Risk assessment of electric power generation systems using modified jellyfish search algorithm
An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jelly...
- Autores:
-
Chittari, Archana
Y.V. Sivareddy
V. Sankar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/13537
- Acceso en línea:
- https://doi.org/10.32397/tesea.vol5.n2.595
- Palabra clave:
- Jellyfish search algorithm
LOLP
LOLE
EDNS
- Rights
- openAccess
- License
- Archana Chittari, Y.V. Sivareddy, V. Sankar - 2024
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| dc.title.spa.fl_str_mv |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| dc.title.translated.spa.fl_str_mv |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| title |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| spellingShingle |
Risk assessment of electric power generation systems using modified jellyfish search algorithm Jellyfish search algorithm LOLP LOLE EDNS |
| title_short |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| title_full |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| title_fullStr |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| title_full_unstemmed |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| title_sort |
Risk assessment of electric power generation systems using modified jellyfish search algorithm |
| dc.creator.fl_str_mv |
Chittari, Archana Y.V. Sivareddy V. Sankar |
| dc.contributor.author.eng.fl_str_mv |
Chittari, Archana Y.V. Sivareddy V. Sankar |
| dc.subject.eng.fl_str_mv |
Jellyfish search algorithm LOLP LOLE EDNS |
| topic |
Jellyfish search algorithm LOLP LOLE EDNS |
| description |
An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reliability indices. Jelly Fish Search Algorithm has been used in power systems to find the economic load dispatch of generating units, for integration of Distributed Generation (DG) units, Maximum Power tracking of PV system and Optimal Power Flow solutions etc. However, JFSA has not been implemented for the evaluation of reliability indices for electric power generation system. In this context a modified JFSA algorithm is developed for evaluation of certain reliability indices such as Loss of Load Expectation (LOLE), and Expected Demand Not Supplied (EDNS), Loss of Load Probability (LOLP). The algorithm presented is implemented on two test system which are RBTS 6 bus system and IEEE 24 bus Reliability Test System. The Results obtained are compared for different models of Generation and Load and are analysed. |
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2024 |
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2024-12-24 00:00:00 |
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2024-12-24 00:00:00 |
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2024-12-24 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Journal article |
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Text |
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https://doi.org/10.32397/tesea.vol5.n2.595 |
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10.32397/tesea.vol5.n2.595 |
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2745-0120 |
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https://doi.org/10.32397/tesea.vol5.n2.595 |
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eng |
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eng |
| dc.relation.references.eng.fl_str_mv |
MD Singh, OP Roy, and Rajesh Ram. Reliability enhancement of power system using risk index estimation technique. International Journal of Innovations in Engineering and Technology (IJIET), 2(1), 2013. [2] Bie Zhaohong and Wang Xifan. Studies on variance reduction technique of monte carlo simulation in composite system reliability evaluation. Electric Power Systems Research, 63(1):59–64, August 2002. [3] R. Ashok Bakkiyaraj and N. Kumarappan. Optimal reliability planning for a composite electric power system based on monte carlo simulation using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 47:109–116, May 2013. [4] Tulasi RamaKrishna Rao Ballireddy and Pawan Kumar Modi. Reliability evaluation of power system incorporating wind farm for generation expansion planning based on anlsa approach. Wind Energy, 22(7):975–991, April 2019. [5] Mohamed Jawad a. Husain Saleh, Sayed Ahmed Abbas Hasan Abdulla, Ali Maitham a. Aziz Altaweel, and Isa S. Qamber. Lolp and lole calculation for smart cities power plants. September 2019. [6] Arun Rathore and N.P. Patidar. Reliability constrained socio-economic analysis of renewable generation based standalone hybrid power system with storage for off-grid communities. IET Renewable Power Generation, 14(12):2142–2153, July 2020. [7] Hongtao Shan, Yuanyuan Sun, Wenjun Zhang, Aleksey Kudreyko, and Lijia Ren. Reliability analysis of power distribution network based on pso-dbn. IEEE Access, 8:224884–224894, January 2020. [8] Bin Bai, Junyi Zhang, Xuan Wu, Guang Wei Zhu, and Xinye Li. Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems. Expert Systems With Applications, 177:114952, September 2021. [9] Jui-Sheng Chou and Asmare Molla. Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems. Scientific Reports, 12(1), November 2022. [10] Afroz Alam, Preeti Verma, Mohd Tariq, Adil Sarwar, Basem Alamri, Noore Zahra, and Shabana Urooj. Jellyfish search optimization algorithm for mpp tracking of pv system. Sustainability, 13(21):11736, October 2021. [11] Omar Kahouli, Haitham Alsaif, Yassine Bouteraa, Naim Ben Ali, and Mohamed Chaabene. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Applied Sciences, 11(7):3092, March 2021. [12] Mohamed Farhat, Salah Kamel, Ahmed M. Atallah, and Baseem Khan. Optimal power flow solution based on jellyfish search optimization considering uncertainty of renewable energy sources. IEEE Access, 9:100911–100933, January 2021. [13] Yusuf Yare and Ganesh K Venayagamoorthy. Optimal scheduling of generator maintenance using modified discrete particle swarm optimization. In 2007 iREP Symposium-Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability, pages 1–8. IEEE, 2007. [14] Narayanan Kumarappan and Kaliyamoorthy Suresh. Particle swarm optimization based maintenance scheduling using levelized risk method and evaluation of system well being index. July 2010. [15] Probability Subcommittee. Ieee reliability test system. IEEE Transactions on Power Apparatus and Systems, PAS-98(6):2047–2054, November 1979. [16] R. Billinton, S. Kumar, N. Chowdhury, K. Chu, L. Goel, E. Khan, P. Kos, G. Nourbakhsh, and J. Oteng-Adjei. A reliability test system for educational purposes-basic results. IEEE Transactions on Power Systems, 5(1):319–325, January 1990. [17] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton, Q. Chen, C. Fong, S. Haddad, S. Kuruganty, W. Li, R. Mukerji, D. Patton, N. Rau, D. Reppen, A. Schneider, M. Shahidehpour, and C. Singh. The ieee reliability test system-1996. a report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Transactions on Power Systems, 14(3):1010–1020, January 1999. [18] Roy Billinton and Ronald N. Allan. Reliability Evaluation of Engineering Systems. January 1992. [19] R Billinton and RN Allan. Reliability evaluation of power systems,(plenum). New York, US, 1996. [20] Li Wenyuan et al. Risk assessment of power systems: models, methods, and applications. In IEEE Press Series on Power Engineering. A Jhon Wiley Sons, Inc, 2005. [21] V Sankar. System reliability concepts. Himalaya Publishing House, 2015. |
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Transactions on Energy Systems and Engineering Applications |
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5 |
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1 |
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https://revistas.utb.edu.co/tesea/article/download/595/403 |
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Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications |
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2 |
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Archana Chittari, Y.V. Sivareddy, V. Sankar - 2024 |
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info:eu-repo/semantics/openAccess |
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Universidad Tecnológica de Bolívar |
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Chittari, ArchanaY.V. SivareddyV. Sankar2024-12-24 00:00:002024-12-24 00:00:002024-12-24An electric utility's main goal is to fulfil the requirements and expectations of its customers by providing power. When there are uncertainties, like equipment failures, system reliability evaluation offers a framework to guarantee that the system will still function properly. A modified Jellyfish Search Algorithm (JFSA) has been proposed for estimation of Electric power generation system reliability indices. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and other modified versions of algorithms have been used in algorithms that use optimization methods for the assessment of reliability indices. Jelly Fish Search Algorithm has been used in power systems to find the economic load dispatch of generating units, for integration of Distributed Generation (DG) units, Maximum Power tracking of PV system and Optimal Power Flow solutions etc. However, JFSA has not been implemented for the evaluation of reliability indices for electric power generation system. In this context a modified JFSA algorithm is developed for evaluation of certain reliability indices such as Loss of Load Expectation (LOLE), and Expected Demand Not Supplied (EDNS), Loss of Load Probability (LOLP). The algorithm presented is implemented on two test system which are RBTS 6 bus system and IEEE 24 bus Reliability Test System. The Results obtained are compared for different models of Generation and Load and are analysed.application/pdfengUniversidad Tecnológica de BolívarArchana Chittari, Y.V. Sivareddy, V. Sankar - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/595Jellyfish