def test_pvsyst_recombination_loss(method, poa, temp_cell, expected, tol):
"""test PVSst recombination loss"""
pvsyst_fs_495 = get_pvsyst_fs_495()
# first evaluate PVSyst model with thin-film recombination loss current
# at reference conditions
x = pvsystem.calcparams_pvsyst(
effective_irradiance=poa, temp_cell=temp_cell,
alpha_sc=pvsyst_fs_495['alpha_sc'],
gamma_ref=pvsyst_fs_495['gamma_ref'],
mu_gamma=pvsyst_fs_495['mu_gamma'], I_L_ref=pvsyst_fs_495['I_L_ref'],
I_o_ref=pvsyst_fs_495['I_o_ref'], R_sh_ref=pvsyst_fs_495['R_sh_ref'],
R_sh_0=pvsyst_fs_495['R_sh_0'], R_sh_exp=pvsyst_fs_495['R_sh_exp'],
R_s=pvsyst_fs_495['R_s'],
cells_in_series=pvsyst_fs_495['cells_in_series'],
EgRef=pvsyst_fs_495['EgRef']
)
il_pvsyst, io_pvsyst, rs_pvsyst, rsh_pvsyst, nnsvt_pvsyst = x
voc_est_pvsyst = estimate_voc(photocurrent=il_pvsyst,
saturation_current=io_pvsyst,
nNsVth=nnsvt_pvsyst)
vd_pvsyst = np.linspace(0, voc_est_pvsyst, 1000)
pvsyst = bishop88(
diode_voltage=vd_pvsyst, photocurrent=il_pvsyst,
saturation_current=io_pvsyst, resistance_series=rs_pvsyst,
resistance_shunt=rsh_pvsyst, nNsVth=nnsvt_pvsyst,
d2mutau=pvsyst_fs_495['d2mutau'],
NsVbi=VOLTAGE_BUILTIN*pvsyst_fs_495['cells_in_series']
)
# test max power
assert np.isclose(max(pvsyst[2]), expected['pmp'], *tol)
# test short circuit current
isc_pvsyst = np.interp(0, pvsyst[1], pvsyst[0])
assert np.isclose(isc_pvsyst, expected['isc'], *tol)
# test open circuit voltage
voc_pvsyst = np.interp(0, pvsyst[0][::-1], pvsyst[1][::-1])
assert np.isclose(voc_pvsyst, expected['voc'], *tol)
# repeat tests as above with specialized bishop88 functions
y = dict(d2mutau=pvsyst_fs_495['d2mutau'],
NsVbi=VOLTAGE_BUILTIN*pvsyst_fs_495['cells_in_series'])
mpp_88 = bishop88_mpp(*x, **y, method=method)
assert np.isclose(mpp_88[2], expected['pmp'], *tol)
isc_88 = bishop88_i_from_v(0, *x, **y, method=method)
assert np.isclose(isc_88, expected['isc'], *tol)
voc_88 = bishop88_v_from_i(0, *x, **y, method=method)
assert np.isclose(voc_88, expected['voc'], *tol)
ioc_88 = bishop88_i_from_v(voc_88, *x, **y, method=method)
assert np.isclose(ioc_88, 0.0, *tol)
vsc_88 = bishop88_v_from_i(isc_88, *x, **y, method=method)
assert np.isclose(vsc_88, 0.0, *tol)
def test_numerical_precision():
"""
Test that there are no numerical errors due to floating point arithmetic.
"""
expected = pd.read_csv(DATA_PATH)
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
results = bishop88(vdtest, *ARGS, gradients=True)
assert np.allclose(expected['i'], results[0])
assert np.allclose(expected['v'], results[1])
assert np.allclose(expected['p'], results[2])
assert np.allclose(expected['grad_i'], results[3])
assert np.allclose(expected['grad_v'], results[4])
assert np.allclose(expected['grad'], results[5])
assert np.allclose(expected['grad_p'], results[6])
assert np.allclose(expected['grad2p'], results[7])
def test_numerical_precision():
"""
Test that there are no numerical errors due to floating point arithmetic.
