Parametric study of design factors on the performance of flexible solar modules

ABSTRACT

Solar cells have traditionally been built on rigid substrates. However, a lot of research is currently going on to improve the design and performance of solar cells built on flexible substrates. Flexible solar cells are light and rugged; however, the cell’s efficiency is highly affected by factors related to the design or the environment. In many instances, the effect of one factor on the performance of solar modules is affected by the levels of other factors. Therefore, in this work, analysis of variance (ANOVA) tools were used to study the simultaneous effect of design factors of shading, air gap, and curvature on the performance of flexible solar modules. Furthermore, an artificial neural network used the experimental data to predict the performance of the modules under similar conditions. The ANOVA results showed that all the main factors were significant and directly influenced the performance of the solar module. Furthermore, the combined effect of the factors is also significant. Furthermore, the ANN model was able to predict the performance of the modules with high precision, as indicated by the coefficient of determination (R²) value of 99.92%.

KEYWORDS:

• Flexible solar modules
• outdoor testing
• design of experiments
• artificial neural networks (ANN)
• solar modules performance

Nomenclature

ANN	=	Artificial neural network
DOE	=	Design of experiment
R2	=	Coefficient of determination
R2(adj)	=	Adjusted coefficient of determination
FF	=	Fill factor
Vmax	=	Maximum voltage
Imax	=	Maximum current
VOC	=	Open circuit voltage
ISC	=	Short circuit current
Pin	=	Input power
η	=	Efficiency
DF	=	Degree of freedom
Adj SS	=	Adjusted sum of squares
Adj MS	=	Adjusted mean square
F- value	=	A significance statistic
P-value	=	A significance level

Disclosure statement

No potential conflict of interest was reported by the author(s).