def calculate_cell_temperature(self,
delta_T=3,
temperature_cell_key='temperature_cell'):
"""
Set cell temeperature in dataframe.
Returns
-------
"""
# Calculate cell temperature
self.df[temperature_cell_key] = sapm_cell_from_module(
module_temperature=self.df[self.temperature_module_key],
poa_global=self.df[self.irradiance_poa_key],
deltaT=delta_T)
print("Cell temperature assigned to '{}'".format(temperature_cell_key))
def test_sapm_cell_from_module(sapm_default):
default = temperature.sapm_cell_from_module(50, 900,
sapm_default['deltaT'])
assert_allclose(default, 50 + 900 / 1000 * sapm_default['deltaT'])
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from pvpro.fit import production_data_curve_fit
from pvpro.classify import classify_operating_mode
# Import synthetic data
df = pd.read_pickle('synth01_out.pkl')
from pvlib.temperature import sapm_cell_from_module
# Estimate cell temperature.
df['temperature_cell_meas'] = sapm_cell_from_module(
module_temperature=df['temperature_module_meas'],
poa_global=df['poa_meas'],
deltaT=3)
# Classify operating modes.
df['operating_cls'] = classify_operating_mode(voltage=df['v_dc'],
current=df['i_dc'],
power_clip=np.inf)
# Clip dataframe shorter.
df = df[:5000]
# Run the fit
pfit, residual, ret = production_data_curve_fit(
temperature_cell=df['temperature_cell_meas'],
effective_irradiance=df['poa_meas'],
operating_cls=df['operating_cls'],
voltage = np.array(list(df.keys())[1:]).astype('float')
# Choose fields for sensors.
irradiance_poa_key = 'RefCell5_Wm2_Avg'
temperature_module_key = 'RTD_C_Avg_13'
deltaT = 3
specs = {
'cells_in_series': 60,
'alpha_sc': 0.053 * 1e-2 * 8.6,
'beta_voc': -0.351 * 1e-2 * 37.0
}
# Get sensor values.
poa = ws1[irradiance_poa_key]
tm = ws2[temperature_module_key]
tc = sapm_cell_from_module(tm, poa, deltaT)
# Initialize
ivcurves = {}
for key in [
'i', 'v', 'tc', 'tm', 'ee', 'i_sc', 'v_oc', 'i_mp', 'v_mp',
'timestamp', 'idx'
]:
ivcurves[key] = []
n = 0
for k in range(len(df)):
# for k in range(1):
current = np.array(df.iloc[k, 1:]).astype('float')
# Clip dataframe to avoid bad data on startup. Could improve fit using
# better filtering of bad data
df = df[np.logical_and.reduce((
df[temperature_module_key] < 50,
df[temperature_module_key] > 15,
df.index > df.index[20000],
df.index < df.index[25000],
))]
# Datasheet values (Sharp NU-U235F2)'
imp_ref_datasheet = 7.84
vmp_ref_datasheet = 30
# Estimate cell temperature.
df['temperature_cell'] = sapm_cell_from_module(
module_temperature=df[temperature_module_key],
poa_global=df[poa_key],
deltaT=3)
# Classify operating modes, this is a simple fast algorithm to find MPP points.
df['operating_cls'] = classify_operating_mode(voltage=df[voltage_key],
current=df[current_key],
power_clip=np.inf)
# Mask off MPP points
mpp = df['operating_cls'] == 0
# Estimate imp ref
est_imp = estimate_imp_ref(poa=df.loc[mpp, poa_key],
temperature_cell=df.loc[mpp, 'temperature_cell'],
imp=df.loc[mpp, current_key],
figure=True)