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Optimal Design of Passive Power Filters Using the MRFO Algorithm and a Practical Harmonic Analysis Approach including Uncertainties in Distribution Networks

Author

Listed:
  • Thamer A. H. Alghamdi

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
    Electrical Engineering Department, School of Engineering, Albaha University, Albaha 7738-65799, Saudi Arabia)

  • Fatih Anayi

    (Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Michael Packianather

    (High Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

Abstract

The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is proposed to investigate the harmonic performance of such an optimally designed PPF with variations in power networks. The MRFO algorithm has shown a superior solution-finding ability, but a relatively higher computational effort in comparison with other recently proposed algorithms. The harmonic performance of the optimal PPF solution with uncertainties was analyzed using the proposed method. The results imply that the optimally designed PPF can effectively attenuate the high-order harmonics and improved the system performance parameters over different operating conditions to continually comply with the standard limits. The proposed MCS method showed that the optimally designed PPF reduced the voltage and current distortions by roughly 54% and 30%, respectively, and improved the network hosting capacity by 10% for the worst-case scenario.

Suggested Citation

  • Thamer A. H. Alghamdi & Fatih Anayi & Michael Packianather, 2022. "Optimal Design of Passive Power Filters Using the MRFO Algorithm and a Practical Harmonic Analysis Approach including Uncertainties in Distribution Networks," Energies, MDPI, vol. 15(7), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2566-:d:784959
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    References listed on IDEAS

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    1. Nien-Che Yang & Sun-Wei Liu, 2021. "Multi-Objective Teaching–Learning-Based Optimization with Pareto Front for Optimal Design of Passive Power Filters," Energies, MDPI, vol. 14(19), pages 1-24, October.
    2. Ismael, Sherif M. & Abdel Aleem, Shady H.E. & Abdelaziz, Almoataz Y. & Zobaa, Ahmed F., 2019. "State-of-the-art of hosting capacity in modern power systems with distributed generation," Renewable Energy, Elsevier, vol. 130(C), pages 1002-1020.
    3. Nien-Che Yang & Danish Mehmood, 2022. "Multi-Objective Bee Swarm Optimization Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems," Mathematics, MDPI, vol. 10(1), pages 1-20, January.
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    Cited by:

    1. Thamer A. H. Alghamdi & Fatih Anayi & Michael Packianather, 2022. "Modelling and Control Development of a Cascaded NPC-Based MVDC Converter for Harmonic Analysis Studies in Power Distribution Networks," Energies, MDPI, vol. 15(13), pages 1-24, July.
    2. Anthoula Menti & Pavlos Pachos & Constantinos S. Psomopoulos, 2025. "Supraharmonic Distortion at the Grid Connection Point of a Network Comprising a Photovoltaic System," Energies, MDPI, vol. 18(3), pages 1-31, January.

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