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Under conditions of high penetration of renewables, the low-voltage (LV)distribution network needs to be carefully managed. In such a scenario, anaccurate real-time low-voltage power network model is an importantprerequisite, which opens up the possibility for application of many advancednetwork control and optimisation methods thus providing improved power flowbalancing, reduced maintenance costs, and enhanced reliability and security ofa grid. Smart meters serve as a source of information in LV networks and allowfor accurate measurements at almost every node, which makes it advantageous touse data driven methods. In this paper, we formulate a non-linear andnon-convex problem, solve it efficiently, and propose a number of fully smartmeter data driven methods for line parameters estimation. Our algorithms arefast, recursive in data, scale linearly with the number of nodes, and can beexecuted in a decentralised manner by running small algorithms inside eachsmart meter. The performance of these algorithms is demonstrated for differentmeasurement accuracy scenarios through simulations.

Low-Voltage Distribution Network Impedances Identification Based on Smart Meter Data