Current-Voltage(I-V) Features of Photovoltaic Modules

Jiqi Liu, Alan Curran, Justin S. Fada, Xuan Ma, Wei-Heng Huang, Jennifer L. Braid, Roger H. French


Data Description

The dataset consists of current-voltage(\(I-V\)) features obtained by \(I-V\) feature extraction from \(I-V\) curves of photovoltaic (PV) modules. The features have been extracted using the ddiv package algorithm for brand A PV modules under damp heat indoor accelerated exposures for up to 3000 hours. The I-V curves were measured in a step-wise manner after every 500h of exposure time, and provided by the SunEdison company. The I-V features include max power (\(P_{mp}\)), short circuit current (\(I_{sc}\)), current at max power (\(I_{mp}\)), fill factor (\(FF\)), series resistance (\(R_s\)), shunt resistance (\(R_{sh}\)), open circuit voltage(\(V_{oc}\)), voltage at max power (\(V_{mp}\)). \(R_{sh}\) is too noisy to contain for modeling. After checking the correlation between \(I_{sc}\), \(I_{mp}\), \(V_{oc}\), \(V_{mp}\), \(FF\), \(R_s\), we find that \(FF\), \(R_s\), \(V_{mp}\) are highly correlated, and therefore only choose one of these three variables to be included in the model. Here we choose \(I_{sc}\), \(I_{mp}\), \(R_s\) and \(V_{oc}\) 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 mechanisms of PV degradation. The variable ‘\(dy\)’ is time that has been converted into decimal years so that \(dy\)=1 corresponds to 1 year of exposure time. We use this dataset to build an <S|M|R> network model with time (\(dy\)) as the exogenous stressor variable, four I-V features (\(I_{sc}\), \(I_{mp}\), \(R_s\) and \(V_{oc}\)) as mechanistic endogenous variables and maximum power (\(P_{mp}\)) as the endogenous response variable.

Load data and run code to build netSEM

## Load the acrylic data set

## Run netSEMp1 model
ans1 <- netSEMp1(IVfeature)
## Plot the network model for principle 1
plot(ans1, cutoff = c(0.25, 0.5, 0.8))

## Run netSEMp2 model
ans2 <- netSEMp2(IVfeature)
## Plot the network model for principle 2
plot(ans2, cutoff = c(0.25, 0.5, 0.8))

Network diagram for data

The direct <S|R> path from dy to \(P_{mp}\) has an \(adj. R^2\) of 0.757. Considering the path with one mechanism, the paths contain \(I_{mp}\) or \(R_s\) are likely to be as good as the direct path. The path from \(dy\) to \(R_s\) has an \(adj. R^2\) of 0.761 and the path from \(R_s\) to \(P_{mp}\) has an \(adj. R^2\)of 0.912. The path from dy to \(I_{mp}\) has an \(adj. R^2\) of 0.683 and the path from \(I_{mp}\) to \(P_{mp}\) has an \(adj. R^2\) of 0.829. And we further use the pathwayRMSE function to calculate the root mean squared error(RMSE) of the direct path and the two paths with \(I_{mp}\) or \(R_s\). The RMSE for the direct path is 2.8998, for the path contain \(I_{mp}\) is 3.3007, for the path contain \(R_s\) is 2.9053. So overall, the predicted accuracy of the direct path and the path contain \(R_s\) are very similar and the latter one with \(R_s\) informs us about the active the degradation mechanism.

IVfeature netSEMp1 model

IVfeature netSEMp1 model

IVfeature netSEMp2 model

IVfeature netSEMp2 model


  1. ddiv: Data Driven I-v Feature Extraction, R package,

  2. J. Liu, Alan Curren, Justin S. Fada, Xuan Ma, Wei-Heng Huang, C.Birk Jones, E. Schnabel, M. Kohl, Jennifer L. Braid, and Roger H.French, “Cross-correlation Analysis of the Indoor Accelerated and Real World Exposed Photovoltaic Systems Across Multiple Climate Zones,” in IEEE WCPEC-7 Conference, HI, 2018. DOI:


This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140.