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Optimal Service Restoration Scheme for Radial Distribution Network Using Teaching Learning Based Optimization

Author

Listed:
  • Mulusew Ayalew

    (Department of Electrical and Computer Engineering, Dilla University, Dilla 419, Ethiopia)

  • Baseem Khan

    (Department of Electrical and Computer Engineering, Hawassa University, Hawassa 005, Ethiopia)

  • Zuhair Muhammed Alaas

    (Electrical Engineering Department, College of Engineering, Jazan University, P.O. Box 114, Jazan 45142, Saudi Arabia)

Abstract

In the event of a fault isolation process, all loads located downstream from the faulted point become out of service, and as a consequence, the power interruption affects a greater portion of the radial distribution system. This paper proposes an optimal Service Restoration (SR) method that entails changing the network topology configuration via optimal tie-switch and section switch combinations. However, when the network topology configuration is performed, it results in increased load currents. As a result, some Protective Devices (PDs) can operate undesirably and some network branches may become unprotected. Therefore, it is essential to consider protection constraints in the SR problem to maintain service continuity during power interruptions. The proposed method aims at optimal SR with minimum out-of-service loads, minimum power loss, and improved voltage profiles and at the same time ensures PDs operate correctly during the normal and overloading conditions. The proposed method was carried out on the Debre Markos distribution networks, using the Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Differential Evolutionary (DEV) algorithms. The proposed SR was carried out considering and without considering protection constraints. The obtained SR topology was not feasible for SR without considering protection constraints, since some PDs fail to operate properly in normal loading conditions. After executing the proposed SR algorithms by considering protection constraints for a single fault case, the power loss reductions in TLBO, DEV, and PSO were 64.9073%, 45.9073%, and 55.358 %, respectively. The minimum voltage profiles obtained in each proposed TLBO, DEV, and PSO algorithm were 0.96%, 0.95%, and 0.96%, respectively. In each algorithm, except for the branch under fault, all healthy out-of-service branches were restored. When the protection constraints were considered in an optimal SR, load current did not exceed the rating of the fuses. The results show the importance of considering protection constraints during SR to prevent dysfunction of the PDs in the network. Comparative analyses were carried out on each algorithm and TLBO algorithms performed better than PSO and DEV for search functions.

Suggested Citation

  • Mulusew Ayalew & Baseem Khan & Zuhair Muhammed Alaas, 2022. "Optimal Service Restoration Scheme for Radial Distribution Network Using Teaching Learning Based Optimization," Energies, MDPI, vol. 15(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2505-:d:782206
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    Citations

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    Cited by:

    1. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2022. "A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    2. Shahenda Sarhan & Abdullah M. Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems," Mathematics, MDPI, vol. 10(13), pages 1-22, July.
    3. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.

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