Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks
ABSTRACTThe predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2332114 |
| _version_ | 1797253712874307584 |
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| author | Fen Xu Jian Wang Minghuan Guo Zhifeng Wang |
| author_facet | Fen Xu Jian Wang Minghuan Guo Zhifeng Wang |
| author_sort | Fen Xu |
| collection | DOAJ |
| description | ABSTRACTThe predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and measuring the lunar concentration ratio distribution become a promising option. However, many factors affect the flux distribution of a heliostat field. To obtain an accurate predictive model for the solar flux distribution, we propose a deep-learning method using conditional generative adversarial networks (cGAN) and lunar concentration images. The method can take account of tracking errors of individual heliostats, defects of reflecting surfaces, as well as atmospheric attenuation effects, and has the potential to give a reliable prediction of solar flux distribution. Mathematical relations between the solar flux distribution and the solar concentration ratio distribution are discussed in the paper. Experiments have been designed and carried out with an ordinary heliostat at the Beijing Badaling solar concentrating power station. Experimental results show that the AI-generated solar concentration ratio distributions are very close to the actual solar concentration ratio distributions, demonstrating the feasibility of AI models for the prediction of solar flux distribution. |
| first_indexed | 2024-04-24T21:38:26Z |
| format | Article |
| id | doaj.art-84c93f02c3fc4ee0bad07fb5b4976d8c |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-04-24T21:38:26Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-84c93f02c3fc4ee0bad07fb5b4976d8c2024-03-21T12:33:50ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2332114Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial NetworksFen Xu0Jian Wang1Minghuan Guo2Zhifeng Wang3School of Electrical & Control Engineering, North-China University of Technology, Beijing, ChinaSchool of Electrical & Control Engineering, North-China University of Technology, Beijing, ChinaInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, ChinaInstitute of Electrical Engineering, Chinese Academy of Sciences, Beijing, ChinaABSTRACTThe predictive analysis of solar flux distribution on the receiver surface is critical in optimizing the concentration processes of concentrating solar power (CSP) plants. Due to the difficulties of directly measuring the solar flux distribution of the heliostat field, tracking the Moon and measuring the lunar concentration ratio distribution become a promising option. However, many factors affect the flux distribution of a heliostat field. To obtain an accurate predictive model for the solar flux distribution, we propose a deep-learning method using conditional generative adversarial networks (cGAN) and lunar concentration images. The method can take account of tracking errors of individual heliostats, defects of reflecting surfaces, as well as atmospheric attenuation effects, and has the potential to give a reliable prediction of solar flux distribution. Mathematical relations between the solar flux distribution and the solar concentration ratio distribution are discussed in the paper. Experiments have been designed and carried out with an ordinary heliostat at the Beijing Badaling solar concentrating power station. Experimental results show that the AI-generated solar concentration ratio distributions are very close to the actual solar concentration ratio distributions, demonstrating the feasibility of AI models for the prediction of solar flux distribution.https://www.tandfonline.com/doi/10.1080/08839514.2024.2332114 |
| spellingShingle | Fen Xu Jian Wang Minghuan Guo Zhifeng Wang Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks Applied Artificial Intelligence |
| title | Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks |
| title_full | Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks |
| title_fullStr | Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks |
| title_full_unstemmed | Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks |
| title_short | Prediction of Solar Concentration Flux Distribution for a Heliostat Based on Lunar Concentration Image and Generative Adversarial Networks |
| title_sort | prediction of solar concentration flux distribution for a heliostat based on lunar concentration image and generative adversarial networks |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2332114 |
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