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An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization

Author

Listed:
  • Mini Vishnu

    (Electrical Engineering Department, Government Engineering College, Thrissur, Kerala 680009, India)

  • Sunil Kumar T. K.

    (National Institute of Technology, Calicut, Kerala 673601, India)

Abstract

Well-structured reactive power policies and dispatch are major concerns of operation and control technicians of any power system. Obtaining a suitable reactive power dispatch for any given load condition of the system is a prime duty of the system operator. It reduces loss of active power occurring during transmission by regulating reactive power control variables, thus boosting the voltage profile, enhancing the system security and power transfer capability, thereby attaining an improvement in overall system operation. The reactive power dispatch (RPD) problem being a mixed-integer discrete continuous (MIDC) problem demands the solution to contain all these variable types. This paper proposes a methodology to achieve an optimal and practically feasible solution to the RPD problem through the diversity-enhanced particle swarm optimization (DEPSO) technique. The suggested method is characterized by the calculation of the diversity of each particle from its mean position after every iteration. The movement of the particles is decided based on the calculated diversity, thereby preventing both local optima stagnation and haphazard unguided wandering. DEPSO accounts for the accuracy of the variables used in the RPD problem by providing discrete values and integer values compared to other algorithms, which provide all continuous values. The competency of the proposed method is tested on IEEE 14-, 30-, and 118-bus test systems. Simulation outcomes show that the proposed approach is feasible and efficient in attaining minimum active power losses and minimum voltage deviation from the reference. The results are compared to conventional particle swarm optimization (PSO) and JAYA algorithms.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2862-:d:367172
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

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    4. Lenin Kanagasabai, 2022. "Buoyancy based optimization algorithm for real power loss diminution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2442-2457, October.
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    11. Lenin Kanagasabai, 2022. "Tangible power loss lessening by hybridized beautiful demoiselle-enriched particle swarm and pyramid optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 450-468, February.
    12. Lenin Kanagasabai, 2022. "Mathematics based calculation and stemonitis inspired optimization algorithms for loss reduction and power solidity augmentation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2710-2726, October.
    13. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    14. Robert Małkowski & Michał Izdebski & Piotr Miller, 2020. "Adaptive Algorithm of a Tap-Changer Controller of the Power Transformer Supplying the Radial Network Reducing the Risk of Voltage Collapse," Energies, MDPI, vol. 13(20), pages 1-25, October.
    15. 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.
    16. Lenin Kanagasabai, 2022. "Real power loss reduction by quantum based Ptilonorhynchus violaceus optimization and Haliastur Indus algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1913-1931, August.

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