"""Example: Uncertain Model Parameters
This example demonstrates how to use the UncertainFlorisModel class to
analyze the impact of uncertain wind direction on power results.
"""
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')
