Optimal Design of Passive Power Filters Using the MRFO Algorithm and a Practical Harmonic Analysis Approach including Uncertainties in Distribution Networks

The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is propose...

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Bibliographic Details
Published in:Energies (Basel) 2022-04, Vol.15 (7), p.2566
Main Authors: Alghamdi, Thamer A. H., Anayi, Fatih, Packianather, Michael
Format: Article
Language:English
Subjects:
Algorithms
Computer applications
Design
Design optimization
Design parameters
Filters
Fourier analysis
Harmonic analysis
harmonics analysis
Higher harmonics
optimal design
Optimization
Optimization algorithms
passive power filter
Performance evaluation
Power
power quality
Uncertainty
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Summary:The design of Passive Power Filters (PPFs) has been widely acknowledged as an optimization problem. This paper addresses the PPF parameters design problem using the novel Manta Ray Foraging Optimization (MRFO) algorithm. Moreover, an analytical method based on Monte Carlo Simulation (MCS) is proposed to investigate the harmonic performance of such an optimally designed PPF with variations in power networks. The MRFO algorithm has shown a superior solution-finding ability, but a relatively higher computational effort in comparison with other recently proposed algorithms. The harmonic performance of the optimal PPF solution with uncertainties was analyzed using the proposed method. The results imply that the optimally designed PPF can effectively attenuate the high-order harmonics and improved the system performance parameters over different operating conditions to continually comply with the standard limits. The proposed MCS method showed that the optimally designed PPF reduced the voltage and current distortions by roughly 54% and 30%, respectively, and improved the network hosting capacity by 10% for the worst-case scenario.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15072566