knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 9, fig.height = 5 )
SunsVoc
package do?With the SunsVoc
package, Isc-Voc curves can be constructed with outdoor time-series I-V curves of photovoltaic (PV) modules instead of having to be measured in the lab. Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the diode of photovoltaic cells without the effect of series resistance. Time series of four different power loss modes; namely, uniform current, recombination, series resistance, and current mismatch, can be calculated based on obtained Isc-Voc curves. The details for the analysis method can be found in Wang et al. 2020.
library(SunsVoc)
The imported data should have the following columns: a string containing the IV curve for each timestamp, extracted features from the ddiv
package's IVFeatures
function run on individual IV curves, Voc and Isc readings from any source (if available), Plane of Array (POA) irradiance and module temperature (in Celsius) readings, and a timestamp. If you have Global Horizontal Irradiance (GHI) instead of POA, convert it first to POA. This can be done using the Python package PVLib, using the latitude, longitude, and elevation parameters of the installation site.
Use the following variable names in order to ensure the package processes your data correctly:
Deprecated Name Column name Unit/Standard Format (if applicable) ---------------------- -------------------------- --------------- ------------------------- Time stamp tmst Local yyyy-mm-dd hh:mm:ss Module temperature modt $^\circ$C -- Plane of array irradiance poa W/m^2^ -- Open circuit voltage voc V -- Short circuit current isc A -- Voltage at max. power vmp V -- Current at max. power imp A -- Max. power point pmp W -- Series resistance rs $\Omega$ --
These are the key variable names. Look at the example data below to see other variable names. The extracted features obtained with ddiv
should have the correct names when they are extracted.
Note that the timestamp in each column must match the IV curve with it.
# below is the function used to read in our csv # df_wbw <- read_df_raw_from_csv('../../data/sa42589-albsf_full_ddiv.csv', 0, 7) colnames(df_wbw)
ddiv
PackageUse the ddiv
package's IVfeatures
function to extract features from individual IV curves. See package documentation for ddiv
on how to use this function (note that ddiv
requires the segmented
package, among others). Note that ddiv
returns a list, so it's a good idea to pass its results to a dataframe.
# Load required packages library(dplyr) library(purrr) library(magrittr) library(ddiv) # I-V curve feature extraction using the IVfeature function str_ddiv <- function(iv_str, pb) { iv <- char_to_df(iv_str) # initialize result for when IVfeature encounters an error err_df <- data.frame("Isc" = NA, "Rsh" = NA, "Voc" = NA, "Rs" = NA, "Pmp" = NA,"Imp" = NA, "Vmp" = NA, "FF" = NA) # calculate ddiv with error trapping res <- tryCatch( as.data.frame(IVfeature(I = iv$I, V = iv$V, num = 200, crt = 0.1, crtvalb = 0.2)), error = function(e) err_df ) pb$tick()$print() return(res) } # Batch processing of I-V curves for feature extraction batch_ddiv <- function(df) { iv_list <- df$ivdf pb <- dplyr::progress_estimated(length(iv_list)) ddiv_df <- iv_list %>% map_dfr(~str_ddiv(iv_str = ., pb)) res <- cbind(df$tmst, ddiv_df) return(res) } # It is recommended to test on a small number of I-V curves # to tweak the input parameters of the IVfeature function test <- sample_n(df_wbw, 5) ddiv_test <- batch_ddiv(test) colnames(ddiv_test) <- c("tmst", "isc", "rsh", "voc", "rs", "pmp", "imp", "vmp", "ff" ) # Bind the ddiv result with the original dataframe test_res <- full_join(test[,c(1,3:4)], ddiv_test[,c(1:2,4:8)]) # See the column names of the extracted features colnames(test_res)
It is helpful to know the median temperature of the studied PV module prior to the analysis. The df_wbw
is the example dataset in the package. A method for median temperature determination is included as follows:
T_corr <- median_temp(df_wbw) T_corr
It is recommended that the analysis period be chosen so that each psuedo-IV curve has at least 300 points. In order to achieve this, make sure that there are at least 300 Isc and Voc pairs in each period. 10-minute intervals of collection is generally good for 7 days.
Lastly, the power_loss_bat
function, which deals with power loss modes, requires a subset of the larger dataframe. Use the select_init_df
function to do this. The input dataframe for both select_init_df
and IVXbX
functions would be better to be filtered according to the current accuracy of the tracing equipment and have the rows with NAs in the modt
and pmp
variables removed.
# Read the raw data with tracer accuracy for filtering and time period for pseudo I-V curves df <- read_df_raw(df_wbw, tracer_accuracy = 0.02, t_period = 7) # Subset the data for the first 21 days df_init <- select_init_df(df, days = 21) # See the column names of the initial dataframe colnames(df_init)
Psuedo-IV Curve generation is handled by a master function called IVXbyX
which calls a variety of subfunctions.
# Psuedo-IV curve generation df_full <- IVXbyX(df, corr_temp = T_corr, N_c= 60) # See the column names of the generated data colnames(df_full)
The power_loss_phys_bat
function is finally used to calculate the power loss modes: losses due to uniform current, recombination, series resistance, and current mismatch.
# Power loss mode calculation res <- power_loss_phys_bat(df_full, df_init, corr_T = T_corr, N_c = 60) # Generate an example table for power loss modes knitr::kable(res[1:5, ], caption = "Power Loss Modes")
Visualization of the result can be done using the ggplot2
package.
library(ggplot2) ggplot(data = res, aes(x = date)) + geom_point(aes(y = uni_I, color = "Uniform current", shape = "Uniform current"), size = 2) + geom_point(aes(y = rec, color = "Recombination", shape = "Recombination"), size = 2) + geom_point(aes(y = rs, color = "Rs loss", shape = "Rs loss"), size = 2) + geom_point(aes(y = I_mis, color = "I mismatch", shape = "I mismatch"), size = 2) + ylab(expression(paste(Delta, "Power (W)"))) + xlab("Date") + theme_bw() + theme(axis.text = element_text(size = 12), axis.title = element_text(size = 12), legend.text = element_text(size = 10), legend.position = "top") + scale_shape_manual(values = c(4, 8, 17, 16), name = "Power loss \n mode") + scale_colour_discrete(name = "Power loss \n mode")
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