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Multi-Objective Teaching–Learning-Based Optimization with Pareto Front for Optimal Design of Passive Power Filters

Author

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  • Nien-Che Yang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No.43, Keelung Road, Section 4, Taipei 10607, Taiwan)

  • Sun-Wei Liu

    (Department of Electrical Engineering, Yuan Ze University, No.135, Yuan-Tung Road, Chung-Li, Taoyuan 32003, Taiwan)

Abstract

This paper proposes an optimal design method to suppress critical harmonics and improve the power factor by using passive power filters (PPFs). The main objectives include (1) minimizing the total harmonic distortion of voltage and current, (2) minimizing the initial investment cost, and (3) maximizing the total fundamental reactive power compensation. A methodology based on teaching–learning-based optimization (TLBO) and Pareto optimality is proposed and used to solve this multi-objective PPF design problem. The proposed method is integrated with both external archive and fuzzy decision making. The sub-group search strategy and teacher selection strategy are used to improve the diversity of non-dominated solutions (NDSs). In addition, a selection mechanism for topology combinations for PPFs is proposed. A series of case studies are also conducted to demonstrate the performance and effectiveness of the proposed method. With the proposed selection mechanisms for the topology combinations and parameters for PPFs, the best compromise solution for a complete PPF design is achieved.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6408-:d:651230
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2022. "A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies," Mathematics, MDPI, vol. 10(13), pages 1-26, June.
    2. 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.
    3. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.
    4. Shahenda Sarhan & Abdullah M. Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "Enhanced Teaching Learning-Based Algorithm for Fuel Costs and Losses Minimization in AC-DC Systems," Mathematics, MDPI, vol. 10(13), pages 1-22, July.
    5. 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.

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