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Using information entropy to quantify uncertainty in distribution networks


Negnevitsky, M and Terry, J and Nguyen, T, Using information entropy to quantify uncertainty in distribution networks, Proceedings of the 24th Australasian Universities Power Engineering Conference, 28 September - 1 October, Perth, Australia, pp. 1-6. ISBN 978-0-646-92375-8 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 IEEE

DOI: doi:10.1109/AUPEC.2014.6966487


Information about the certainty of the predicted state of a network is crucial for the effective management of a modern power system. In transmission systems, observability analysis is used to assess whether the estimated state is valid, based on the available measurements. In distribution systems there is no method for quantifying the uncertainty in a modelled state; this places limitations on how the modelled state can be used. This paper explores a new method of quantifying the uncertainty in a state estimation solution, with information entropy. A Monte Carlo simulation approach was used to determine the probability of the network being in a specific state. The proposed approach allows for the objective evaluation of the certainty of a state solution in distribution networks, which can be easily interpreted by distribution network service providers. Case studies were conducted, results are resented and discussed.

Item Details

Item Type:Refereed Conference Paper
Keywords:distribution network state, information entropy, network observability, optimal meter placement
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:97633
Year Published:2014
Deposited By:Engineering
Deposited On:2015-01-05
Last Modified:2017-11-06

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