LAPSE:2023.3535
Published Article

LAPSE:2023.3535
Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight
February 22, 2023
Abstract
Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching−learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems’ reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system’s voltage profile.
Part of the widely discussed problem in electrical power systems is the optimal reactive power dispatch (ORPD) due to its reliability and economical operation of electrical power systems. The ORPD is a complex and nonlinear optimization problem. The pathfinder algorithm (PFA) is a newly developed algorithm that inspires the group movement of prey with a leader called a pathfinder when hunting for food. The inertia weight is added to the PFA and is called an improved pathfinder algorithm (IPFA) to support the proper random work of the swarm to avoid the decrease in searchability of the PFA. The IPFA was proposed in this work to diminish the active power loss while improving the voltage profile. The IPFA was validated on the IEEE 30 and 118 bus systems along with particle swarm optimization (PSO) and the teaching−learning-based optimizer (TLBO). The proposed IPFA provides the best result as the losses of the IEEE 30 and 118 test systems were reduced to 16.035 and 115.048 MW from the initial base of 17.89 and 132.86 MW, respectively. The losses of PSO and the TLBO were 16.1568 and 16.1607 MW for the IEEE 30 bus system, respectively, while for the IEEE 118 bus system, the PSO provided 117.9129 MW and the TLBO provided 118.0524 MW. The two test systems’ reduction percentages (%) were 10.37% and 13.41%, respectively. The results were compared with those of other algorithms in the literature, and the IPFA provided a superior result, thereby suggesting the superiority of IPFA methods in diminishing the power loss and improving the system’s voltage profile.
Record ID
Keywords
diminished active power loss, improved pathfinder algorithm, ORPD, pathfinder algorithm
Suggested Citation
Adegoke SA, Sun Y. Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight. (2023). LAPSE:2023.3535
Author Affiliations
Adegoke SA: Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa [ORCID]
Sun Y: Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Sun Y: Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
Journal Name
Energies
Volume
16
Issue
3
First Page
1270
Year
2023
Publication Date
2023-01-25
ISSN
1996-1073
Version Comments
Original Submission
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PII: en16031270, Publication Type: Journal Article
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LAPSE:2023.3535
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https://doi.org/10.3390/en16031270
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Feb 22, 2023
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