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Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm

Author

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  • Mahmoud Abdelsalam

    (Electrical Energy Department, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Giza 12212, Egypt)

  • Hatem Y. Diab

    (Electrical Energy Department, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Giza 12212, Egypt)

Abstract

Distributed, generation-based micro-grids are increasingly being used in the build-up of the modern power system. However, the protection of these micro-grids has many challenges. One of the important challenges is the coordination of directional overcurrent (DOC) relays. The optimization of the coordination of DOC relays is considered a nonlinear programming problem with pre-defined constrains. In this paper, the problem of the optimal coordination of DOC relays is solved using a multi-verse optimization (MVO) algorithm which is inspired from cosmology science. The proposed algorithm is tested by applying it to Institute of Electrical and Electronics Engineers (IEEE) 3 bus and IEEE 9 bus networks. The performance of the proposed algorithm is compared with the particle swarm optimization (PSO) algorithm when applied to both networks. All results show that the performance of the MVO algorithm is better than PSO in terms of its reduction of both the overall operating time (OT) of DOC relays and the computational burden of the computer solving the optimization problem.

Suggested Citation

  • Mahmoud Abdelsalam & Hatem Y. Diab, 2019. "Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm," Energies, MDPI, vol. 12(21), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4115-:d:281091
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    References listed on IDEAS

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    1. Jakub Ehrenberger & Jan Švec, 2017. "Directional Overcurrent Relays Coordination Problems in Distributed Generation Systems," Energies, MDPI, vol. 10(10), pages 1-17, September.
    2. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    3. Lincun Fang & Shiyin Qin & Gang Xu & Tianli Li & Kemin Zhu, 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms," Energies, MDPI, vol. 4(3), pages 1-13, March.
    4. Krystel K. Castillo-Villar, 2014. "Metaheuristic Algorithms Applied to Bioenergy Supply Chain Problems: Theory, Review, Challenges, and Future," Energies, MDPI, vol. 7(11), pages 1-33, November.
    5. Abdul Wadood & Tahir Khurshaid & Saeid Gholami Farkoush & Jiangtao Yu & Chang-Hwan Kim & Sang-Bong Rhee, 2019. "Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems," Energies, MDPI, vol. 12(12), pages 1-27, June.
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    Cited by:

    1. Thiramuni Sisitha Sameera Senarathna & Kullappu Thantrige Manjula Udayanga Hemapala, 2020. "Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids," Energies, MDPI, vol. 13(13), pages 1-23, June.
    2. Faraj Al-Bhadely & Aslan İnan, 2023. "Improving Directional Overcurrent Relay Coordination in Distribution Networks for Optimal Operation Using Hybrid Genetic Algorithm with Sequential Quadratic Programming," Energies, MDPI, vol. 16(20), pages 1-21, October.
    3. Hatem Diab & Mahmoud Abdelsalam & Alaa Abdelbary, 2021. "A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques," Sustainability, MDPI, vol. 13(9), pages 1-25, April.

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