Pipeline

library(SunsVoc)

# 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)

T_corr <- median_temp(df_wbw)

T_corr
#> [1] 30

# 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)
#>  [1] "tmst"     "ivdf"     "modt"     "poa"      "isc"      "voc"     
#>  [7] "rs"       "pmp"      "imp"      "vmp"      "date"     "day"     
#> [13] "n_period"

# Psuedo-IV curve generation 
df_full <- IVXbyX(df, corr_temp = T_corr, N_c= 60)
#> Warning: `progress_estimated()` was deprecated in dplyr 1.0.0.
#> ℹ The deprecated feature was likely used in the SunsVoc package.
#>   Please report the issue to the authors.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.

# See the column names of the generated data
colnames(df_full)
#>  [1] "date"      "day"       "n_period"  "piv"       "pisc"      "pvoc"     
#>  [7] "prsh"      "prss"      "ppmp"      "pimp"      "pvmp"      "pfff"     
#> [13] "isc_1sun"  "voc_1sun"  "n"         "imp_fit"   "vmp_fit"   "pmp_fit"  
#> [19] "rs_fit"    "voc_adjR2" "voc_rmse"  "vmp_adjR2" "vmp_rmse"  "rs_adjR2" 
#> [25] "rs_rmse"

# 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")

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")