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@cwhanse
cwhanse committed Apr 8, 2024
1 parent c0d47b3 commit eae4a84
Showing 1 changed file with 27 additions and 27 deletions.
54 changes: 27 additions & 27 deletions docs/examples/snow-detection/snow-mode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
both operating voltage and current are reduced.
Mode 2: The system is online and covered with opaque snow, such that
operating voltage is reduced by the snow, but transmission
is consistent with snow-free conditions. #TODO seems contradictory
is consistent with snow-free conditions. # TODO seems contradictory
Mode 3: The system is online and covered with light-transmissive snow,
such that current is decreased but voltage is consistent with all
system substrings being online.
Expand Down Expand Up @@ -68,9 +68,9 @@
cec_inverter_db = pvlib.pvsystem.retrieve_sam('CECInverter')
my_inverter = cec_inverter_db[config['inverter']]

max_ac_power = my_inverter['Paco']*0.001 # originally in [W], converted to [kW]
mppt_low_voltage = my_inverter['Mppt_low'] # [V]
mppt_high_voltage = my_inverter['Mppt_high'] # [V]
max_ac_power = my_inverter['Paco']*0.001 # originally in W, convert to kW
mppt_low_voltage = my_inverter['Mppt_low'] # [V]
mppt_high_voltage = my_inverter['Mppt_high'] # [V]

print(f"Inverter AC power rating: {max_ac_power} kW")
print(f"Inverter MPPT range: {mppt_low_voltage} V - {mppt_high_voltage} V")
Expand Down Expand Up @@ -376,7 +376,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
1. Model effective irradiance based on measured current using the SAPM
2. Calculate transmission
3. Uses transmission to model voltage with the SAPM.
#TODO How is this voltage different than measured (snow-affected voltage)?
# TODO How is this voltage different than measured (snow-affected voltage)?
4. Determine the snow mode for each point.
Parameters
Expand Down Expand Up @@ -453,16 +453,16 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
# where=((voltage > 0) & (modeled_vmp>0)))
# vmp_ratio[modeled_vmp==0] = 0

#TODO lets vectorize the function itself
# TODO lets vectorize the function itself
categorize_v = np.vectorize(categorize)

mode = categorize_v(vmp_ratio, T, voltage, config['min_dcv'],
config['threshold_vratio'],
config['threshold_transmission'])
my_dict = {'transmission' : T,
'modeled_vmp' : modeled_vmp,
'vmp_ratio' : vmp_ratio,
'mode' : mode}
my_dict = {'transmission': T,
'modeled_vmp': modeled_vmp,
'vmp_ratio': vmp_ratio,
'mode': mode}

return my_dict

Expand Down Expand Up @@ -495,12 +495,12 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
threshold_vratio = config['threshold_vratio']
threshold_transmission = config['threshold_transmission']

my_config = {'threshold_vratio' : threshold_vratio,
'threshold_transmission' : threshold_transmission,
'min_dcv' : mppt_low_voltage,
'max_dcv' : mppt_high_voltage,
'num_str_per_cb' : int(config['num_str_per_cb'][f'{inv_cb}']),
'num_mods_per_str' : int(config['num_mods_per_str'][f'{inv_cb}'])}
my_config = {'threshold_vratio': threshold_vratio,
'threshold_transmission': threshold_transmission,
'min_dcv': mppt_low_voltage,
'max_dcv': mppt_high_voltage,
'num_str_per_cb': int(config['num_str_per_cb'][f'{inv_cb}']),
'num_mods_per_str': int(config['num_mods_per_str'][f'{inv_cb}'])}

out = wrapper(data[v], data[i],
data['Cell Temp [C]'],
Expand All @@ -523,12 +523,12 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
config['num_str_per_cb'][f'INV{inv_num} CB{cb_num}'])

my_config = {
'threshold_vratio' : threshold_vratio,
'threshold_transmission' : threshold_transmission,
'min_dcv' : mppt_low_voltage,
'max_dcv' : mppt_high_voltage,
'num_str_per_cb' : int(config['num_str_per_cb'][f'{inv_cb}']),
'num_mods_per_str' : int(config['num_mods_per_str'][f'{inv_cb}'])}
'threshold_vratio': threshold_vratio,
'threshold_transmission': threshold_transmission,
'min_dcv': mppt_low_voltage,
'max_dcv': mppt_high_voltage,
'num_str_per_cb': int(config['num_str_per_cb'][f'{inv_cb}']),
'num_mods_per_str': int(config['num_mods_per_str'][f'{inv_cb}'])}

out = wrapper(data[v_col], data[i_col],
data['Cell Temp [C]'],
Expand Down Expand Up @@ -605,7 +605,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
p = modeled_power_cols[i]

# Color intervals by mode
cmap = {0 : 'r',
cmap = {0: 'r',
1: 'b',
2: 'yellow',
3: 'cyan',
Expand All @@ -617,7 +617,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
temp = data[~data[m].isna()]

# Plot each day individually so we are not exaggerating losses
days_mapped = temp.index.map(lambda x : x.date())
days_mapped = temp.index.map(lambda x: x.date())
days = np.unique(days_mapped)
grouped = temp.groupby(days_mapped)

Expand Down Expand Up @@ -646,7 +646,7 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
color=cmap[temp_grouped.at[temp_grouped.index[end], m]],
alpha=0.05)

# # Add different colored intervals to legend
# Add different colored intervals to legend
handles, labels = ax.get_legend_handles_labels()

modeled_line = Line2D([0], [0], label='Modeled', color='k', ls='--')
Expand Down Expand Up @@ -679,8 +679,8 @@ def wrapper(voltage, current, temp_cell, effective_irradiance,
# four modes that are associated with the presence of snow.

# Daily snowfall is measured at 7:00 am of each day
snow = pd.read_csv(snowfall_file, index_col='DATE') # originally in [mm]
snow['SNOW'] *= 1/(10*2.54) # convert to [in]
snow = pd.read_csv(snowfall_file, index_col='DATE') # originally in [mm]
snow['SNOW'] *= 1/(10*2.54) # convert to [in]

loss_cols = [c for c in data.columns if "Loss" in c]
mode_cols = [c for c in data.columns if "mode" in c and "modeled" not in c]
Expand Down

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