An artificial intelligent approach for the optimization of organic rankine cycle power generation systems
The study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of...
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Institute of Advanced Engineering and Science
2023
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| _version_ | 1833413078924918784 |
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| author | Tan J.D. Lim C.W. Koh S.P. Tiong S.K. Koay Y.Y. |
| author2 | 38863172300 |
| author_facet | 38863172300 Tan J.D. Lim C.W. Koh S.P. Tiong S.K. Koay Y.Y. |
| author_sort | Tan J.D. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of an ORC to a higher rate. In this research, a Self-Adjusted Peak Search algorithm (SAPS) is proposed as the MPPT scheme of an ORC system. The SAPS has the ability to perform a relatively detailed search when the convergence reaches the near-optima peak without jeopardizing the speed of the overall convergence process. The SAPS is tested in a simulation to track for a moving maximum power pint (MPP) of an ORC system. Experiment results show that the SAPS outperformed several other well-established optimization algorithm in tracking the moving MPP, especially in term of the solution accuracies. It can thus be concluded that the proposed SAPS performs well as a mean of an MPPT scheme in an ORC system. � 2019 Institute of Advanced Engineering and Science. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-24732 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | my.uniten.dspace-247322023-05-29T15:26:23Z An artificial intelligent approach for the optimization of organic rankine cycle power generation systems Tan J.D. Lim C.W. Koh S.P. Tiong S.K. Koay Y.Y. 38863172300 35722335000 22951210700 15128307800 57189626122 The study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of an ORC to a higher rate. In this research, a Self-Adjusted Peak Search algorithm (SAPS) is proposed as the MPPT scheme of an ORC system. The SAPS has the ability to perform a relatively detailed search when the convergence reaches the near-optima peak without jeopardizing the speed of the overall convergence process. The SAPS is tested in a simulation to track for a moving maximum power pint (MPP) of an ORC system. Experiment results show that the SAPS outperformed several other well-established optimization algorithm in tracking the moving MPP, especially in term of the solution accuracies. It can thus be concluded that the proposed SAPS performs well as a mean of an MPPT scheme in an ORC system. � 2019 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T07:26:22Z 2023-05-29T07:26:22Z 2019 Article 10.11591/ijeecs.v14.i1.pp340-345 2-s2.0-85061148782 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061148782&doi=10.11591%2fijeecs.v14.i1.pp340-345&partnerID=40&md5=0dea1f309e7bf975d8736e4eed8d8503 https://irepository.uniten.edu.my/handle/123456789/24732 14 1 340 345 All Open Access, Hybrid Gold Institute of Advanced Engineering and Science Scopus |
| spellingShingle | Tan J.D. Lim C.W. Koh S.P. Tiong S.K. Koay Y.Y. An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title | An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title_full | An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title_fullStr | An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title_full_unstemmed | An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title_short | An artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| title_sort | artificial intelligent approach for the optimization of organic rankine cycle power generation systems |
| url_provider | http://dspace.uniten.edu.my/ |