sw_08_opt_yaw_single_ws.py

# Copyright 2022 NREL

# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.

# See https://floris.readthedocs.io for documentation

import numpy as np
import matplotlib.pyplot as plt
from floris.tools import FlorisInterface
from floris.tools.optimization.yaw_optimization.yaw_optimizer_sr import (
    YawOptimizationSR,
)

"""
This example demonstrates how to perform a yaw optimization for multiple wind directions and 1 wind speed.

First, we initialize our Floris Interface, and then generate a 3 turbine wind farm. Next, we create the yaw optimization object `yaw_opt` and perform the optimization using the SerialRefine method. Finally, we plot the results.
"""

# Load the default example floris object
fi = FlorisInterface("examples/inputs/cc_60mw.yaml") # GCH model matched to the default "legacy_gauss" of V2
# fi = FlorisInterface("examples/inputs/cc.yaml") # New CumulativeCurl model

# Reinitialize as a 3-turbine farm with range of WDs and 1 WS
D = 126.0 # Rotor diameter for the NREL 5 MW

for ws in np.arange(3.0, 26.0, 1.0):

    fi.reinitialize(
        #layout=[[0.0, 5 * D, 10 * D], [0.0, 0.0, 0.0]],
        wind_directions=np.arange(181.0, 271.0, 1.0), 
        wind_speeds=[ws], #a
    )

    print(fi.floris.farm.rotor_diameters)


    # Initialize optimizer object and run optimization using the Serial-Refine method
    yaw_opt = YawOptimizationSR(fi)  #, exploit_layout_symmetry=False)
    df_opt = yaw_opt.optimize()

    print("Optimization results:")
    print(df_opt)
    # dh. txt output
    f=open("_rst_08_"+str(ws)+"_180-270.txt","a",encoding="utf8")
    print("Optimization results: \n",file=f)
    print(df_opt,file=f)

    # to CSV
    df_opt.yaw_angles_opt.to_csv('sw08_yaw_'+str(ws)+'_180-270.csv',index=False)
    df_opt.to_csv('sw08_'+str(ws)+'__180-270.csv',index=False)


    """
    # Split out the turbine results
    for t in range(3):
        df_opt['t%d' % t] = df_opt.yaw_angles_opt.apply(lambda x: x[t])

    # Show the results
    fig, axarr = plt.subplots(2,1,sharex=True,sharey=False,figsize=(8,8))

    # Yaw results
    ax = axarr[0]
    for t in range(3):
        ax.plot(df_opt.wind_direction,df_opt['t%d' % t],label='t%d' % t)
    ax.set_ylabel('Yaw Offset (deg')
    ax.legend()
    ax.grid(True)

    # Power results
    ax = axarr[1]
    ax.plot(df_opt.wind_direction,df_opt.farm_power_baseline,color='k',label='Baseline Farm Power')
    ax.plot(df_opt.wind_direction,df_opt.farm_power_opt,color='r',label='Optimized Farm Power')
    ax.set_ylabel('Power (W)')
    ax.set_xlabel('Wind Direction (deg)')
    ax.legend()
    ax.grid(True)

    plt.show()
    """