os.path.join(file_dir, "../windtoolkit_geo_center_us.p"))
# plot wind rose
wind_rose.plot_wind_rose()
# =============================================================================
print("Finding baseline and optimal yaw angles in FLORIS...")
# =============================================================================
# Without uncertainty
# Instantiate the Optimization object
yaw_opt = YawOptimizationWindRose(
fi,
df.wd,
df.ws,
minimum_yaw_angle=min_yaw,
maximum_yaw_angle=max_yaw,
minimum_ws=minimum_ws,
maximum_ws=maximum_ws,
)
# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()
# Perform optimization
df_opt = yaw_opt.optimize()
# With uncertainty
# Instantiate the Optimization object
yaw_opt = YawOptimizationWindRose(
fi,
# freq = np.abs(np.sort(np.random.randn(len(wd))))
# freq = freq / freq.sum()
# df = wind_rose.make_wind_rose_from_user_data(wd, ws)
# plot wind rose
wind_rose.plot_wind_rose()
# plt.show()
# lkj
# Instantiate the Optimization object
yaw_opt = YawOptimizationWindRose(
fi,
df.wd,
df.ws,
minimum_yaw_angle=min_yaw,
maximum_yaw_angle=max_yaw,
minimum_ws=minimum_ws,
maximum_ws=maximum_ws,
opt_options=opt_options,
)
# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()
# Perform optimization
df_opt = yaw_opt.optimize()
print("base: ", df_base["power_baseline"])
print("opt: ", df_opt["power_opt"])
# Initialize power rose
# Use simple weibull
wind_rose = wfct.wind_rose.WindRose()
freq = wind_rose.weibull(ws_list)
freq = freq / np.sum(freq)
# freq = np.ones_like(ws_list) / num_cases
# Compute and time the AEP calculation
# # Now check the timing
print("===START TEST===")
start = time.perf_counter()
# Instantiate the Optimization object
# Note that the optimization is not performed in this example.
# Assuming power is zero below 3 m/s
yaw_opt = YawOptimizationWindRose(fi, wd_list, ws_list, minimum_ws=3)
# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()
# Create wind rose DataFrame
df = pd.DataFrame({"wd": wd_list, "ws": ws_list, "freq_val": freq})
# Initialize power rose
case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(case_name, df,
df_base["power_no_wake"],
df_base["power_baseline"])
# convert to Watt-hours
else:
df = wind_rose.load(
os.path.join(file_dir,
"../optimization/scipy/windtoolkit_geo_center_us.p"))
# plot wind rose
wind_rose.plot_wind_rose()
# =============================================================================
print("Finding power with and without wakes in FLORIS...")
# =============================================================================
# Instantiate the Optimization object
# Note that the optimization is not performed in this example.
yaw_opt = YawOptimizationWindRose(fi, df.wd, df.ws, minimum_ws=minimum_ws)
# Determine baseline power with and without wakes
df_base = yaw_opt.calc_baseline_power()
# Initialize power rose
case_name = "Example " + str(N_row) + " x " + str(N_row) + " Wind Farm"
power_rose = pr.PowerRose()
power_rose.make_power_rose_from_user_data(case_name, df,
df_base["power_no_wake"],
df_base["power_baseline"])
# Display AEP analysis
fig, axarr = plt.subplots(2, 1, sharex=True, figsize=(6.4, 6.5))
power_rose.plot_by_direction(axarr)
power_rose.report()