A linear method to extract diode model parameters of solar panels from a single I–V curve. (April 2015)
- Record Type:
- Journal Article
- Title:
- A linear method to extract diode model parameters of solar panels from a single I–V curve. (April 2015)
- Main Title:
- A linear method to extract diode model parameters of solar panels from a single I–V curve
- Authors:
- Lim, Li Hong Idris
Ye, Zhen
Ye, Jiaying
Yang, Dazhi
Du, Hui - Abstract:
- Abstract: The I – V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I – V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I – V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed. Highlights: A linear method of extracting the internal diode model parameters from the I – V characteristic curve for solar cells. The reverse derivation of diode model parameters is a big challenge due to the nonlinear relationship between the parameters. The integral-based linear least square identification method is proposed to extract all parameters simultaneously from a I – V curve. No iterative searching or approximation is required in theAbstract: The I – V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I – V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I – V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed. Highlights: A linear method of extracting the internal diode model parameters from the I – V characteristic curve for solar cells. The reverse derivation of diode model parameters is a big challenge due to the nonlinear relationship between the parameters. The integral-based linear least square identification method is proposed to extract all parameters simultaneously from a I – V curve. No iterative searching or approximation is required in the proposed method. Real life examples are provided to illustrate the accuracy and effectiveness of the proposed method. … (more)
- Is Part Of:
- Renewable energy. Volume 76(2015)
- Journal:
- Renewable energy
- Issue:
- Volume 76(2015)
- Issue Display:
- Volume 76, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 76
- Issue:
- 2015
- Issue Sort Value:
- 2015-0076-2015-0000
- Page Start:
- 135
- Page End:
- 142
- Publication Date:
- 2015-04
- Subjects:
- Diode model -- I–V curve -- Linear least square -- Binary search algorithm
Renewable energy sources -- Periodicals
Power resources -- Periodicals
Énergies renouvelables -- Périodiques
Ressources énergétiques -- Périodiques
333.794 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09601481 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/renewable-energy/ ↗ - DOI:
- 10.1016/j.renene.2014.11.018 ↗
- Languages:
- English
- ISSNs:
- 0960-1481
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7364.187000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 7798.xml