"""
The ``modelchain_example`` module shows a simple usage of the windpowerlib by
using the :class:`~.modelchain.ModelChain` class. The modelchains are
implemented to ensure an easy start into the Windpowerlib. They work like
models that combine all functions provided in the library. Via parameteres
desired functions of the windpowerlib can be selected. For parameters not being
specified default parameters are used.
There are mainly three steps. First you have to import your weather data, then
you need to specify your wind turbine, and in the last step call the
windpowerlib functions to calculate the feed-in time series.
"""
__copyright__ = "Copyright oemof developer group"
__license__ = "GPLv3"
import os
import pandas as pd
try:
from matplotlib import pyplot as plt
except ImportError:
plt = None
from windpowerlib import ModelChain
from windpowerlib import WindTurbine
# You can use the logging package to get logging messages from the windpowerlib
# Change the logging level if you want more or less messages
import logging
logging.getLogger().setLevel(logging.DEBUG)
[docs]def get_weather_data(filename='weather.csv', **kwargs):
r"""
Imports weather data from a file.
The data include wind speed at two different heights in m/s, air
temperature in two different heights in K, surface roughness length in m
and air pressure in Pa. The file is located in the example folder of the
windpowerlib. The height in m for which the data applies is specified in
the second row.
Parameters
----------
filename : string
Filename of the weather data file. Default: 'weather.csv'.
Other Parameters
----------------
datapath : string, optional
Path where the weather data file is stored.
Default: 'windpowerlib/example'.
Returns
-------
weather_df : pandas.DataFrame
DataFrame with time series for wind speed `wind_speed` in m/s,
temperature `temperature` in K, roughness length `roughness_length`
in m, and pressure `pressure` in Pa.
The columns of the DataFrame are a MultiIndex where the first level
contains the variable name as string (e.g. 'wind_speed') and the
second level contains the height as integer at which it applies
(e.g. 10, if it was measured at a height of 10 m).
"""
if 'datapath' not in kwargs:
kwargs['datapath'] = os.path.join(os.path.split(
os.path.dirname(__file__))[0], 'example')
file = os.path.join(kwargs['datapath'], filename)
# read csv file
weather_df = pd.read_csv(file, index_col=0, header=[0, 1])
# change type of index to datetime and set time zone
weather_df.index = pd.to_datetime(weather_df.index).tz_localize(
'UTC').tz_convert('Europe/Berlin')
# change type of height from str to int by resetting columns
weather_df.columns = [weather_df.axes[1].levels[0][
weather_df.axes[1].labels[0]],
weather_df.axes[1].levels[1][
weather_df.axes[1].labels[1]].astype(int)]
return weather_df
[docs]def initialize_wind_turbines():
r"""
Initializes two :class:`~.wind_turbine.WindTurbine` objects.
Function shows three ways to initialize a WindTurbine object. You can
either specify your own turbine, as done below for 'my_turbine', or fetch
power and/or power coefficient curve data from the OpenEnergy Database
(oedb), as done for the 'enercon_e126', or provide your turbine data in csv
files as done for 'dummy_turbine' with an example file.
Execute ``windpowerlib.wind_turbine.get_turbine_types()`` to get a table
including all wind turbines for which power and/or power coefficient curves
are provided.
Returns
-------
Tuple (WindTurbine, WindTurbine, WindTurbine)
"""
# specification of own wind turbine (Note: power values and nominal power
# have to be in Watt)
my_turbine = {
'name': 'myTurbine',
'nominal_power': 3e6, # in W
'hub_height': 105, # in m
'rotor_diameter': 90, # in m
'power_curve': pd.DataFrame(
data={'value': [p * 1000 for p in [
0.0, 26.0, 180.0, 1500.0, 3000.0, 3000.0]], # in W
'wind_speed': [0.0, 3.0, 5.0, 10.0, 15.0, 25.0]}) # in m/s
}
# initialize WindTurbine object
my_turbine = WindTurbine(**my_turbine)
# specification of wind turbine where power curve is provided in the oedb
# if you want to use the power coefficient curve change the value of
# 'fetch_curve' to 'power_coefficient_curve'
enercon_e126 = {
'name': 'E-126/4200', # turbine type as in register #
'hub_height': 135, # in m
'rotor_diameter': 127, # in m
'fetch_curve': 'power_curve', # fetch power curve #
'data_source': 'oedb' # data source oedb or name of csv file
}
# initialize WindTurbine object
e126 = WindTurbine(**enercon_e126)
# specification of wind turbine where power coefficient curve is provided
# by a csv file
csv_file = os.path.join(os.path.dirname(__file__), 'data',
'example_power_coefficient_curves.csv')
dummy_turbine = {
'name': 'DUMMY 1', # turbine type as in file #
'hub_height': 100, # in m
'rotor_diameter': 70, # in m
'fetch_curve': 'power_coefficient_curve', # fetch cp curve #
'data_source': csv_file # data source
}
# initialize WindTurbine object
dummy_turbine = WindTurbine(**dummy_turbine)
return my_turbine, e126, dummy_turbine
[docs]def calculate_power_output(weather, my_turbine, e126, dummy_turbine):
r"""
Calculates power output of wind turbines using the
:class:`~.modelchain.ModelChain`.
