Voltage visualization new (#31)

* Voltage_Visualization_New A new branch is created incorporating changes in "Voltage_Visualization" ensuring compatible with data of any duration. * Changes_in_Vmin<180V_condition is made * return na instead of inf when v data missing * Add category for circuits without v data * Changes_in_variable_name_for_voltagevisualization Changed the variable name "DisconnectionPercentage" to "VoltageVisualization". --------- Co-authored-by: Phoebe <[email protected]>
UNSW-CEEM · Mar 25, 2024 · e895c3f · e895c3f
1 parent ea8e6cb
commit e895c3f
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Showing 5 changed files with 133 additions and 2 deletions.
diff --git a/anti_islanding_detection/anti_islanding.R b/anti_islanding_detection/anti_islanding.R
@@ -133,8 +133,8 @@ summarise_voltage_data <- function(combined_data) {
     filter(in_event_window) %>%
     group_by(c_id) %>%
     summarise(
-      vmax_max = max(ifelse(vmax_na, v, vmax), na.rm = TRUE),
-      vmin_min = min(ifelse(vmin_na, v, vmin), na.rm = TRUE),
+      vmax_max = max_with_missing(ifelse(vmax_na, v, vmax)),
+      vmin_min = min_with_missing(ifelse(vmin_na, v, vmin)),
       vmean_mean = mean(ifelse(vmean_na, v, vmean), na.rm = TRUE),
       vmin_na_all = all(vmin_na),
       vmax_na_all = all(vmax_na),
@@ -164,3 +164,7 @@ summarise_voltage_data <- function(combined_data) {
     )
   return(summarised_voltage_data)
 }
+
+max_with_missing <- function(x) ifelse(!all(is.na(x)), max(x, na.rm = TRUE), NA)
+
+min_with_missing <- function(x) ifelse(!all(is.na(x)), min(x, na.rm = TRUE), NA)
diff --git a/load_tool_environment.R b/load_tool_environment.R
@@ -49,3 +49,4 @@ source("run_analysis/run_analysis.R")
 source("anti_islanding_detection/anti_islanding.R")
 source("export/export_kml.R")
 source("out_of_tool_processing/create_voltage_kml.R")
+source("voltage_visualization/calculate_disconnection_percentage.R")
diff --git a/run_analysis/run_analysis.R b/run_analysis/run_analysis.R
@@ -749,6 +749,9 @@ run_analysis <- function(data, settings) {
             title = "Manufacturers missing from datasets",
             body = long_error_message
           )
+
+          # Adding disconnection percentage to data 
+          data$disconnection_percentage <- calc_percentage_disconnect_or_droptozero_DPVs(data$circuit_summary, data$combined_data_f, settings$duration)
         }
       }
     }

diff --git a/shiny.R b/shiny.R
@@ -164,6 +164,8 @@ ui <- fluidPage(
           uiOutput(outputId = "save_distance_response"),
           plotlyOutput(outputId = "ZoneCount"),
           uiOutput("save_zone_count"),
+          plotlyOutput(outputId = "VoltageVisualization"),
+          uiOutput("save_disconnection_percentage"),
           plotlyOutput(outputId = "map"),
           uiOutput(outputId = "save_circuit_kml"),
           HTML("<br><br>"),
@@ -421,6 +423,8 @@ reset_chart_area <- function(output) {
   output$Voltage <- renderPlotly({})
   output$distance_response <- renderPlotly({})
   output$save_distance_response <- renderUI({})
+  output$VoltageVisualization <- renderPlotly({})
+  output$save_disconnection_percentage <- renderUI({})
   output$map <- renderPlotly({})
   output$save_circuit_kml <- renderUI({})
 }
@@ -602,6 +606,7 @@ server <- function(input, output, session) {
     postcode_data = data.frame(),
     response_count = data.frame(),
     zone_count = data.frame(),
+    disconnection_percentage = data.frame(),
     distance_response = data.frame(),
     frequency_data = data.frame(),
     unique_offsets = c(),
@@ -1260,6 +1265,26 @@ server <- function(input, output, session) {
         output$save_zone_count <- renderUI({
           shinySaveButton("save_zone_count", "Save zone response data", "Save file as ...", filetype = list(xlsx = "csv"))
         })
+
+        # # adding a plot for disconnection percentages for each zone
+        output$VoltageVisualization <- renderPlotly({
+        plot_ly(
+          v$disconnection_percentage,
+          x = ~zone,
+          y = ~Percentage,
+          color = ~Voltage_Category,
+          type = "bar"
+        ) %>%
+          layout(
+            yaxis = list(title = "Disconnection/Drop to Zero Percentage (%))"),
+            xaxis = list(title = "Zone categories"),
+            barmode = "stack"
+          )
+        })
+        output$save_disconnection_percentage <- renderUI({
+          shinySaveButton("save_disconnection_percentage", "Save data", "Save file as ...", filetype=list(xlsx="csv"))
+        })
+
         if (dim(v$frequency_data)[1] > 0) {
           output$Frequency <- renderPlotly({
             plot_ly(v$agg_power, x = ~Time, y = ~Frequency, color = ~series, type = "scattergl") %>%
@@ -1847,6 +1872,16 @@ server <- function(input, output, session) {
       write.csv(v$antiislanding_summary, as.character(fileinfo$datapath), row.names = FALSE)
     }
   })
+
+  # Save data on disconnection percentage
+  observeEvent(input$save_disconnection_percentage, {
+    volumes <- c(home = getwd())
+    shinyFileSave(input, "save_disconnection_percentage", roots = volumes, session = session)
+    fileinfo <- parseSavePath(volumes, input$save_disconnection_percentage)
+    if (nrow(fileinfo) > 0) {
+      write.csv(v$disconnection_percentage, as.character(fileinfo$datapath), row.names = FALSE)
+    }
+  })
 
