Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

- Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimizatio...

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Published in:JOURNAL OF ELECTRICAL SYSTEMS
Main Authors: Aziz, Muhammad Zharif Mat; Musirin, Ismail; Mansor, Mohd Helmi; Ismail, Saiful Amri; Abdullah, Azlina; Shaaya, Sharifah Azwa; Kumar, A. V. Senthil
Format: Article
Language:English
Published: ENGINEERING & SCIENTIFIC RESEARCH GROUPS 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001229171600013
author Aziz
Muhammad Zharif Mat; Musirin
Ismail; Mansor
Mohd Helmi; Ismail
Saiful Amri; Abdullah
Azlina; Shaaya
Sharifah Azwa; Kumar
A. V. Senthil
spellingShingle Aziz
Muhammad Zharif Mat; Musirin
Ismail; Mansor
Mohd Helmi; Ismail
Saiful Amri; Abdullah
Azlina; Shaaya
Sharifah Azwa; Kumar
A. V. Senthil
Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
Engineering
author_facet Aziz
Muhammad Zharif Mat; Musirin
Ismail; Mansor
Mohd Helmi; Ismail
Saiful Amri; Abdullah
Azlina; Shaaya
Sharifah Azwa; Kumar
A. V. Senthil
author_sort Aziz
spelling Aziz, Muhammad Zharif Mat; Musirin, Ismail; Mansor, Mohd Helmi; Ismail, Saiful Amri; Abdullah, Azlina; Shaaya, Sharifah Azwa; Kumar, A. V. Senthil
Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
JOURNAL OF ELECTRICAL SYSTEMS
English
Article
- Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.
ENGINEERING & SCIENTIFIC RESEARCH GROUPS
1112-5209

2024
20
7

Engineering

WOS:001229171600013
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001229171600013
title Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
title_short Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
title_full Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
title_fullStr Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
title_full_unstemmed Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
title_sort Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy
container_title JOURNAL OF ELECTRICAL SYSTEMS
language English
format Article
description - Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.
publisher ENGINEERING & SCIENTIFIC RESEARCH GROUPS
issn 1112-5209

publishDate 2024
container_volume 20
container_issue 7
doi_str_mv
topic Engineering
topic_facet Engineering
accesstype
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url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001229171600013
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