search algorithmLOLPLOLEEDNSRisk assessment of electric power generation systems using modified jellyfish search algorithmRisk assessment of electric power generation systems using modified jellyfish search algorithmArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol5.n2.59510.32397/tesea.vol5.n2.5952745-0120MD Singh, OP Roy, and Rajesh Ram. Reliability enhancement of power system using risk index estimation technique. International Journal of Innovations in Engineering and Technology (IJIET), 2(1), 2013. [2] Bie Zhaohong and Wang Xifan. Studies on variance reduction technique of monte carlo simulation in composite system reliability evaluation. Electric Power Systems Research, 63(1):59–64, August 2002. [3] R. Ashok Bakkiyaraj and N. Kumarappan. Optimal reliability planning for a composite electric power system based on monte carlo simulation using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 47:109–116, May 2013. [4] Tulasi RamaKrishna Rao Ballireddy and Pawan Kumar Modi. Reliability evaluation of power system incorporating wind farm for generation expansion planning based on anlsa approach. Wind Energy, 22(7):975–991, April 2019. [5] Mohamed Jawad a. Husain Saleh, Sayed Ahmed Abbas Hasan Abdulla, Ali Maitham a. Aziz Altaweel, and Isa S. Qamber. Lolp and lole calculation for smart cities power plants. September 2019. [6] Arun Rathore and N.P. Patidar. Reliability constrained socio-economic analysis of renewable generation based standalone hybrid power system with storage for off-grid communities. IET Renewable Power Generation, 14(12):2142–2153, July 2020. [7] Hongtao Shan, Yuanyuan Sun, Wenjun Zhang, Aleksey Kudreyko, and Lijia Ren. Reliability analysis of power distribution network based on pso-dbn. IEEE Access, 8:224884–224894, January 2020. [8] Bin Bai, Junyi Zhang, Xuan Wu, Guang Wei Zhu, and Xinye Li. Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems. Expert Systems With Applications, 177:114952, September 2021. [9] Jui-Sheng Chou and Asmare Molla. Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems. Scientific Reports, 12(1), November 2022. [10] Afroz Alam, Preeti Verma, Mohd Tariq, Adil Sarwar, Basem Alamri, Noore Zahra, and Shabana Urooj. Jellyfish search optimization algorithm for mpp tracking of pv system. Sustainability, 13(21):11736, October 2021. [11] Omar Kahouli, Haitham Alsaif, Yassine Bouteraa, Naim Ben Ali, and Mohamed Chaabene. Power system reconfiguration in distribution network for improving reliability using genetic algorithm and particle swarm optimization. Applied Sciences, 11(7):3092, March 2021. [12] Mohamed Farhat, Salah Kamel, Ahmed M. Atallah, and Baseem Khan. Optimal power flow solution based on jellyfish search optimization considering uncertainty of renewable energy sources. IEEE Access, 9:100911–100933, January 2021. [13] Yusuf Yare and Ganesh K Venayagamoorthy. Optimal scheduling of generator maintenance using modified discrete particle swarm optimization. In 2007 iREP Symposium-Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability, pages 1–8. IEEE, 2007. [14] Narayanan Kumarappan and Kaliyamoorthy Suresh. Particle swarm optimization based maintenance scheduling using levelized risk method and evaluation of system well being index. July 2010. [15] Probability Subcommittee. Ieee reliability test system. IEEE Transactions on Power Apparatus and Systems, PAS-98(6):2047–2054, November 1979. [16] R. Billinton, S. Kumar, N. Chowdhury, K. Chu, L. Goel, E. Khan, P. Kos, G. Nourbakhsh, and J. Oteng-Adjei. A reliability test system for educational purposes-basic results. IEEE Transactions on Power Systems, 5(1):319–325, January 1990. [17] C. Grigg, P. Wong, P. Albrecht, R. Allan, M. Bhavaraju, R. Billinton, Q. Chen, C. Fong, S. Haddad, S. Kuruganty, W. Li, R. Mukerji, D. Patton, N. Rau, D. Reppen, A. Schneider, M. Shahidehpour, and C. Singh. The ieee reliability test system-1996. a report prepared by the reliability test system task force of the application of probability methods subcommittee. IEEE Transactions on Power Systems, 14(3):1010–1020, January 1999. [18] Roy Billinton and Ronald N. Allan. Reliability Evaluation of Engineering Systems. January 1992. [19] R Billinton and RN Allan. Reliability evaluation of power systems,(plenum). New York, US, 1996. [20] Li Wenyuan et al. Risk assessment of power systems: models, methods, and applications. In IEEE Press Series on Power Engineering. A Jhon Wiley Sons, Inc, 2005. [21] V Sankar. System reliability concepts. Himalaya Publishing House, 2015.Transactions on Energy Systems and Engineering Applications5114https://revistas.utb.edu.co/tesea/article/download/595/403Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications220.500.12585/13537oai:repositorio.utb.edu.co:20.500.12585/135372025-09-16 09:15:13.527https://creativecommons.org/licenses/by/4.0Archana Chittari, Y.V. Sivareddy, V. Sankar - 2024metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com |