"""
expected = pd.read_csv(DATA_PATH)
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
results = bishop88(vdtest, *ARGS, gradients=True)
assert np.allclose(expected['i'], results[0])
assert np.allclose(expected['v'], results[1])
assert np.allclose(expected['p'], results[2])
assert np.allclose(expected['grad_i'], results[3])
assert np.allclose(expected['grad_v'], results[4])
assert np.allclose(expected['grad'], results[5])
assert np.allclose(expected['grad_p'], results[6])
assert np.allclose(expected['grad2p'], results[7])
def generate_numerical_precision(): # pragma: no cover
"""
Generate expected data with infinite numerical precision using SymPy.
:return: dataframe of expected values
"""
if symbols is NotImplemented:
LOGGER.critical("SymPy is required to generate expected data.")
raise ImportError("could not import sympy")
il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd')
a = sy_exp(vd / nnsvt)
b = 1.0 / rsh
i = il - io * (a - 1.0) - vd * b
v = vd - i * rs
c = io * a / nnsvt
grad_i = - c - b # di/dvd
grad_v = 1.0 - grad_i * rs # dv/dvd
# dp/dv = d(iv)/dv = v * di/dv + i
grad = grad_i / grad_v # di/dv
p = i * v
grad_p = v * grad + i # dp/dv
grad2i = -c / nnsvt
grad2v = -grad2i * rs
grad2p = (
grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i
)
# generate exact values
data = dict(zip((il, io, rs, rsh, nnsvt), ARGS))
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
expected = []
for test in vdtest:
data[vd] = test
test_data = {
'i': np.float64(i.evalf(subs=data)),
'v': np.float64(v.evalf(subs=data)),
'p': np.float64(p.evalf(subs=data)),
'grad_i': np.float64(grad_i.evalf(subs=data)),
'grad_v': np.float64(grad_v.evalf(subs=data)),
'grad': np.float64(grad.evalf(subs=data)),
'grad_p': np.float64(grad_p.evalf(subs=data)),
'grad2p': np.float64(grad2p.evalf(subs=data))
}
LOGGER.debug(test_data)
expected.append(test_data)
return pd.DataFrame(expected, index=vdtest)
def generate_numerical_precision():
"""
Generate expected data with infinite numerical precision using SymPy.
:return: dataframe of expected values
"""
if symbols is NotImplemented:
LOGGER.critical("SymPy is required to generate expected data.")
raise ImportError("could not import sympy")
il, io, rs, rsh, nnsvt, vd = symbols('il, io, rs, rsh, nnsvt, vd')
a = sy_exp(vd / nnsvt)
b = 1.0 / rsh
i = il - io * (a - 1.0) - vd * b
v = vd - i * rs
c = io * a / nnsvt
grad_i = - c - b # di/dvd
grad_v = 1.0 - grad_i * rs # dv/dvd
# dp/dv = d(iv)/dv = v * di/dv + i
grad = grad_i / grad_v # di/dv
p = i * v
grad_p = v * grad + i # dp/dv
grad2i = -c / nnsvt
grad2v = -grad2i * rs
grad2p = (
grad_v * grad + v * (grad2i/grad_v - grad_i*grad2v/grad_v**2) + grad_i
)
# generate exact values
data = dict(zip((il, io, rs, rsh, nnsvt), ARGS))
vdtest = np.linspace(0, estimate_voc(IL, I0, NNSVTH), IVCURVE_NPTS)
expected = []
for test in vdtest:
data[vd] = test
test_data = {
'i': np.float64(i.evalf(subs=data)),
'v': np.float64(v.evalf(subs=data)),
'p': np.float64(p.evalf(subs=data)),
'grad_i': np.float64(grad_i.evalf(subs=data)),
'grad_v': np.float64(grad_v.evalf(subs=data)),
'grad': np.float64(grad.evalf(subs=data)),
'grad_p': np.float64(grad_p.evalf(subs=data)),
