Optimal Design of Passive Power Filters Based on Knowledge-Based Chaotic Evolutionary Algorithm

2008 ◽  
Author(s):  
Yi-nan Guo ◽  
Juan Zhou ◽  
Jian Cheng ◽  
Xing-dong Jiang
Keyword(s):  
Optimal Design ◽  
Evolutionary Algorithm ◽  
Knowledge Based ◽  
Power Filters ◽  
Passive Power

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

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6408
Author(s):  
Nien-Che Yang ◽  
Sun-Wei Liu
Keyword(s):  
Optimal Design ◽  
Reactive Power ◽  
Design Method ◽  
Selection Strategy ◽  
Fuzzy Decision Making ◽  
Multi Objective ◽  
Power Filters ◽  
Passive Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

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