Comparative Analysis in DG Installation Scheme for Resilience Enhancement
This paper presents a comparative analysis of the Distributed Generation (DG) scheme for resilience enhancement. This study models categories of hurricanes as disruptive events, considering data on the fragility of transmission towers concerning wind speeds. The simulation involves generating sustai...
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Institute of Electrical and Electronics Engineers Inc.
2025
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| author | Zakaria F.B. Musirin I.B. Aminudin N.B. Johari D.B. Shaaya S.A. Ibrahim N.F.B. |
| author2 | 55646310800 |
| author_facet | 55646310800 Zakaria F.B. Musirin I.B. Aminudin N.B. Johari D.B. Shaaya S.A. Ibrahim N.F.B. |
| author_sort | Zakaria F.B. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This paper presents a comparative analysis of the Distributed Generation (DG) scheme for resilience enhancement. This study models categories of hurricanes as disruptive events, considering data on the fragility of transmission towers concerning wind speeds. The simulation involves generating sustained winds corresponding to different categories of hurricanes, following the Saffir-Simpson Hurricane scale. The transmission power system will encounter power outages when the transmission tower collapses. The installation of DG is one of the suitable efforts to alleviate this phenomenon where it is used as a compensating device to improve power grid resilience. In this study, the Evolutionary Programming (EP) and Artificial Immune System (AIS) optimization techniques are used to determine sizing and strategic locations for the placement of multiple DG units for loss control in the power system. The resilience status of the system is also observed. The proposed optimization techniques are validated on the IEEE 30-Bus Reliability Test System (RTS) under varying loads. Verification was conducted through a comparison of optimization outcomes obtained from EP and AIS techniques. The findings illustrate the effectiveness of these algorithms in significantly reducing total loss and improving the resilience of the tested system. ? 2024 IEEE. |
| format | Conference paper |
| id | my.uniten.dspace-37156 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | my.uniten.dspace-371562025-03-03T15:48:05Z Comparative Analysis in DG Installation Scheme for Resilience Enhancement Zakaria F.B. Musirin I.B. Aminudin N.B. Johari D.B. Shaaya S.A. Ibrahim N.F.B. 55646310800 8620004100 24733969500 24733632200 16022846200 55140240400 Computer programming Ductile fracture Electric power transmission Hurricanes Outages Risk management Uncertainty analysis Artificial Immune System Comparative analyzes Disruptive event Grid hardening Optimization techniques Resilience Simpson Transmission power systems Transmission tower Wind speed Distributed power generation This paper presents a comparative analysis of the Distributed Generation (DG) scheme for resilience enhancement. This study models categories of hurricanes as disruptive events, considering data on the fragility of transmission towers concerning wind speeds. The simulation involves generating sustained winds corresponding to different categories of hurricanes, following the Saffir-Simpson Hurricane scale. The transmission power system will encounter power outages when the transmission tower collapses. The installation of DG is one of the suitable efforts to alleviate this phenomenon where it is used as a compensating device to improve power grid resilience. In this study, the Evolutionary Programming (EP) and Artificial Immune System (AIS) optimization techniques are used to determine sizing and strategic locations for the placement of multiple DG units for loss control in the power system. The resilience status of the system is also observed. The proposed optimization techniques are validated on the IEEE 30-Bus Reliability Test System (RTS) under varying loads. Verification was conducted through a comparison of optimization outcomes obtained from EP and AIS techniques. The findings illustrate the effectiveness of these algorithms in significantly reducing total loss and improving the resilience of the tested system. ? 2024 IEEE. Final 2025-03-03T07:48:04Z 2025-03-03T07:48:04Z 2024 Conference paper 10.1109/ICPEA60617.2024.10498681 2-s2.0-85191752247 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191752247&doi=10.1109%2fICPEA60617.2024.10498681&partnerID=40&md5=421e9c288565960e6a04b85ac82315c1 https://irepository.uniten.edu.my/handle/123456789/37156 311 316 Institute of Electrical and Electronics Engineers Inc. Scopus |
| spellingShingle | Computer programming Ductile fracture Electric power transmission Hurricanes Outages Risk management Uncertainty analysis Artificial Immune System Comparative analyzes Disruptive event Grid hardening Optimization techniques Resilience Simpson Transmission power systems Transmission tower Wind speed Distributed power generation Zakaria F.B. Musirin I.B. Aminudin N.B. Johari D.B. Shaaya S.A. Ibrahim N.F.B. Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title | Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title_full | Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title_fullStr | Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title_full_unstemmed | Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title_short | Comparative Analysis in DG Installation Scheme for Resilience Enhancement |
| title_sort | comparative analysis in dg installation scheme for resilience enhancement |
| topic | Computer programming Ductile fracture Electric power transmission Hurricanes Outages Risk management Uncertainty analysis Artificial Immune System Comparative analyzes Disruptive event Grid hardening Optimization techniques Resilience Simpson Transmission power systems Transmission tower Wind speed Distributed power generation |
| url_provider | http://dspace.uniten.edu.my/ |