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.
Highlights
A new multi-objective artificial bee colony (MOABC) method was proposed to optimize the planning of Passive power filters (PPFs) design
Four types of PPFs were considered in the optimization problem
generational distance (GD) was used to evaluate the accuracy of the results obtained by MOABC and to compare the results with those obtained by MOPSO
Summary
Nonlinear loads such as rectifiers, power converters, computers, televisions, and a multitude of others have become indispensable in the modern world They have some disadvantages owing to their application, and harmonics are one of them [1,2]. Harmonics in power systems is a major and typical problem that occurs because of the distortion in the current and voltage waveforms that use nonlinear loads [1,3,4,5]. In [31], a more recent method that uses teaching-learningbased optimization (TLBO) with Pareto optimality was developed This method can perform well, the deployment of TLBO and Pareto to obtain the desired PPF design requires good integration practices between the external archive and fuzzy system for decision making
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