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Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems

Author

Listed:
  • Nien-Che Yang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • Danish Mehmood

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

  • Kai-You Lai

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan)

Abstract

Passive power filters (PPFs) are most effective in mitigating harmonic pollution from power systems; however, the design of PPFs involves several objectives, which makes them a complex multiple-objective optimization problem. This study proposes a method to achieve an optimal design of PPFs. We have developed a new multi-objective optimization method based on an artificial bee colony (ABC) algorithm with a minimum Manhattan distance. Four different types of PPFs, namely, single-tuned, second-order damped, third-order damped, and C-type damped order filters, and their characteristics were considered in this study. A series of case studies have been presented to prove the efficiency and better performance of the proposed method over previous well-known algorithms.

Suggested Citation

  • Nien-Che Yang & Danish Mehmood & Kai-You Lai, 2021. "Multi-Objective Artificial Bee Colony Algorithm with Minimum Manhattan Distance for Passive Power Filter Optimization Problems," Mathematics, MDPI, vol. 9(24), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:24:p:3187-:d:699589
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    References listed on IDEAS

    as
    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.
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    3. Kalair, A. & Abas, N. & Kalair, A.R. & Saleem, Z. & Khan, N., 2017. "Review of harmonic analysis, modeling and mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1152-1187.
    4. Bahriye Akay, 2013. "Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms," Journal of Global Optimization, Springer, vol. 57(2), pages 415-445, October.
    5. Xiang, Yi & Zhou, Yuren & Liu, Hailin, 2015. "An elitism based multi-objective artificial bee colony algorithm," European Journal of Operational Research, Elsevier, vol. 245(1), pages 168-193.
    6. 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.
    7. Wenping Zou & Yunlong Zhu & Hanning Chen & Beiwei Zhang, 2011. "Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2011, pages 1-37, November.
    8. Yi Xiang & Yuming Peng & Yubin Zhong & Zhenyu Chen & Xuwen Lu & Xuejun Zhong, 2014. "A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization," Computational Optimization and Applications, Springer, vol. 57(2), pages 493-516, March.
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