'grad2p': np.float64(grad2p.evalf(subs=data))
}
LOGGER.debug(test_data)
expected.append(test_data)
return pd.DataFrame(expected, index=vdtest)
def test_pvsyst_recombination_loss(poa, temp_cell, expected, tol):
"""test PVSst recombination loss"""
pvsyst_fs_495 = get_pvsyst_fs_495()
# first evaluate PVSyst model with thin-film recombination loss current
# at reference conditions
x = pvsystem.calcparams_pvsyst(
effective_irradiance=poa, temp_cell=temp_cell,
alpha_sc=pvsyst_fs_495['alpha_sc'],
gamma_ref=pvsyst_fs_495['gamma_ref'],
mu_gamma=pvsyst_fs_495['mu_gamma'], I_L_ref=pvsyst_fs_495['I_L_ref'],
I_o_ref=pvsyst_fs_495['I_o_ref'], R_sh_ref=pvsyst_fs_495['R_sh_ref'],
R_sh_0=pvsyst_fs_495['R_sh_0'], R_sh_exp=pvsyst_fs_495['R_sh_exp'],
R_s=pvsyst_fs_495['R_s'],
cells_in_series=pvsyst_fs_495['cells_in_series'],
EgRef=pvsyst_fs_495['EgRef']
)
il_pvsyst, io_pvsyst, rs_pvsyst, rsh_pvsyst, nnsvt_pvsyst = x
voc_est_pvsyst = estimate_voc(photocurrent=il_pvsyst,
saturation_current=io_pvsyst,
nNsVth=nnsvt_pvsyst)
vd_pvsyst = np.linspace(0, voc_est_pvsyst, 1000)
pvsyst = bishop88(
diode_voltage=vd_pvsyst, photocurrent=il_pvsyst,
saturation_current=io_pvsyst, resistance_series=rs_pvsyst,
resistance_shunt=rsh_pvsyst, nNsVth=nnsvt_pvsyst,
d2mutau=pvsyst_fs_495['d2mutau'],
NsVbi=VOLTAGE_BUILTIN*pvsyst_fs_495['cells_in_series']
)
# test max power
assert np.isclose(max(pvsyst[2]), expected['pmp'], *tol)
# test short circuit current
isc_pvsyst = np.interp(0, pvsyst[1], pvsyst[0])
assert np.isclose(isc_pvsyst, expected['isc'], *tol)
# test open circuit current
voc_pvsyst = np.interp(0, pvsyst[0][::-1], pvsyst[1][::-1])
assert np.isclose(voc_pvsyst, expected['voc'], *tol)
def test_pvsyst_recombination_loss(pvsyst_fs_495, poa, temp_cell, expected,
tol):
"""test PVSst recombination loss"""
# first evaluate PVSyst model with thin-film recombination loss current
# at reference conditions
x = pvsystem.calcparams_pvsyst(
effective_irradiance=poa, temp_cell=temp_cell,
alpha_sc=pvsyst_fs_495['alpha_sc'],
gamma_ref=pvsyst_fs_495['gamma_ref'],
mu_gamma=pvsyst_fs_495['mu_gamma'], I_L_ref=pvsyst_fs_495['I_L_ref'],
I_o_ref=pvsyst_fs_495['I_o_ref'], R_sh_ref=pvsyst_fs_495['R_sh_ref'],
R_sh_0=pvsyst_fs_495['R_sh_0'], R_sh_exp=pvsyst_fs_495['R_sh_exp'],
R_s=pvsyst_fs_495['R_s'],
cells_in_series=pvsyst_fs_495['cells_in_series'],
EgRef=pvsyst_fs_495['EgRef']
)
il_pvsyst, io_pvsyst, rs_pvsyst, rsh_pvsyst, nnsvt_pvsyst = x
voc_est_pvsyst = estimate_voc(photocurrent=il_pvsyst,
saturation_current=io_pvsyst,
nNsVth=nnsvt_pvsyst)
vd_pvsyst = np.linspace(0, voc_est_pvsyst, 1000)
pvsyst = bishop88(
diode_voltage=vd_pvsyst, photocurrent=il_pvsyst,
saturation_current=io_pvsyst, resistance_series=rs_pvsyst,
resistance_shunt=rsh_pvsyst, nNsVth=nnsvt_pvsyst,
d2mutau=pvsyst_fs_495['d2mutau'],
NsVbi=VOLTAGE_BUILTIN*pvsyst_fs_495['cells_in_series']
)
# test max power
assert np.isclose(max(pvsyst[2]), expected['pmp'], *tol)
# test short circuit current
isc_pvsyst = np.interp(0, pvsyst[1], pvsyst[0])
assert np.isclose(isc_pvsyst, expected['isc'], *tol)
# test open circuit current
voc_pvsyst = np.interp(0, pvsyst[0][::-1], pvsyst[1][::-1])
assert np.isclose(voc_pvsyst, expected['voc'], *tol)
