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Optimal Protection Scheme for Enhancing AC Microgrids Stability against Cascading Outages by Utilizing Events Scale Reduction Technique and Fuzzy Zero-Violation Clustering Algorithm

Author

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  • Hossein Karimkhan Zand

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Kazem Mazlumi

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Amir Bagheri

    (Department of Electrical Engineering, Faculty of Engineering, University of Zanjan, Zanjan 45371-38791, Iran)

  • Hamed Hashemi-Dezaki

    (Department of Electrical and Computer Engineering, University of Kashan, Kashan 8731753153, Iran)

Abstract

The precision with which directional overcurrent relays (DOCRs) are set up establishes the microgrid customers’ access to reliable and uninterrupted electricity. In order to avoid failure in DOCRs operation, it is critical to consider a single contingency ( N-1 event) on the protection optimization setting problem (POSP). However, power systems may face cascading outages or simultaneous contingencies ( N-K events), which greatly expand the problem’s complexity and scale. The effect of cascading events on this problem is an open research gap. Initially, this paper proposes a novel approach to reducing the scale of simultaneous events called the N-K events scale reduction technique ( N-K -ESRT). Moreover, an innovative method named fuzzy zero-violation clustering is utilized to group these contingencies. Ultimately, the DOCRs’ decision parameters are generated by three optimization algorithms, namely interior point (IPA), simulated annealing, and pattern search. In all case studies (including a real industrial network called TESKO2 feeder, the IEEE Std. 399-1997, and the IEEE 14 bus systems), the capabilities of the proposed method are effectively validated based on the DOCR’s tripping time and the algorithm’s execution time.

Suggested Citation

  • Hossein Karimkhan Zand & Kazem Mazlumi & Amir Bagheri & Hamed Hashemi-Dezaki, 2023. "Optimal Protection Scheme for Enhancing AC Microgrids Stability against Cascading Outages by Utilizing Events Scale Reduction Technique and Fuzzy Zero-Violation Clustering Algorithm," Sustainability, MDPI, vol. 15(21), pages 1-27, November.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15550-:d:1272728
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    References listed on IDEAS

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    1. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    2. Ahmed M. Agwa & Attia A. El-Fergany, 2023. "Protective Relaying Coordination in Power Systems Comprising Renewable Sources: Challenges and Future Insights," Sustainability, MDPI, vol. 15(9), pages 1-25, April.
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