Investigation of load variant under power distribution network reconfiguration using EPSO algorithm
Recently, the power loss issue has emerged as a critical challenge, causing significant disruptions in the nation's infrastructure, economy, and daily lives of its citizens. Despite being a rapidly developing country with a growing demand for electricity, frequent instances of power loss and in...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wydawnictwo SIGMA-NOT
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/27830/2/019010708202416253.pdf http://eprints.utem.edu.my/id/eprint/27830/ http://pe.org.pl/articles/2024/5/22.pdf |
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| Summary: | Recently, the power loss issue has emerged as a critical challenge, causing significant disruptions in the nation's infrastructure, economy, and daily lives of its citizens. Despite being a rapidly developing country with a growing demand for electricity, frequent instances of power loss and interruption have resulted in severe consequences such as reduced productivity, financial losses, compromised public safety, and increased inconvenience to individuals and businesses. Due to that reason, this study proposes the Evolutionary Particle Swarm Optimization (EPSO) algorithm which is a hybrid optimization technique that combines the principles of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) to solve optimization problems by reducing the power losses under Distribution Network Reconfiguration (DNR). Moreover, the consideration of load variants involved in DNR while validating the voltage profile improvement with the best load weightage has been made concurrently. A detailed performance analysis is carried out on IEEE 33-bus test systems to demonstrate the effectiveness of the proposed method. Through simultaneous optimization, it was found that power loss reduction was achieved after conducting power DNR in a radial network connection. Furthermore, the test result also indicated that the EPSO algorithm produced better results in terms of convergence time compared to the conventional PSO algorithm. |
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