   get_current_settings <- function() {
     settings <- vector(mode = "list")

diff --git a/voltage_visualization/calculate_disconnection_percentage.R b/voltage_visualization/calculate_disconnection_percentage.R
@@ -0,0 +1,88 @@
+# Finding percentage disconnected/Dropped to Zero DPVs at specific voltage ranges
+
+calc_percentage_disconnect_or_droptozero_DPVs <- function(circuit_summary, combined_data_filtered, duration){
+
+  find_rows_disconnected_or_droptozero <- filter(circuit_summary,
+                                                 circuit_summary$response_category == '4 Disconnect' | circuit_summary$response_category == '3 Drop to Zero')
+
+  circuit_counts_for_voltage <- find_rows_disconnected_or_droptozero %>%
+    mutate(Voltage_Category = case_when(
+      is.na(vmin_min) ~ "No voltage data",
+      vmin_min >= 180 & vmin_min < 200 ~ "180V \u2264 Vmin < 200V", 
+      vmin_min >= 200 & vmin_min < 220 ~ "200V \u2264 Vmin < 220V",
+      vmin_min >= 220 & vmin_min < 240 ~ "220V \u2264 Vmin < 240V",
+      vmin_min >= 240 & vmin_min < 260 ~ "240V \u2264 Vmin < 260V",
+      vmax_max >= 260 & vmax_max < 265 ~ "260V \u2264 Vmax < 265V",
+      vmax_max >= 265 ~ "Vmax \u2265 265V",
+      vmin_min < 180 & vmax_max > 265 ~ "Vmin < 180V & Vmax > 265V",
+    )) %>%
+    group_by(zone, Voltage_Category) %>%
+    summarise(Count_ccts = n()) %>%
+    ungroup()
+
+  circuit_counts_for_voltage <- data.frame(circuit_counts_for_voltage)
+
+  total_disconnect_droptozero_counts_per_zone <- circuit_counts_for_voltage %>%
+    group_by(zone) %>%
+    summarise(total_count_disconnect = sum(Count_ccts))
+
+  total_counts_per_zone <- circuit_summary %>%
+    group_by(zone) %>%
+    summarise(total_count = n())
+
+  merged_data_voltage_visualization <- merge(total_counts_per_zone, total_disconnect_droptozero_counts_per_zone, by = "zone", all = TRUE) %>%
+    merge(circuit_counts_for_voltage, by = "zone", all = TRUE) %>%
+    mutate(Percentage = (Count_ccts / total_count) * 100)
+
+  # analysing Vmin <= 180V seperately
+  data_vmin_less_than_180 <- combined_data_filtered %>%
+    arrange(c_id, ts)%>%
+    filter(response_category == '4 Disconnect' | response_category == '3 Drop to Zero') %>%
+    filter(!is.na(vmin)) %>%
+    mutate(flag_vmin = ifelse(vmin <= 180, 1, 0), 
+           group_id = cumsum(flag_vmin == 0)) %>%
+    group_by(c_id, group_id) %>%
+    mutate(consecutive_count = cumsum(flag_vmin)) %>%
+    mutate(start_time = ifelse(consecutive_count == 1, ts, NA),
+           end_time = ifelse(consecutive_count == max(consecutive_count) & flag_vmin != 0, ts, NA)) %>%
+    fill(start_time, .direction = "down") %>%
+    fill(end_time, .direction = "up") %>%
+    mutate(start_end = as.POSIXct(start_time, origin = "1970-01-01",   tz = "UTC")) %>%
+    mutate(end_end = as.POSIXct(end_time, origin = "1970-01-01", tz = "UTC")) %>%
+    mutate(diff_time = end_time - start_time + as.numeric(duration))
+
+  for_10s_df <- data_vmin_less_than_180 %>%
+    filter(diff_time == 10) %>%
+    distinct(c_id, zone)
+
+  less_than_10s_df <- data_vmin_less_than_180 %>%
+    filter(diff_time < 10) %>%
+    distinct(c_id, zone) %>%
+    anti_join(for_10s_df, by = c("c_id", "zone"))
+
+  more_than_10s_df <- data_vmin_less_than_180 %>%
+    filter(diff_time > 10) %>%
+    distinct(c_id, zone) %>%
+    anti_join(for_10s_df, by = c("c_id", "zone")) %>%
+    anti_join(less_than_10s_df, by = c("c_id", "zone"))
+
+  combined_unique_cids <- bind_rows(
+    for_10s_df %>% mutate(Voltage_Category = "Vmin < 180V for 10s"),
+    less_than_10s_df %>% mutate(Voltage_Category = "Vmin < 180V for less than 10s"),
+    more_than_10s_df %>% mutate(Voltage_Category = "Vmin < 180V for more than 10s")
+  )
+
+  data_representing_time_delay <- combined_unique_cids %>%
+    group_by(Voltage_Category, zone) %>%
+    summarise(Count_ccts = n_distinct(c_id))
+
+  data_representing_time_delay <- merge(total_counts_per_zone, data_representing_time_delay, by = "zone", all = TRUE)
+  data_representing_time_delay$Percentage <- (data_representing_time_delay$Count_ccts / data_representing_time_delay$total_count) * 100
+
+  data_to_plot <- bind_rows(
+    select(merged_data_voltage_visualization, zone, Voltage_Category, Percentage),
+    select(data_representing_time_delay, zone, Voltage_Category, Percentage)
+  )
+
+  return(data_to_plot)
+}