add ASTM E1036 parameter extraction

pvlib/ivtools/__init__.py

@@ -4,4 +4,4 @@
 
 """
 
-from pvlib.ivtools import sde, sdm, utils  # noqa: F401
+from pvlib.ivtools import sde, sdm, utils, params  # noqa: F401

pvlib/ivtools/params.py

@@ -0,0 +1,114 @@
+"""
+The ``params`` module contains classes and functions to extract parameters
+(e.g. Isc and Voc) from current-voltage curves.
+"""
+import pandas as pd
+import numpy as np
+from numpy.polynomial.polynomial import Polynomial as Poly
+
+
+def astm_e1036(v, i, imax_limits=(0.75, 1.15), vmax_limits=(0.75, 1.15),
+               voc_points=3, isc_points=3):
+    '''
+    Extract photovoltaic IV parameters according to ASTM E1036. Assumes the
+    curve is in the first quadrant
+
+    Parameters
+    ----------
+    v : array-like
+        Voltage points
+    i : array-like
+        Current points
+    imax_limits : tuple
+        Two-element tuple (low, high) specifying the fraction of estimated
+        Imp within which to fit a polynomial for max power calculation
+    vmax_limits : tuple
+        Two-element tuple (low, high) specifying the fraction of estimated
+        Vmp within which to fit a polynomial for max power calculation
+    voc_points : int
+        the number of points near open circuit to use for linear fit
+        and Voc calculation
+    isc_points : int
+        the number of points near short circuit to use for linear fit and
+        Isc calculation
+
+    Returns
+    -------
+    dict
+        Calculated IV parameters
+
+    Adapted from https://github.com/NREL/iv_params
+    Copyright (c) 2022, Alliance for Sustainable Energy, LLC
+    All rights reserved.
+    '''
+
+    df = pd.DataFrame()
+    df['v'] = v
+    df['i'] = i
+    df['p'] = df['v'] * df['i']
+
+    # first calculate estimates of voc and isc
+    voc = np.nan
+    isc = np.nan
+
+    # determine if we can use voc and isc estimates
+    i_min_ind = df['i'].abs().idxmin()
+    v_min_ind = df['v'].abs().idxmin()
+    voc_est = df['v'][i_min_ind]
+    isc_est = df['i'][v_min_ind]
+
+    # accept the estimates if they are close enough
+    if abs(df['i'][i_min_ind]) <= isc_est * 0.001:
+        voc = voc_est
+    if abs(df['v'][v_min_ind]) <= voc_est * 0.005:
+        isc = isc_est
+
+    # perform a linear fit if estimates rejected
+    if np.isnan(voc):
+        df['i_abs'] = df['i'].abs()
+        voc_df = df.nsmallest(voc_points, 'i_abs')
+        voc_fit = Poly.fit(voc_df['i'], voc_df['v'], 1)
+        voc = voc_fit(0)
+
+    if np.isnan(isc):
+        df['v_abs'] = df['v'].abs()
+        isc_df = df.nsmallest(isc_points, 'v_abs')
+        isc_fit = Poly.fit(isc_df['v'], isc_df['i'], 1)
+        isc = isc_fit(0)
+
+    # estimate max power point
+    max_index = df['p'].idxmax()
+    mp_est = df.loc[max_index]
+
+    # filter around max power
+    mask = (
+        (df['i'] >= imax_limits[0] * mp_est['i']) &
+        (df['i'] <= imax_limits[1] * mp_est['i']) &
+        (df['v'] >= vmax_limits[0] * mp_est['v']) &
+        (df['v'] <= vmax_limits[1] * mp_est['v'])
+    )
+    filtered = df[mask]
+
+    # fit polynomial and find max
+    mp_fit = Poly.fit(filtered['v'], filtered['p'], 4)
+    roots = mp_fit.deriv().roots()
+    # only coniser real roots
+    roots = roots.real[abs(roots.imag) < 1e-5]
+    # only consider roots in the relevant part of the domain
+    roots = roots[(roots < filtered['v'].max()) &
+                  (roots > filtered['v'].min())]
+    vmp = roots[np.argmax(mp_fit(roots))]
+    pmp = mp_fit(vmp)
+    imp = pmp / vmp
+
+    ff = pmp / (voc * isc)
+
+    result = {}
+    result['voc'] = voc
+    result['isc'] = isc
+    result['vmp'] = vmp
+    result['imp'] = imp
+    result['pmp'] = pmp
+    result['ff'] = ff
+
+    return result