Optimal design of passive power filters using the MRFO algorithm and a practical harmonic analysis approach including uncertainties in distribution networks

Alghamdi, Thamer A. H., Anayi, Fatih ORCID: https://orcid.org/0000-0001-8408-7673 and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2022. Optimal design of passive power filters using the MRFO algorithm and a practical harmonic analysis approach including uncertainties in distribution networks. Energies 15 (7) , 2566. 10.3390/en15072566

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
Publisher:	MDPI
ISSN:	1996-1073
Date of First Compliant Deposit:	7 April 2022
Date of Acceptance:	29 March 2022
Last Modified:	27 Sep 2024 14:50
URI:	https://orca.cardiff.ac.uk/id/eprint/149098

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