Interval estimation of voltage magnitude in radial distribution feeder with minimal data acquisition requirements

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

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The increased complexity of electric distribution networks, entails higher requirements on the interoperability between the information and communication technology (ICT) infrastructure and the power system. In current literature, distribution network monitoring methods require reliable operation of the ICT infrastructure, and depend on load estimation accuracy in cases of unavailable measurements. In the low voltage (LV) distribution network, ICT infrastructure is sensible to noise and congestion, and load estimation is challenged by irrational consumer behaviour. Such limitation impede the implementation of currently proposed methods. The conditions are complicated further when considering the impact of emerging data privacy concerns that can prevent utilization of household consumption data. Therefore, this work proposes a novel method for voltage magnitude interval estimation by utilizing the information from existing data acquisition systems in LV distribution networks. The proposed method is based on knowledge of load connection, and estimates the entire radial feeder voltage magnitude conditions from measurements of voltage magnitudes from minimum one node. The proposed method can therefore be implemented on existing ICT infrastructure and effectively keeps the privacy of consumers consumption profiles. The ability of the proposed method to estimate the feeder voltage interval is illustrated by an example and evaluated using Monte Carlo simulations for different meter distribution and accuracy scenarios.
Original languageEnglish
JournalInternational Journal of Electrical Power & Energy Systems
Volume113
Pages (from-to)281-287
ISSN0142-0615
DOIs
Publication statusPublished - 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Cyber-physical power system, Interval estimation, Voltage monitoring, Smart meters, Data privacy

ID: 179897948