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...
Published in: | JOURNAL OF ELECTRICAL SYSTEMS |
---|---|
Main Authors: | , , , , , , , |
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 |
|
id |
WOS:001229171600013 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001229171600013 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809679004862513152 |