Resilience template rework

US/CA/SLAC/.index

@@ -1,3 +1,4 @@
 ica_analysis
 electrification
 loadfactor
+anticipation

US/CA/SLAC/anticipation/.catalog

@@ -0,0 +1,7 @@
+header.glm:a=r
+folium.glm:a=r
+add_info.py:a=rx
+status_log.py:a=rx
+folium_data.py:a=rx
+convert_to_csv.glm:a=rx
+veg_data_preprocess.py:a=rx

US/CA/SLAC/anticipation/add_info.py

@@ -0,0 +1,7 @@
+import pandas as pd
+csv_input = pd.read_csv('path_vege.csv')
+wind_speed = 10
+width = 5
+csv_input['wind_speed'] = wind_speed
+csv_input['width'] = width
+csv_input.to_csv('path_vege.csv', index=False)

US/CA/SLAC/anticipation/convert_to_csv.glm

@@ -0,0 +1 @@
+#write ${modelname/.glm/}_feeder.csv overhead_line,node:phases

US/CA/SLAC/anticipation/folium.glm

@@ -0,0 +1,47 @@
+module powerflow;
+#set relax_naming_rules=TRUE
+class pole
+{
+	double pole_height;
+	double position;
+	double linesag;
+	double linesway;
+	double contact;
+	double strike;
+}
+class point
+{
+	double latitude;
+	double longitude;
+	double position;
+	double linesag;
+	double linesway;
+	double contact;
+	double strike;
+	char1024 status;
+}
+object pole_configuration
+{
+	name "tower";
+}
+object pole_configuration
+{
+	name "3pole";
+}
+object pole_configuration
+{
+	name "2pole";
+}
+object pole_configuration
+{
+	name "vert3";
+}
+object pole_configuration
+{
+	name "sideT";
+}
+object pole_configuration
+{
+	name "flat3";
+}
+#input "path_result_plot.csv" -f "table" -t "object" -C "point" -M "powerflow"

US/CA/SLAC/anticipation/folium_data.py

@@ -0,0 +1,10 @@
+import pandas as pd
+csv_input = pd.read_csv('path_result.csv', \
+	usecols = ['position', 'latitude', 'longitude', 'linesag', 'linesway', 'contact', 'strike'])
+csv_input['status'] = 'OK'		# set default as OK
+# read the strike value 
+strike_threshold = 0.1
+for index, row in csv_input.iterrows():
+	if row['strike'] > strike_threshold:
+		csv_input.loc[index, "status"] = 'FAILED'
+csv_input.to_csv('path_result_plot.csv', index=False)

US/CA/SLAC/anticipation/header.glm

@@ -0,0 +1,76 @@
+// Test poles
+//
+//
+// Convert command:
+//   gridlabd convert CARDINAL_Polar\ -\ Design\ CalcDesign\ DSO.xlsx cardinal_poles.csv -f xlsx-spida -t csv-geodata
+//
+// Run command:
+//   gridlabd main_cardinal_poles.glm // cardinal_poles.glm
+//
+// Python command:
+//   convert('CARDINAL_Polar - Design CalcDesign DSO.xlsx', 'CARDINAL_PolarDesign Attachment and Equipment_Asset Details from SPIDA and SAP.xlsx', 'cardinal_poles.csv', options={'extract_equipment':'yes','include_network':'yes', 'include_mount':'yes', 'include_network':'CARDINAL.csv'})
+//
+#define suppress_repeat_messages=TRUE
+#define pythonpath="/usr/local/opt/gridlabd/current/share/gridlabd/template/US/CA/SLAC/anticipation"
+//#exec printenv
+#set minimum_timestep=3600
+
+
+module status_log;
+module climate;
+module tape;
+
+module powerflow {
+	solver_method NR;
+	line_capacitance true;
+	message_flags VERBOSE;
+	}
+#ifdef RELIABILITY_ON
+module reliability {
+    report_event_log true;
+}
+object fault_check {				
+	name test_fault;
+	check_mode ONCHANGE;	
+	reliability_mode true;		
+//	output_filename ${FAULT_OUT_PATH};	
+	grid_association true;
+};
+
+object power_metrics {		
+	name pwrmetrics;
+	base_time_value 1 h;	
+}
+
+object metrics {
+	name testmetrics;
+	report_file metrics.txt;						
+	module_metrics_object pwrmetrics;					
+	metrics_of_interest "SAIFI,SAIDI,CAIDI,ASAI,MAIFI";
+	customer_group "class=meter";			
+	metric_interval 5 h; 								
+	report_interval 5 h;								
+};
+#endif
+clock {
+	starttime "${STARTTIME}";
+	stoptime "${STOPTIME}";
+	timezone "${TIMEZONE}";
+}
+
+
+#ifdef WIND_SPEED
+object climate {
+	name weather;
+	object player {
+		property wind_speed; 
+		file ${WIND_SPEED};
+	};
+}
+#else 
+#weather get CA-Chino_Airport.tmy3
+object climate {
+	name weather;
+	tmyfile "CA-Chino_Airport.tmy3";
+}
+#endif

