import matplotlib.pyplot as plt
import numpy as np
from floris import (
FlorisModel,
TimeSeries,
UncertainFlorisModel,
)
# Instantiate FlorisModel for comparison
fmodel = FlorisModel("../inputs/gch.yaml") # GCH model
################################################
# Resolution parameters
################################################
# The resolution parameters are used to define the precision of the wind direction,
# wind speed, and turbulence intensity and control parameters. All the inputs
# passed into the UncertainFlorisModel class are rounded to this resolution. Then
# following expansion, non-unique cases are removed. Here we apply the default
# resolution parameters.
wd_resolution = 1.0 # Degree
ws_resolution = 1.0 # m/s
ti_resolution = 0.01 # Decimal fraction
yaw_resolution = 1.0 # Degree
power_setpoint_resolution = 100.0 # kW
################################################
# wd_sample_points
################################################
# The wind direction sample points (wd_sample_points) parameter is used to define
# the number of points to sample the wind direction uncertainty. For example,
# if the the single condition to analyze is 270 degrees, and the wd_sample_points
# is [-2, -1, 0, 1 ,2], then the cases to be run and weighted
# will be 268, 269, 270, 271, 272. If not supplied default is
# [-2 * wd_std, -1 * wd_std, 0, wd_std, 2 * wd_std]
wd_sample_points = [-6, -3, 0, 3, 6]
################################################
# WT_STD
################################################
# The wind direction standard deviation (wd_std) parameter is the primary input
# to the UncertainFlorisModel class. This parameter is used to weight the points
# following expansion by the wd_sample_points. The smaller the value, the closer
# the weighting will be to the nominal case.
wd_std = 3 # Default is 3 degrees
################################################
# Verbosity
################################################
# Setting verbose = True will print out the sizes of teh cases run
verbose = True
################################################
# Define the UncertainFlorisModel
################################################
print('*** Instantiating UncertainFlorisModel ***')
ufmodel = UncertainFlorisModel("../inputs/gch.yaml",
wd_resolution=wd_resolution,
ws_resolution=ws_resolution,
ti_resolution=ti_resolution,
yaw_resolution=yaw_resolution,
power_setpoint_resolution=power_setpoint_resolution,
wd_std=wd_std,
wd_sample_points=wd_sample_points,
verbose=verbose)
################################################
# Run the models
################################################
# Define an inflow where wind direction is swept while
# wind speed and turbulence intensity are held constant
wind_directions = np.arange(240.0, 300.0, 1.0)
time_series = TimeSeries(
wind_directions=wind_directions,
wind_speeds=8.0,
turbulence_intensities=0.06,
)
# Define a two turbine farm and apply the inflow
D = 126.0
layout_x = np.array([0, D * 6])
layout_y = [0, 0]
fmodel.set(
layout_x=layout_x,
layout_y=layout_y,
wind_data=time_series,
)
print('*** Setting UncertainFlorisModel to 60 Wind Direction Inflow ***')
ufmodel.set(
layout_x=layout_x,
layout_y=layout_y,
wind_data=time_series,
)
# Run both models
fmodel.run()
ufmodel.run()
# Collect the nominal and uncertain farm power
turbine_powers_nom = fmodel.get_turbine_powers() / 1e3
turbine_powers_unc = ufmodel.get_turbine_powers() / 1e3
farm_powers_nom = fmodel.get_farm_power() / 1e3
farm_powers_unc_3 = ufmodel.get_farm_power() / 1e3
# Plot results
fig, axarr = plt.subplots(1, 3, figsize=(15, 5))
ax = axarr[0]
ax.plot(wind_directions, turbine_powers_nom[:, 0].flatten(), color="k", label="Nominal power")
ax.plot(
wind_directions,
turbine_powers_unc[:, 0].flatten(),
color="r",
label="Power with uncertainty",
)
ax.grid(True)
ax.legend()
ax.set_xlabel("Wind Direction (deg)")
ax.set_ylabel("Power (kW)")
ax.set_title("Upstream Turbine")
ax = axarr[1]
ax.plot(wind_directions, turbine_powers_nom[:, 1].flatten(), color="k", label="Nominal power")
ax.plot(
wind_directions,
turbine_powers_unc[:, 1].flatten(),
color="r",
label="Power with uncertainty",
)
ax.set_title("Downstream Turbine")
ax.grid(True)
ax.legend()
ax.set_xlabel("Wind Direction (deg)")
ax.set_ylabel("Power (kW)")
ax = axarr[2]
ax.plot(wind_directions, farm_powers_nom.flatten(), color="k", label="Nominal farm power")
ax.plot(
wind_directions,
farm_powers_unc_3.flatten(),
color="r",
label="Farm power with uncertainty",
)
ax.set_title("Farm Power")
ax.grid(True)
ax.legend()
ax.set_xlabel("Wind Direction (deg)")
ax.set_ylabel("Power (kW)")
plt.show()
import warnings
warnings.filterwarnings('ignore')
