.png){kind=icon}
Return
Neural network based solar cell model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enconman.2005.07.007.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a neural network based approach for improving the accuracy of the electrical equivalent circuit of a photovoltaic module. The equivalent circuit parameters of a PV module mainly depend on solar irradiation and temperature. The dependence on environmental factors of the circuit parameters is investigated by using a set of current-voltage curves. It is shown that the relationship between them is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the neural network is utilized to overcome these difficulties. The neural network is trained once by using some measured current-voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly without solving any nonlinear implicit equations that is necessary in conventional methods. To verify the proposed model, an experimental set up is installed. The comparison between the measured values and the proposed model results shows higher accuracy than the conventional model for all operating conditions. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
photovoltaic module
equivalent circuit parameter
artificial neural network
I-V characteristics
modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.9
Papers:
2.0W
Citations:
11.3W
Organization
No organization information available
Cited Papers
.png){kind=icon}
Principled Entrepreneurship And Shared Leadership: The Case Of TEOCO (The Employee Owned Company)
OAAI
OAAIShareSaveShareSaveShareSaveShareSaveShareSave
View more