Abstract

Harmonic distortion in power systems is a significant problem, and it is thus necessary to mitigate critical harmonics. This study proposes an optimal method for designing passive power filters (PPFs) to suppress these harmonics. The design of a PPF involves multi-objective optimization. A multi-objective bee swarm optimization (MOBSO) with Pareto optimality is implemented, and an external archive is used to store the non-dominated solutions obtained. The minimum Manhattan distance strategy was used to select the most balanced solution in the Pareto solution set. A series of case studies are presented to demonstrate the efficiency and superiority of the proposed method. Therefore, the proposed method has a very promising future not only in filter design but also in solving other multi-objective optimization problems.

Highlights

  • Nonlinear loads are commonly used by consumers [1], and the current drawn by these nonlinear loads is not sinusoidal, even though they are connected to a sinusoidal supply

  • Sample System system kV, Hzis isconsidered. This system consists nonlinear loads kV, Hz system consists of of nonlinear loads considered as a harmonic source, and the

  • 197.5 the results show that the passive power filters (PPFs) design by the multi-objective bee swarm optimization (MOBSO) is more balanced than the PPF design

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Summary

Background

Nonlinear loads are commonly used by consumers [1], and the current drawn by these nonlinear loads is not sinusoidal, even though they are connected to a sinusoidal supply. The growth of nonlinear loads causes distortions in the current and voltage waveform [2,3,4], which are represented as harmonics. These harmonics may have several adverse effects on power systems, including power loss, power factor reduction, equipment malfunctioning, deterioration, and damage [5,6,7,8,9,10,11]. Several studies have shown the effectiveness of PPFs in reducing harmonics from nonlinear systems [13,14,15,16,17,18,19]

Literature Review
1.3.Aim
Passive Power Filter Models
Problem Formulation
Minimizing Initial Investment Cost
Maximizing Total Fundamental Reactive Power Compensation
Total Fundamental Reactive Power Compensation
Bee Swarm Optimization Algorithm
Pareto Optimality
External Archive
Minimum Manhattan Distance
Interpretation
Discussion
Setting
36.8 Foragerzise
Accuracy Test
Performance
Case 3
MMD for results
Conclusions
Full Text
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