A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System
Progressing demand can lead to voltage decay in a power system which causes under-voltage phenomenon. Load shedding is a reliable last option to secure a power system from possible voltage collapse occurrence when unintended disturbance occurs. Under Voltage Load Shedding (UVLS) is one of the suitab...
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my.uniten.dspace-369792025-03-03T15:46:18Z A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System Shukor S.F.A. Musirin I. Hamid Z.A. Senthil Kumar A.V. Mansor M.H. Salimin R.H. 57211412345 8620004100 36559455000 56888921600 56372667100 25928749500 Computer programming Electric load shedding Electric power plant loads Evolutionary algorithms Power quality Bulk power systems Evolutionary and jaya algorithm Evolutionary programming algorithms Load shedding scheme Load-shedding Multi objective Multi-objectives optimization Power Under-voltage load shedding Undervoltage Multiobjective optimization Progressing demand can lead to voltage decay in a power system which causes under-voltage phenomenon. Load shedding is a reliable last option to secure a power system from possible voltage collapse occurrence when unintended disturbance occurs. Under Voltage Load Shedding (UVLS) is one of the suitable methods to overcome further unsecure operation. This will require a reliable optimization technique to identify the most suitable locations and sizing for UVLS scheme. This paper presents a hybrid multi-objective integrated jaya-evolutionary programming (MOIJEP) algorithm for under voltage load shedding (UVLS) scheme in bulk power system. MOIJEP integrates the features in the original Jaya algorithm into the conventional Evolutionary Programming (EP). A weighted sum multi-objective which considers voltage stability index, loss and minimum voltage in the system is formulated in this study, implemented for multi-load shedding scheme. Results obtained using the proposed MOIJEP are superior to the benchmarked technique, i.e., MOIJEP when validated on the IEEE 57-Bus Reliability Test System (RTS). ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Final 2025-03-03T07:46:18Z 2025-03-03T07:46:18Z 2024 Conference paper 10.1007/978-981-97-3940-0_76 2-s2.0-85198726804 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198726804&doi=10.1007%2f978-981-97-3940-0_76&partnerID=40&md5=799298d0bc06df11ea498ff78f4036a9 https://irepository.uniten.edu.my/handle/123456789/36979 1208 LNEE 689 696 Springer Science and Business Media Deutschland GmbH Scopus |
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Computer programming Electric load shedding Electric power plant loads Evolutionary algorithms Power quality Bulk power systems Evolutionary and jaya algorithm Evolutionary programming algorithms Load shedding scheme Load-shedding Multi objective Multi-objectives optimization Power Under-voltage load shedding Undervoltage Multiobjective optimization |
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Computer programming Electric load shedding Electric power plant loads Evolutionary algorithms Power quality Bulk power systems Evolutionary and jaya algorithm Evolutionary programming algorithms Load shedding scheme Load-shedding Multi objective Multi-objectives optimization Power Under-voltage load shedding Undervoltage Multiobjective optimization Shukor S.F.A. Musirin I. Hamid Z.A. Senthil Kumar A.V. Mansor M.H. Salimin R.H. A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
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Progressing demand can lead to voltage decay in a power system which causes under-voltage phenomenon. Load shedding is a reliable last option to secure a power system from possible voltage collapse occurrence when unintended disturbance occurs. Under Voltage Load Shedding (UVLS) is one of the suitable methods to overcome further unsecure operation. This will require a reliable optimization technique to identify the most suitable locations and sizing for UVLS scheme. This paper presents a hybrid multi-objective integrated jaya-evolutionary programming (MOIJEP) algorithm for under voltage load shedding (UVLS) scheme in bulk power system. MOIJEP integrates the features in the original Jaya algorithm into the conventional Evolutionary Programming (EP). A weighted sum multi-objective which considers voltage stability index, loss and minimum voltage in the system is formulated in this study, implemented for multi-load shedding scheme. Results obtained using the proposed MOIJEP are superior to the benchmarked technique, i.e., MOIJEP when validated on the IEEE 57-Bus Reliability Test System (RTS). ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. |
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57211412345 |
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57211412345 Shukor S.F.A. Musirin I. Hamid Z.A. Senthil Kumar A.V. Mansor M.H. Salimin R.H. |
format |
Conference paper |
author |
Shukor S.F.A. Musirin I. Hamid Z.A. Senthil Kumar A.V. Mansor M.H. Salimin R.H. |
author_sort |
Shukor S.F.A. |
title |
A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
title_short |
A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
title_full |
A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
title_fullStr |
A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
title_full_unstemmed |
A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System |
title_sort |
hybrid multi-objective integrated jaya-evolutionary programming (moijep) algorithm for under voltage load shedding (uvls) scheme in bulk power system |
publisher |
Springer Science and Business Media Deutschland GmbH |
publishDate |
2025 |
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1825816038548176896 |
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13.244109 |