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Reactive Power Management Based Hybrid GAEO

Author

Listed:
  • Mahmoud Hemeida

    (Minia Higher Institute of Engineering, Minya 61111, Egypt)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nishihara 903-0213, Japan)

  • Salem Alkhalaf

    (Department of Computer, College of Science and Arts in Ar-Rass, Qassim University, Ar Rass 52571, Saudi Arabia)

  • Asmaa Fawzy

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Mahrous Ahmed

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Dina Osheba

    (Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El Kom 32511, Egypt)

Abstract

Electrical power networks are expanded regularly to meet growing energy requirements. Reactive power dispatch (RPD) optimization is a powerful tool to enhance a system’s efficiency, reliability, and security. RPD optimization is classified as a non-linear and non-convex problem. In this paper, the RPD optimization problem is solved based on novel hybrid genetic algorithms—equilibrium optimizer (GAEO) optimization algorithms. The control variables are determined in such a way that optimizes RPD and minimizes power losses. The efficiency of the proposed optimization algorithms is compared to other techniques that have been used recently to solve the RPD problem. The proposed algorithm has been tested for optimization RPD for three test systems, IEEE14-bus, IEEE-30bus, and IEEE57-bus. The obtained results show the superiority of GAEO over other techniques for small test systems, IEEE14-bus and IEEE-30bus. GAEO shows good results for large system, IEEE 57-bus.

Suggested Citation

  • Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6933-:d:832815
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    References listed on IDEAS

    as
    1. Zahir Sahli & Abdellatif Hamouda & Abdelghani Bekrar & Damien Trentesaux, 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †," Energies, MDPI, vol. 11(8), pages 1-21, August.
    2. Andrei M. Tudose & Irina I. Picioroaga & Dorian O. Sidea & Constantin Bulac, 2021. "Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm," Energies, MDPI, vol. 14(5), pages 1-20, February.
    3. Mini Vishnu & Sunil Kumar T. K., 2020. "An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization," Energies, MDPI, vol. 13(11), pages 1-21, June.
    4. Salem Alkhalaf & Tomonobu Senjyu & Ayat Ali Saleh & Ashraf M. Hemeida & Al-Attar Ali Mohamed, 2019. "A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    5. Yongquan Zhou & Jinzhong Zhang & Xiao Yang & Ying Ling, 2020. "Optimal reactive power dispatch using water wave optimization algorithm," Operational Research, Springer, vol. 20(4), pages 2537-2553, December.
    6. Ayat Ali Saleh & Tomonobu Senjyu & Salem Alkhalaf & Majed A. Alotaibi & Ashraf M. Hemeida, 2020. "Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models," Energies, MDPI, vol. 13(21), pages 1-24, November.
    7. Tawfiq M. Aljohani & Ahmed F. Ebrahim & Osama Mohammed, 2019. "Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization," Energies, MDPI, vol. 12(12), pages 1-24, June.
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    Cited by:

    1. Khan, Babar Sattar & Qamar, Affaq & Ullah, Farman & Bilal, Muhammad, 2023. "Ingenuity of Shannon entropy-based fractional order hybrid swarming strategy to solve optimal power flows," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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