The :class:`~.modelchain.ModelChain` is a class that provides all necessary
steps to calculate the power output of a wind turbine. You can either use
the default methods for the calculation steps, as done for 'my_turbine',
or choose different methods, as done for the 'e126'. Of course, you can
also use the default methods while only changing one or two of them, as
done for 'dummy_turbine'.
Parameters
----------
weather : pd.DataFrame
Contains weather data time series.
my_turbine : WindTurbine
WindTurbine object with self provided power curve.
e126 : WindTurbine
WindTurbine object with power curve from the OpenEnergy Database.
dummy_turbine : WindTurbine
WindTurbine object with power coefficient curve from example file.
"""
# power output calculation for my_turbine
# initialize ModelChain with default parameters and use run_model method
# to calculate power output
mc_my_turbine = ModelChain(my_turbine).run_model(weather)
# write power output time series to WindTurbine object
my_turbine.power_output = mc_my_turbine.power_output
# power output calculation for e126
# own specifications for ModelChain setup
modelchain_data = {
'wind_speed_model': 'logarithmic', # 'logarithmic' (default),
# 'hellman' or
# 'interpolation_extrapolation'
'density_model': 'ideal_gas', # 'barometric' (default), 'ideal_gas' or
# 'interpolation_extrapolation'
'temperature_model': 'linear_gradient', # 'linear_gradient' (def.) or
# 'interpolation_extrapolation'
'power_output_model': 'power_curve', # 'power_curve' (default) or
# 'power_coefficient_curve'
'density_correction': True, # False (default) or True
'obstacle_height': 0, # default: 0
'hellman_exp': None} # None (default) or None
# initialize ModelChain with own specifications and use run_model method
# to calculate power output
mc_e126 = ModelChain(e126, **modelchain_data).run_model(weather)
# write power output time series to WindTurbine object
e126.power_output = mc_e126.power_output
# power output calculation for example_turbine
# own specification for 'power_output_model'
mc_example_turbine = ModelChain(
dummy_turbine,
power_output_model='power_coefficient_curve').run_model(weather)
dummy_turbine.power_output = mc_example_turbine.power_output
return
[docs]def plot_or_print(my_turbine, e126, dummy_turbine):
r"""
Plots or prints power output and power (coefficient) curves.
Parameters
----------
my_turbine : WindTurbine
WindTurbine object with self provided power curve.
e126 : WindTurbine
WindTurbine object with power curve from data file provided by the
windpowerlib.
dummy_turbine : WindTurbine
WindTurbine object with power coefficient curve from example file.
"""
# plot or print turbine power output
if plt:
e126.power_output.plot(legend=True, label='Enercon E126')
my_turbine.power_output.plot(legend=True, label='myTurbine')
dummy_turbine.power_output.plot(legend=True, label='dummyTurbine')
plt.show()
else:
print(e126.power_output)
print(my_turbine.power_output)
print(dummy_turbine.power_output)
# plot or print power (coefficient) curve
if plt:
if e126.power_coefficient_curve is not None:
e126.power_coefficient_curve.plot(
x='wind_speed', y='power coefficient', style='*',
title='Enercon E126 power coefficient curve')
plt.show()
if e126.power_curve is not None:
e126.power_curve.plot(x='wind_speed', y='value', style='*',
title='Enercon E126 power curve')
plt.show()
if my_turbine.power_coefficient_curve is not None:
my_turbine.power_coefficient_curve.plot(
x='wind_speed', y='power coefficient', style='*',
title='myTurbine power coefficient curve')
plt.show()
if my_turbine.power_curve is not None:
my_turbine.power_curve.plot(x='wind_speed', y='value', style='*',
title='myTurbine power curve')
plt.show()
else:
if e126.power_coefficient_curve is not None:
print(e126.power_coefficient_curve)
if e126.power_curve is not None:
print(e126.power_curve)
[docs]def run_example():
r"""
Runs the basic example.
"""
weather = get_weather_data('weather.csv')
my_turbine, e126, dummy_turbine = initialize_wind_turbines()
calculate_power_output(weather, my_turbine, e126, dummy_turbine)
plot_or_print(my_turbine, e126, dummy_turbine)
if __name__ == "__main__":
run_example()