimization of locally generated power. Moreover, when a power line communication (PLC) network is overlaid to the SMG, topology reconstruction is useful also for routing. In this paper, we propose a technique that allows estimating the SMG topology by exploiting PLC signal itself, thus providing an appealing plug-and-play solution for routing optimization. We use measurements of the transmission time taken by the PLC signal to propagate between couples of nodes, and exploit the fact that the communication signal follows the shortest route along the wires to identify the nodes across which it passed. To this end, we cast the topology estimation problem into an hypothesis testing problem, solved by the generalized likelihood ratio test (GLRT). Low complexity solutions based upon a Gaussian approximation of the measurement errors will also be investigated. Performance of the topology estimation technique is assessed on a realistic low voltage outdoor SMG scenario, proving its effectiveness for reliable SMG management.
Topology Estimation for Smart Micro Grids via Powerline Communications
ERSEGHE, TOMASO;TOMASIN, STEFANO;VIGATO, ALBERTO
2013
Abstract
Automated estimation of topology in smart micro grids (SMGs) has been advocated for the optimization of locally generated power. Moreover, when a power line communication (PLC) network is overlaid to the SMG, topology reconstruction is useful also for routing. In this paper, we propose a technique that allows estimating the SMG topology by exploiting PLC signal itself, thus providing an appealing plug-and-play solution for routing optimization. We use measurements of the transmission time taken by the PLC signal to propagate between couples of nodes, and exploit the fact that the communication signal follows the shortest route along the wires to identify the nodes across which it passed. To this end, we cast the topology estimation problem into an hypothesis testing problem, solved by the generalized likelihood ratio test (GLRT). Low complexity solutions based upon a Gaussian approximation of the measurement errors will also be investigated. Performance of the topology estimation technique is assessed on a realistic low voltage outdoor SMG scenario, proving its effectiveness for reliable SMG management.Pubblicazioni consigliate
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