Calibrate a hot-spot factor for given power transformer.
Based on a continuous nominal load with a duration of one week, a specified constant ambient temperature,
and a hot-spot temperature limit, the hot-spot factor is calibrated to get as close as
possible to 100% nominal load while staying within the 'hot_spot_factor_min' and 'hot_spot_factor_max' bounds.
Parameters:
| Name |
Type |
Description |
Default |
uncalibrated_transformer
|
PowerTransformer
|
A power transformer without a specified hot-spot factor.
|
required
|
hot_spot_limit
|
float
|
temperature limit for the transformer hot-spot.
|
required
|
ambient_temp
|
float
|
constant ambient temperature in degrees Celsius used for the temperature calculations.
|
required
|
hot_spot_factor_min
|
float
|
minimum allowed value for the hot-spot factor.
|
required
|
hot_spot_factor_max
|
float
|
maximum allowed value for the hot-spot factor.
|
required
|
Returns:
| Name | Type |
Description |
PowerTransformer |
PowerTransformer
|
A calibrated power transformer, where the hot-spot factor is now specified.
|
Source code in transformer_thermal_model/hot_spot_calibration/calibrate_hotspot_factor.py
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92 | def calibrate_hotspot_factor(
uncalibrated_transformer: PowerTransformer,
hot_spot_limit: float,
ambient_temp: float,
hot_spot_factor_min: float,
hot_spot_factor_max: float,
) -> PowerTransformer:
"""Calibrate a hot-spot factor for given power transformer.
Based on a continuous nominal load with a duration of one week, a specified constant ambient temperature,
and a hot-spot temperature limit, the hot-spot factor is calibrated to get as close as
possible to 100% nominal load while staying within the 'hot_spot_factor_min' and 'hot_spot_factor_max' bounds.
Args:
uncalibrated_transformer (PowerTransformer): A power transformer without a specified hot-spot factor.
hot_spot_limit (float): temperature limit for the transformer hot-spot.
ambient_temp (float): constant ambient temperature in degrees Celsius used for the temperature calculations.
hot_spot_factor_min (float): minimum allowed value for the hot-spot factor.
hot_spot_factor_max (float): maximum allowed value for the hot-spot factor.
Returns:
PowerTransformer: A calibrated power transformer, where the hot-spot factor is now specified.
"""
if hot_spot_factor_min > hot_spot_factor_max:
raise ValueError("The upper bound cannot be smaller than the lower bound of the hot-spot factor limits.")
logger.info(
"Calibrating the hot-spot factor of the transformer. The current hot-spot factor equals"
+ f"{uncalibrated_transformer.specs.hot_spot_fac}."
)
one_week_steps = 4 * 24 * 7
datetime_index = pd.date_range("2020-01-01", periods=one_week_steps, freq="15min")
calibrated_transformer = copy.deepcopy(uncalibrated_transformer)
# Because the transformer should be calibrated to have a max hot-spot temperature at a constant
# ambient temperature (generally 20 degrees celsius), the transformer.temp_verh should not be used
# during calibration.
calibrated_transformer.specs.amb_temp_surcharge = 0.0
continuous_load = pd.Series([calibrated_transformer.specs.nom_load_sec_side] * one_week_steps, index=datetime_index)
ambient_temp_profile = pd.Series([ambient_temp] * one_week_steps, index=datetime_index)
model_input = InputProfile.create(
datetime_index=datetime_index, load_profile=continuous_load, ambient_temperature_profile=ambient_temp_profile
)
# Initiate the difference, one of the termination criteria, to a positive number.
difference = 100
# Initiate the new_hot_spot_factor with the upper limit. This is the starting point for the search algorithm which
# iterativily lowers the new_hot_spot_factor until a valid value is found.
new_hot_spot_factor = hot_spot_factor_max
calibrated_transformer._set_HS_fac(new_hot_spot_factor)
while difference > 0 and (new_hot_spot_factor >= hot_spot_factor_min - 0.01):
old_hot_spot_factor = new_hot_spot_factor
model = Model(
temperature_profile=model_input,
transformer=calibrated_transformer,
)
results = model.run().convert_to_dataframe()
hot_spot_max = results["hot_spot_temperature"].max()
# Calculate the difference which is used as a termination criterium in the while loop:
# the maximum temperature of the hot-spot should be below the hot-spot temperature limit.
difference = hot_spot_max - hot_spot_limit
# Lower the hot-spot factor with a small number for the next iteration.
new_hot_spot_factor = old_hot_spot_factor - 0.01
calibrated_transformer._set_HS_fac(new_hot_spot_factor)
calibrated_hot_spot_factor = np.clip(old_hot_spot_factor, a_min=hot_spot_factor_min, a_max=hot_spot_factor_max)
calibrated_transformer._set_HS_fac(calibrated_hot_spot_factor)
# During calibration the amb_temp_surcharge was set to zero. To return a transformer with correct specs,
# the amb_temp_surcharge is set to the correct value again.
calibrated_transformer.specs.amb_temp_surcharge = uncalibrated_transformer.specs.amb_temp_surcharge
logger.info(
"The hot-spot factor of the transformer is calibrated. The new hot-spot factor equals"
+ f"{calibrated_transformer.specs.hot_spot_fac}."
)
return calibrated_transformer
|