Optimization of photovoltaic energy harvesting using clonal selection algorithm
The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address...
| Main Authors: | , , , , , |
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| Format: | Proceedings |
| Language: | English English |
| Published: |
IEEE
2023
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/41762/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/41762/2/FULL%20TEXT.pdf |
| _version_ | 1825715920412082176 |
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| author | Tan, Min Keng Grace Butiza Joponi Lim Ahmad Razani Haron Chai, Chang-Yii Tze, Kenneth Kin Teo |
| author_facet | Tan, Min Keng Grace Butiza Joponi Lim Ahmad Razani Haron Chai, Chang-Yii Tze, Kenneth Kin Teo |
| author_sort | Tan, Min Keng |
| collection | UMS |
| description | The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address this issue, the Clonal Selection Algorithm (CSA) is proposed as an effective approach to enhance MPPT algorithm performance. The CSA dynamically adjusts the voltage perturbation size based on instant ambient irradiance and temperature, leading to improved global maximum power point tracking and enhanced efficiency in PV systems. Experimental results demonstrate the superiority of the proposed CSA over conventional MPPT algorithms, especially in scenarios with varying solar irradiance. The CSA's adaptability allows PV systems to operate closer to their optimal efficiency, maximizing energy harvest from available solar resources. Overall, this research contributes valuable insights into sustainable and efficient energy solutions by leveraging the capabilities of the CSA. Successfully integrating the CSA in PV systems plays a critical role in establishing an eco-friendly and resilient renewable energy infrastructure, for a greener future. |
| first_indexed | 2024-12-09T00:53:33Z |
| format | Proceedings |
| id | ums.eprints-41762 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-12-09T00:53:33Z |
| publishDate | 2023 |
| publisher | IEEE |
| record_format | dspace |
| spelling | ums.eprints-417622024-11-08T02:54:44Z https://eprints.ums.edu.my/id/eprint/41762/ Optimization of photovoltaic energy harvesting using clonal selection algorithm Tan, Min Keng Grace Butiza Joponi Lim Ahmad Razani Haron Chai, Chang-Yii Tze, Kenneth Kin Teo T1-995 Technology (General) TK1-9971 Electrical engineering. Electronics. Nuclear engineering The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address this issue, the Clonal Selection Algorithm (CSA) is proposed as an effective approach to enhance MPPT algorithm performance. The CSA dynamically adjusts the voltage perturbation size based on instant ambient irradiance and temperature, leading to improved global maximum power point tracking and enhanced efficiency in PV systems. Experimental results demonstrate the superiority of the proposed CSA over conventional MPPT algorithms, especially in scenarios with varying solar irradiance. The CSA's adaptability allows PV systems to operate closer to their optimal efficiency, maximizing energy harvest from available solar resources. Overall, this research contributes valuable insights into sustainable and efficient energy solutions by leveraging the capabilities of the CSA. Successfully integrating the CSA in PV systems plays a critical role in establishing an eco-friendly and resilient renewable energy infrastructure, for a greener future. IEEE 2023 Proceedings NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41762/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41762/2/FULL%20TEXT.pdf Tan, Min Keng and Grace Butiza Joponi and Lim and Ahmad Razani Haron and Chai, Chang-Yii and Tze, Kenneth Kin Teo (2023) Optimization of photovoltaic energy harvesting using clonal selection algorithm. https://ieeexplore.ieee.org/abstract/document/10291536 |
| spellingShingle | T1-995 Technology (General) TK1-9971 Electrical engineering. Electronics. Nuclear engineering Tan, Min Keng Grace Butiza Joponi Lim Ahmad Razani Haron Chai, Chang-Yii Tze, Kenneth Kin Teo Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title | Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title_full | Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title_fullStr | Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title_full_unstemmed | Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title_short | Optimization of photovoltaic energy harvesting using clonal selection algorithm |
| title_sort | optimization of photovoltaic energy harvesting using clonal selection algorithm |
| topic | T1-995 Technology (General) TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | https://eprints.ums.edu.my/id/eprint/41762/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/41762/2/FULL%20TEXT.pdf |
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