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A Residual Attention Aware Network Based Faults Characterization of a Power System with SFCL

Conference Paper


Abstract


  • Faults in power systems are responsible to increase the current abruptly that gradually damages the protective equipment and loads. Although the use of a superconducting fault current limiter (SFCL) decreases the level of fault current, it considers 2 cycles for detecting the fault where the conventional circuit breaker takes it up to 5 cycles. In this paper, we propose a deep learning network to detect and classify the fault with a fraction of the cycle for the rapid restoration of faulty phases. The decayed current signals using the SFCL are considered to measure the effectiveness of the proposed learning network. The obtained result shows that the proposed learning approach can detect and classify the fault within the fraction of one cycle.

Publication Date


  • 2020

Citation


  • Fahim, S. R., Sarker, S. K., Das, S. K., Islam, M. R., Kouzani, A. Z., & Parvez Mahmud, M. A. (2020). A Residual Attention Aware Network Based Faults Characterization of a Power System with SFCL. In 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020. doi:10.1109/ASEMD49065.2020.9276122

Scopus Eid


  • 2-s2.0-85099288234

Web Of Science Accession Number


Abstract


  • Faults in power systems are responsible to increase the current abruptly that gradually damages the protective equipment and loads. Although the use of a superconducting fault current limiter (SFCL) decreases the level of fault current, it considers 2 cycles for detecting the fault where the conventional circuit breaker takes it up to 5 cycles. In this paper, we propose a deep learning network to detect and classify the fault with a fraction of the cycle for the rapid restoration of faulty phases. The decayed current signals using the SFCL are considered to measure the effectiveness of the proposed learning network. The obtained result shows that the proposed learning approach can detect and classify the fault within the fraction of one cycle.

Publication Date


  • 2020

Citation


  • Fahim, S. R., Sarker, S. K., Das, S. K., Islam, M. R., Kouzani, A. Z., & Parvez Mahmud, M. A. (2020). A Residual Attention Aware Network Based Faults Characterization of a Power System with SFCL. In 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020. doi:10.1109/ASEMD49065.2020.9276122

Scopus Eid


  • 2-s2.0-85099288234

Web Of Science Accession Number