from __future__ import annotations
from collections.abc import Iterable
import numpy as np
import yaml
[docs]
def build_cosine_loss_turbine_dict(
turbine_data_dict,
turbine_name,
file_name=None,
generator_efficiency=None,
hub_height=90.0,
cosine_loss_exponent_yaw=1.88,
cosine_loss_exponent_tilt=1.88,
rotor_diameter=125.88,
TSR=8.0,
ref_air_density=1.225,
ref_tilt=5.0
):
"""
Tool for formatting a full turbine dict from data formatted as a
dictionary.
Default value for turbine physical parameters are from the NREL 5MW reference
wind turbine.
Returns a turbine dictionary object as expected by FLORIS. Optionally,
prints the dictionary to a yaml to be included in a FLORIS wake model yaml.
turbine_data is a dictionary that contains keys specifying the
turbine power and thrust as a function of wind speed. The following keys
are possible:
- wind_speed [m/s]
- power [kW]
- power_coefficient [-]
- thrust [kN]
- thrust_coefficient [-]
Of these, wind_speed is required. One of power and power_coefficient
must be specified; and one of thrust and thrust_coefficient must be
specified. If both (absolute) and _coefficient versions are specified, the
(absolute) power will be used along with the thrust_coefficient, with the
other entries ignored.
Args:
turbine_data_dict (dict): Dictionary containing performance of the wind
turbine as a function of wind speed. Described in more detail above.
turbine_name (string): Name of the turbine, which will be used for the
turbine_type field as well as the filename.
file_name (): Name for the produced yaml, including possibly path.
Defaults to None, in which case no yaml is written.
generator_efficiency (float): Generator efficiency [-]. Unused if power is specified
in absolute terms in the turbine_data_dict. Must be specified if
power not specified and power_coefficient specified instead. Defaults to None.
hub_height (float): Hub height [m]. Defaults to 90.0.
cosine_loss_exponent_yaw (float): Cosine exponent for power loss to yaw [-].
Defaults to 1.88.
cosine_loss_exponent_tilt (float): Cosine exponent for thrust loss to yaw [-].
Defaults to 1.88.
rotor_diameter (float). Rotor diameter [m]. Defaults to 126.0.
TSR (float). Turbine optimal tip-speed ratio [-]. Defaults to 8.0.
ref_air_density (float). Air density used to specify power and thrust
curves [kg/m^3]. Defaults to 1.225.
ref_tilt (float). Rotor tilt (due to shaft tilt and/or platform
tilt) used when defining the power and thrust curves [deg]. Defaults
to 5.0.
Returns:
turbine_dict (dict): Formatted turbine dictionary as expected by FLORIS.
"""
# Check that necessary columns are specified
if "wind_speed" not in turbine_data_dict:
raise KeyError("wind_speed column must be specified.")
u = np.array(turbine_data_dict["wind_speed"])
A = np.pi * rotor_diameter**2/4
# Construct the Cp curve
if "power" in turbine_data_dict:
if "power_coefficient" in turbine_data_dict:
print(
"Found both power and power_coefficient. "
"Ignoring power_coefficient."
)
p = np.array(turbine_data_dict["power"])
elif "power_coefficient" in turbine_data_dict:
if generator_efficiency is None:
raise KeyError(
"generator_efficiency must be specified to convert power_coefficient to power."
)
Cp = np.array(turbine_data_dict["power_coefficient"])
if _find_nearest_value_for_wind_speed(Cp, u, 10) > 16.0/27.0 or \
_find_nearest_value_for_wind_speed(Cp, u, 10) < 0.0:
print(
"Unusual power coefficient detected. Check that power coefficients"
"are physical."
)
validity_mask = (Cp != 0) | (u != 0)
p = np.zeros_like(Cp, dtype=float)
p[validity_mask] = (
Cp[validity_mask]
* 0.5 * ref_air_density * A * generator_efficiency
* u[validity_mask]**3 / 1000
)
else:
raise KeyError(
"Either power or power_coefficient must be specified."
)
# Construct Ct curve
if "thrust_coefficient" in turbine_data_dict:
if "thrust" in turbine_data_dict:
print(
"Found both thrust and thrust_coefficient. "
"Ignoring thrust."
)
Ct = np.array(turbine_data_dict["thrust_coefficient"])
elif "thrust" in turbine_data_dict:
T = np.array(turbine_data_dict["thrust"])
if _find_nearest_value_for_wind_speed(T, u, 10) > 3000 or \
_find_nearest_value_for_wind_speed(T, u, 10) < 100:
print(
"Unusual thrust value detected. Please check that thrust",
"is specified in kN."
)
validity_mask = (T != 0) | (u != 0)
Ct = np.zeros_like(T)
Ct[validity_mask] = (T[validity_mask]*1000)/\
(0.5*ref_air_density*A*u[validity_mask]**2)
else:
raise KeyError(
"Either thrust or thrust_coefficient must be specified."
)
# Build the turbine dict
power_thrust_dict = {
"ref_air_density": ref_air_density,
"ref_tilt": ref_tilt,
"cosine_loss_exponent_yaw": cosine_loss_exponent_yaw,
"cosine_loss_exponent_tilt": cosine_loss_exponent_tilt,
"wind_speed": u.tolist(),
"power": p.tolist(),
"thrust_coefficient": Ct.tolist()
}
turbine_dict = {
"turbine_type": turbine_name,
"hub_height": hub_height,
"rotor_diameter": rotor_diameter,
"TSR": TSR,
"operation_model": "cosine-loss",
"power_thrust_table": power_thrust_dict
}
# Create yaml file
if file_name is not None:
yaml.dump(
turbine_dict,
open(file_name, "w"),
sort_keys=False
)
print(file_name, "created.")
return turbine_dict
def _find_nearest_value_for_wind_speed(test_vals, ws_vals, ws):
errs = np.absolute(ws_vals-ws)
idx = errs.argmin()
return test_vals[idx]
[docs]
def check_smooth_power_curve(power, tolerance=0.001):
"""
Check whether there are "wiggles" in the power signal.
"""
if power[-1] < 0.95*max(power): # Cut-out or shutdown included
expected_changes = 2
else: # Shutdown appears not to be included
expected_changes = 1
dirs = np.where(
np.abs(np.diff(power)) > tolerance,
np.sign(np.diff(power)),
np.zeros(len(power)-1)
)
dir_changes = np.sum(np.abs(np.diff(dirs)))
is_smooth = dir_changes <= expected_changes
return is_smooth
