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A Multi-Variable DTR Algorithm for the Estimation of Conductor Temperature and Ampacity on HV Overhead Lines by IoT Data Sensors

Author

Listed:
  • Rossana Coccia

    (Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy)

  • Veronica Tonti

    (TERNA S.p.A., Viale Egidio Galbani, 70, 00156 Rome, Italy)

  • Chiara Germanò

    (ELIS Innovation Hub, Via Sandro Sandri, 81, 00159 Rome, Italy)

  • Francesco Palone

    (TERNA S.p.A., Viale Egidio Galbani, 70, 00156 Rome, Italy)

  • Lorenzo Papi

    (TERNA S.p.A., Viale Egidio Galbani, 70, 00156 Rome, Italy)

  • Lorenzo Ricciardi Celsi

    (ELIS Innovation Hub, Via Sandro Sandri, 81, 00159 Rome, Italy)

Abstract

The transfer capabilities of High-Voltage Overhead Lines (HV OHLs) are often limited by the critical power line temperature that depends on the magnitude of the transferred current and the ambient conditions, i.e., ambient temperature, wind, etc. To utilize existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to the critical power line temperatures, this paper proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on some HV OHLs located in different Italian geographical locations. The goal is to estimate the OHL conductor temperature and ampacity, using a data-driven thermo-mechanical model with the Bayesian probability approach, in order to improve the confidence interval of the results. This work highlights that it could be possible to estimate a space-time distribution of temperature for each OHL and an increase in the actual current threshold values for optimizing OHL ampacity. The proposed model is validated using the Monte Carlo method.

Suggested Citation

  • Rossana Coccia & Veronica Tonti & Chiara Germanò & Francesco Palone & Lorenzo Papi & Lorenzo Ricciardi Celsi, 2022. "A Multi-Variable DTR Algorithm for the Estimation of Conductor Temperature and Ampacity on HV Overhead Lines by IoT Data Sensors," Energies, MDPI, vol. 15(7), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2581-:d:785322
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    References listed on IDEAS

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    1. Eleonora Arena & Alessandro Corsini & Roberto Ferulano & Dario Alfio Iuvara & Eric Stefan Miele & Lorenzo Ricciardi Celsi & Nour Alhuda Sulieman & Massimo Villari, 2021. "Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis," Energies, MDPI, vol. 14(13), pages 1-16, July.
    2. Hideharu Sugihara & Tsuyoshi Funaki & Nobuyuki Yamaguchi, 2017. "Evaluation Method for Real-Time Dynamic Line Ratings Based on Line Current Variation Model for Representing Forecast Error of Intermittent Renewable Generation," Energies, MDPI, vol. 10(4), pages 1-16, April.
    3. Nour Alhuda Sulieman & Lorenzo Ricciardi Celsi & Wei Li & Albert Zomaya & Massimo Villari, 2022. "Edge-Oriented Computing: A Survey on Research and Use Cases," Energies, MDPI, vol. 15(2), pages 1-28, January.
    4. Fabio Massaro & Mariano Giuseppe Ippolito & Gaetano Zizzo & Giovanni Filippone & Andrea Puccio, 2018. "Methodologies for the Exploitation of Existing Energy Corridors. GIS Analysis and DTR Applications," Energies, MDPI, vol. 11(4), pages 1-15, April.
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