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Communication-Less Data-Driven Coordination Technique for Hybrid AC/DC Transmission Networks

Author

Listed:
  • Arif Mehdi

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Syed Jarjees Ul Hassan

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Zeeshan Haider

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Ho-Young Kim

    (Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea)

  • Arif Hussain

    (Electrical Engineering Department, Iowa University, Iowa City, IA 52242, USA)

Abstract

There is a paradigm shift to hybrid (AC/DC) networks that integrate both AC and DC to meet growing energy demands, mitigate global warming, and interconnect distributed energy sources (DERs). However, the unique characteristics of AC/DC faults, the mutual interaction of hybrid lines, the harmonic components of converters/inverters, multiple directions of energy flow, and varying current levels have challenged the existing protection algorithms. Therefore, this paper presents a data-driven coordination AC/DC fault protection algorithm. The algorithm utilizes faulty voltage and current signals to retrieve the precise time-domain characteristics of AC, DC, and intersystem (IS) faults to develop the algorithm. The proposed algorithm consists of four stages: stage 1 includes the detection of faults, stage 2 identifies the fault as either AC or DC, stage 3 classifies the respective AC and DC faults, and stage 4 locates the AC/DC fault precisely. The hybrid test system is developed in a MATLAB/Simulink environment, and the data-driven algorithm is trained and tested in Python. The extensive simulation results for multiple fault cases, either AC or DC, and the comparisons of various performance indicators confirm the effectiveness of the developed algorithm, which performs efficiently under a noisy and extended hybrid AC/DC network. Compared to other schemes, the proposed coordination protection approach can enhance the speed and accuracy of hybrid AC/DC networks.

Suggested Citation

  • Arif Mehdi & Syed Jarjees Ul Hassan & Zeeshan Haider & Ho-Young Kim & Arif Hussain, 2025. "Communication-Less Data-Driven Coordination Technique for Hybrid AC/DC Transmission Networks," Energies, MDPI, vol. 18(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1416-:d:1611222
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    References listed on IDEAS

    as
    1. Wang, Ting & Zhang, Chunyan & Hao, Zhiguo & Monti, Antonello & Ponci, Ferdinanda, 2023. "Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach," Applied Energy, Elsevier, vol. 336(C).
    2. Raad Salih Jawad & Hafedh Abid, 2023. "HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method," Energies, MDPI, vol. 16(3), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

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