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Fuzzy Dynamic Thermal Rating System-Based Thermal Aging Model for Transmission Lines

Author

Listed:
  • Yasir Yaqoob

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Malaysia)

  • Arjuna Marzuki

    (School of Science and Technology, Wawasan Open University, George Town 10050, Malaysia)

  • Ching-Ming Lai

    (Department of Electrical Engineering, National Chung Hsing University (NCHU), 145 Xing Da Road, South District, Taichung 402, Taiwan)

  • Jiashen Teh

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia (USM), Nibong Tebal 14300, Malaysia)

Abstract

Electricity demand has surged over the last several years and will persist in the future. Increased transmission loads cause transmission lines to operate much closer to their security limits, leading to thermal and mechanical stress and thus affecting the transmission reliability and thermal aging. Accordingly, monitoring the conductor temperature over time is critical to identifying power transmission networks that may need extra attention and perhaps maintenance. This paper presents a fuzzy thermal aging model for transmission lines equipped with a fuzzy dynamic thermal rating system based on the IEEE 738 standard. In this framework, the ampacity of the transmission line was calculated. The conductor temperature was computed with the back-calculation method by considering the fully loaded transmission line. The estimated conductor temperature was employed to determine the corresponding conductor fuzzy loss of tensile strength, i.e., the fuzzy annealing degree of the conductor based on the Harvey model. Additionally, a tensile strength loss cost profile is provided. Simulation and numerical results indicate that the proposed framework is robust against various operating conditions of the parameters considered in the study and provides crucial information for managing transmission assets and transmission network operation.

Suggested Citation

  • Yasir Yaqoob & Arjuna Marzuki & Ching-Ming Lai & Jiashen Teh, 2022. "Fuzzy Dynamic Thermal Rating System-Based Thermal Aging Model for Transmission Lines," Energies, MDPI, vol. 15(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4395-:d:840502
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    References listed on IDEAS

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    1. F. Gülşen Erdinç & Ozan Erdinç & Recep Yumurtacı & João P. S. Catalão, 2020. "A Comprehensive Overview of Dynamic Line Rating Combined with Other Flexibility Options from an Operational Point of View," Energies, MDPI, vol. 13(24), pages 1-30, December.
    2. Jiashen Teh & Ching-Ming Lai & Yu-Huei Cheng, 2018. "Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    3. Ildar Daminov & Rémy Rigo-Mariani & Raphael Caire & Anton Prokhorov & Marie-Cécile Alvarez-Hérault, 2021. "Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime," Energies, MDPI, vol. 14(5), pages 1-27, March.
    4. Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
    5. Chiodo, Elio & Lauria, Davide & Mottola, Fabio & Pisani, Cosimo, 2016. "Lifetime characterization via lognormal distribution of transformers in smart grids: Design optimization," Applied Energy, Elsevier, vol. 177(C), pages 127-135.
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