A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. (May 2021)
- Record Type:
- Journal Article
- Title:
- A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. (May 2021)
- Main Title:
- A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models
- Authors:
- Li, Shuijia
Gong, Wenyin
Gu, Qiong - Abstract:
- Abstract: Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells. Highlights:Abstract: Photovoltaic (PV) cells are widely used for their clean and sustainable advantages, forcing researchers to accurately model their characteristics. The behavior of PV cells can be derived from their current–voltage characteristics, depending on their unknown circuit model parameters. Due to the simulation, evaluation, control, and optimization of PV systems, it is essential to accurately and reliably extract the parameters of PV models. However, because of the non-linear, multi-variable, and multi-modal characteristics, it is still a very challenging task. With the rapid development of intelligent computing, various meta-heuristic algorithms have been devoted to extracting the parameters of different PV models. The purpose of this paper is to comprehensively review the meta-heuristic algorithms and their related variants that have been used to extract the parameters of different PV models. Different from the existing research works, this paper presents a comprehensive review based on the reliability, robustness, computational resources, and time complexity of the algorithm. These features are essential to design an algorithm for efficient parameter extraction of PV models. Based on the conducted review, some useful recommendations are provided, which have important reference significance when designing the new parameter extraction methods of PV models and are of great significance for further improving the performance, control, and design of PV cells. Highlights: Some common PV models and their parameter optimization problems are described. Various meta-heuristic methods for parameter extraction of PV models are reviewed. Important insights and future research directions are summarized. … (more)
- Is Part Of:
- Renewable & sustainable energy reviews. Volume 141(2021)
- Journal:
- Renewable & sustainable energy reviews
- Issue:
- Volume 141(2021)
- Issue Display:
- Volume 141, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 141
- Issue:
- 2021
- Issue Sort Value:
- 2021-0141-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-05
- Subjects:
- Parameter extraction -- Photovoltaic models -- Meta-heuristic algorithms -- Optimization methods
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/13640321 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-and-sustainable-energy-reviews ↗ - DOI:
- 10.1016/j.rser.2021.110828 ↗
- Languages:
- English
- ISSNs:
- 1364-0321
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.186000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 22889.xml