US/CA/SLAC/anticipation/status_log.py

@@ -0,0 +1,53 @@
+import pandas as pd
+import datetime
+
+metered_energy = {}
+pole_status = {}
+objs = {}
+wrn_count = 0
+
+def get_info(t, object_class, target_property) : 
+    for obj in objs:
+        if gridlabd.get_object(obj)['class'] == object_class:
+            if t not in data:
+                data[t] = {}
+            data[t][obj] = gridlabd.get_object(obj)[target_property]
+    return data
+
+def dump_csv(data,file_name ) : 
+    # Flatten the nested dictionary
+    flat_data = []
+
+    for timestamp, item in data.items():
+        row = {'timestamp': datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')}
+        row.update(item)
+        flat_data.append(row)
+
+    # Create a DataFrame
+    df = pd.DataFrame(flat_data).set_index('timestamp')
+    df.to_csv(file_name, index=True)
+    return df
+
+def on_init(t) : 
+    global objs 
+    objs = gridlabd.get('objects')
+    return True
+
+def on_commit(t) :
+    global pole_status, metered_energy, wrn_count
+    pole_status.update(get_info(t,'pole', 'status'))
+    # pole_status.update(get_info(t,'pole', 'total_moment'))
+    try : 
+        metered_energy.update(get_info(t,'meter','measured_real_energy'))
+    finally : 
+        if wrn_count == 0 : 
+            print(f"WARNING [status_log]: meters are not available in the model.")
+            wrn_count = 1
+    return True 
+
+def on_term(t) : 
+    global pole_status, metered_energy
+
+    df_energy = dump_csv(pole_status,'/tmp/output/pole_status.csv')
+    dump_csv(metered_energy, '/tmp/output/metered_energy.csv')
+    return None

US/CA/SLAC/anticipation/veg_data_preprocess.py

@@ -0,0 +1,35 @@
+import pandas as pd
+import sys
+
+def extract_poles() :
+	input_file_name = sys.argv[1]
+	output_file_name = sys.argv[2]
+
+	df = pd.read_csv(input_file_name)
+
+	# Create a new DataFrame with only rows where 'class' is 'pole'
+	filtered_df = df[df['class'] == 'pole']
+
+	# Reset the index of the filtered DataFrame
+	filtered_df.reset_index(drop=True, inplace=True)
+
+	# Extract 'pole_length' from the corresponding 'pole_configuration' rows
+	pole_length_map = dict(zip(df[df['class'] == 'pole_configuration']['name'], df[df['class'] == 'pole_configuration']['pole_length']))
+
+	# Map 'pole_length' values based on 'class' using .map after creating a copy
+	filtered_df = filtered_df.copy()  # Create a copy to avoid SettingWithCopyWarning
+
+	filtered_df['pole_length'] = filtered_df['configuration'].map(pole_length_map)
+
+	# Check for missing values in latitude and longitude columns and drop rows with any missing value
+	filtered_df.dropna(subset=["latitude", "longitude"], inplace=True)
+
+	# Reset the index
+	filtered_df.reset_index(drop=True, inplace=True)
+
+	# Display the final DataFrame
+	filtered_df.to_csv(output_file_name, index=False)
+
+
+if __name__ == "__main__":
+	extract_poles()