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

Nien-Che Yang and Sun-Wei Liu
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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

Energies, 2021, vol. 14, issue 19, 1-24

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.

Keywords: harmonic; passive power filters; teaching–learning-based optimization; Pareto front; sub-group search strategy; teacher selection strategy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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