@cwhanse Thanks a lot for sharing! 😊

I had to wait until the weekend to find some time to have a look at it... 😅

I see you have 2 separate implementations. Both implementations, however, look similar, and they are both used to calculate the same Sandia parameters. I wonder if a single function could/should be used, instead of two.

See for example:

from pvlib.inverter_fits import fit_sandia_datasheet
from pvlib.inverter_fits import fit_sandia_meas

dc_voltage_levels = {'Vmin': 220., 'Vnom': 240., 'Vmax': 260.}
p_ac_0 = 1000.

curves = read_csv("pvlib/data/inverter_fit_snl_datasheet.csv")
curves["dc_voltage"] = curves["dc_voltage_level"].apply(lambda level: dc_voltage_levels[level])
curves["ac_power"] = curves["pac"]

datasheet_params = fit_sandia_datasheet(curves, p_ac_0, dc_voltage_levels, 1.0)
meas_params = fit_sandia_meas(curves, p_ac_0, 1.0)

Now, this is what datasheet_params look like:

{'Paco': 1000.0,
 'Pdco': 1050.0,
 'Vdco': 240.0,
 'Pso': 10.000304429963938,
 'C0': 9.9883399204206e-07,
 'C1': 9.999994488189908e-05,
 'C2': 0.009999747784568034,
 'C3': 0.0010013954894991116,
 'Pnt': 1.0}

And the meas_params:

{'Paco': 1000.0,
 'Pdco': 1050.0000008984525,
 'Vdco': 240.0,
 'Pso': 9.999999860749542,
 'C0': 9.999994978389884e-07,
 'C1': 9.999996161482867e-05,
 'C2': 0.009999996315292192,
 'C3': 0.0010002283566649965,
 'Pnt': 1.0}

It seems like the _meas() function achieves better results. Also, even though benchmarks are always "subjective" to the environment they are run in, I get these results:

So maybe keeping just the _meas() function makes sense? Or maybe define the _datasheet() function to transform the input dataframe and then call _meas() internally.

I have not carefully read through the implementation of each function, and will wait to hear from you before doing so (in case I can skip one of the implementations 😇 ). Anyway they look great so far! 😊

Implement a function to calculate inverter model parameters #975

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