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Utilising Smart-Meter Harmonic Data for Low-Voltage Network Topology Identification

Author

Listed:
  • Ali Othman

    (Electrical & Computer Engineering Department, University of Canterbury, Christchurch 8041, New Zealand)

  • Neville R. Watson

    (Electrical & Computer Engineering Department, University of Canterbury, Christchurch 8041, New Zealand)

  • Andrew Lapthorn

    (Electrical & Computer Engineering Department, University of Canterbury, Christchurch 8041, New Zealand)

  • Radnya Mukhedkar

    (EPECentre, University of Canterbury, Christchurch 8041, New Zealand)

Abstract

Identifying the topology of low-voltage (LV) networks is becoming increasingly important. Having precise and accurate topology information is crucial for future network operations and network modelling. Topology identification approaches based on smart-meter data typically rely on Root Mean Square (RMS) voltage, current, and power measurements, which are limited in accuracy due to factors such as time resolution, measurement intervals, and instrument errors. This paper presents a novel methodology for identifying distribution network topologies through the utilisation of smart-meter harmonic data. The methodology introduces, for the first time, the application of voltage Total Harmonic Distortion (THD) and individual harmonic components ( V 2 – V 20 ) as topology identifiers. The proposed approach leverages the unique properties of harmonic distortion to improve the accuracy of topology identification. This paper first analyses the influential factors affecting topology identification, establishing that harmonic distortion propagation patterns offer superior discrimination compared to RMS voltage. Through systematic investigation, the findings demonstrate the potential of harmonic-based analysis as a more effective alternative for topology identification in modern power distribution systems.

Suggested Citation

  • Ali Othman & Neville R. Watson & Andrew Lapthorn & Radnya Mukhedkar, 2025. "Utilising Smart-Meter Harmonic Data for Low-Voltage Network Topology Identification," Energies, MDPI, vol. 18(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3333-:d:1687137
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    References listed on IDEAS

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    1. André Ulrich & Sergej Baum & Ingo Stadler & Christian Hotz & Eberhard Waffenschmidt, 2023. "Maximising Distribution Grid Utilisation by Optimising E-Car Charging Using Smart Meter Gateway Data," Energies, MDPI, vol. 16(9), pages 1-20, April.
    2. McKenna, Eoghan & Thomson, Murray, 2016. "High-resolution stochastic integrated thermal–electrical domestic demand model," Applied Energy, Elsevier, vol. 165(C), pages 445-461.
    3. Chong Wang & Zheng Lou & Ming Li & Chaoyang Zhu & Dongsheng Jing, 2024. "Identification of Distribution Network Topology and Line Parameter Based on Smart Meter Measurements," Energies, MDPI, vol. 17(4), pages 1-19, February.
    4. Wang, Fei & Lu, Xiaoxing & Chang, Xiqiang & Cao, Xin & Yan, Siqing & Li, Kangping & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data," Energy, Elsevier, vol. 238(PB).
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    1. Xihao Guo & Chenghao Xu & Zixiang Ming & Bo Meng & Shan Yang & Linna Xu & Yongli Zhu, 2025. "User–Feeder Topology Identification in Low-Voltage Residential Power Networks: A Clustering Fusion Approach," Energies, MDPI, vol. 18(18), pages 1-26, September.

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