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A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

Author

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  • Mohamed A. M. Shaheen

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Dalia Yousri

    (Electrical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum 63514, Egypt)

  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakakah 74331, Saudi Arabia
    Electrical Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Hany M. Hasanien

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Abdulaziz Alkuhayli

    (Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia)

  • S. M. Muyeen

    (School of Electrical Engineering Computing and Mathematical Sciences, Curtin University, Perth 6102, Australia)

Abstract

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

Suggested Citation

  • Mohamed A. M. Shaheen & Dalia Yousri & Ahmed Fathy & Hany M. Hasanien & Abdulaziz Alkuhayli & S. M. Muyeen, 2020. "A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem," Energies, MDPI, vol. 13(21), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5679-:d:437487
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    References listed on IDEAS

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    Cited by:

    1. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    2. Mohamed A. M. Shaheen & Hany M. Hasanien & Rania A. Turky & Martin Ćalasan & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "OPF of Modern Power Systems Comprising Renewable Energy Sources Using Improved CHGS Optimization Algorithm," Energies, MDPI, vol. 14(21), pages 1-21, October.
    3. Shaheen, Mohamed A.M. & Ullah, Zia & Hasanien, Hany M. & Tostado-Véliz, Marcos & Ji, Haoran & Qais, Mohammed H. & Alghuwainem, Saad & Jurado, Francisco, 2023. "Enhanced transient search optimization algorithm-based optimal reactive power dispatch including electric vehicles," Energy, Elsevier, vol. 277(C).
    4. Metin Varan & Ali Erduman & Furkan Menevşeoğlu, 2023. "A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems," Energies, MDPI, vol. 16(13), pages 1-28, June.
    5. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).

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