The dataset is current-voltage(I-V) features data obtained by I-V features extracted algorithm for the brand A modules under damp heat indoor accelerated test which is up to 3000 hours. The measurement were taken every 500h. The raw data is provided by SunEdison company. The I-V features include max power(Pmp), short circuit current(Isc), current at max power(Imp), fill factor(FF), series resistance(Rs), shunt resistance(Rsh), open circuit voltage(Voc), voltage at max power(Vmp). Rsh is too noisy to contain for modeling. After checking the correlation between Isc, Imp, Voc, Vmp, FF, Rs. FF, Rs, Vmp are highly correlated, so we randomly select one to be contained in the model. Here we choose Isc, Imp, Rs and Voc to be contained in the model and these four I-V features show no indication of high correlation. The trend of the I-V features are related with the mechanims of PV degradation. The variable 'dy' is time that has been converted into decimal year in which 1 means 1 year. We would use this dataet to buit <S|M|R> model with time as stressor, four I-V features as mechanisms and max power as reponse.
A data frame with 20 rows and 9 variables:
max power at the measurement step
the exposure time after been converted into decimal year
short circuit current
current at max power
open circuit voltage
Jiqi Liu, Alan Curran, Justin S. Fada, Xuan Ma, Wei-Heng Huang, Jennifer L. Braid, Roger H. French
Solar Durability and Lifetime Extension (SDLE) Research Center, Case Western Reserve University
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