Welcome to the windpowerlib documentation!¶

Contents:

Getting started¶

Introduction¶

The windpowerlib is a library that provides a set of functions and classes to calculate the power output of wind turbines. It was originally part of the feedinlib (windpower and pv) but was taken out to build up a community concentrating on wind power models.

For a quick start see the Examples and basic usage section.

Documentation¶

Full documentation can be found at readthedocs.

Use the project site of readthedocs to choose the version of the documentation. Go to the download page to download different versions and formats (pdf, html, epub) of the documentation.

Installation¶

If you have a working Python3 environment, use pypi to install the latest windpowerlib version.

pip3 install windpowerlib

So far, the windpowerlib is mainly tested on python 3.4 but seems to work down to 2.7. Please see the installation page of the oemof documentation for complete instructions on how to install python on your operating system.

Optional Packages¶

To see the plots of the windpowerlib example in the Examples and basic usage section you should install the matplotlib package. Matplotlib can be installed using pip3 though some Linux users reported that it is easier and more stable to use the pre-built packages of your Linux distribution.

Examples and basic usage¶

The basic usage of the windpowerlib is shown here. The presented example is available as jupyter notebook and python script. You can download them along with example weather data:

Python script

Jupyter notebook

Example data file

To run the examples you first have to install the windpowerlib. To run the notebook you also need to install notebook using pip3. To launch jupyter notebook type jupyter notebook in terminal. This will open a browser window. Navigate to the directory containing the notebook to open it. See the jupyter notebook quick start guide for more information on how to install and how to run jupyter notebooks.

Contributing¶

Clone/Fork: https://github.com/wind-python/windpowerlib

If you are interested in wind models and want to help improve the existing model do not hesitate to contact us. As the windpowerlib started with contributors from the oemof developer group we use the same developer rules.

License¶

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Basic usage¶

This example shows you the basic usage of the windpowerlib. 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 feedin timeseries.

Before you start you have to import the packages needed for these steps.

Import necessary packages and modules¶

In [1]:

__copyright__ = "Copyright oemof developer group"
__license__ = "GPLv3"

import os
import pandas as pd

from windpowerlib.modelchain import ModelChain
from windpowerlib.wind_turbine import WindTurbine
from windpowerlib import wind_turbine as wt

You can use the logging package to get logging messages from the windpowerlib. Change the logging level if you want more or less messages.

In [2]:

import logging
logging.getLogger().setLevel(logging.DEBUG)

Import weather data¶

In order to use the windpowerlib you need to at least provide wind speed data for the time frame you want to analyse. The function below imports example weather data from the weather.csv file provided along with the windpowerlib. 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.

To find out which weather data in which units need to be provided to use the ModelChain or other functions of the windpowerlib see the individual function documentation.

In [3]:

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 (e.g. wind_speed) and the second level
        contains the height 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


# Read weather data from csv
weather = get_weather_data(filename='weather.csv', datapath='')
print(weather[['wind_speed', 'temperature', 'pressure']][0:3])

variable_name             wind_speed          temperature         pressure
height                            10       80          2       10       0
2010-01-01 00:00:00+01:00    5.32697  7.80697      267.60  267.57  98405.7
2010-01-01 01:00:00+01:00    5.46199  7.86199      267.60  267.55  98382.7
2010-01-01 02:00:00+01:00    5.67899  8.59899      267.61  267.54  98362.9

Initialise wind turbine¶

To initialise a specific turbine you need a dictionary that contains the basic parameters. A turbine is defined by its nominal power, hub height, rotor diameter, and power or power coefficient curve.

There are two ways to initialise a WindTurbine object in the windpowerlib. You can either specify your own turbine, as done below for ‘myTurbine’, or fetch power and/or power coefficient curve data from data files provided by the windpowerlib, as done for the ‘enerconE126’.

You can execute the following to get a list of all wind turbines for which power or power coefficient curves are provided.

In [4]:

# get power curves
# get names of wind turbines for which power curves are provided (default)
# set print_out=True to see the list of all available wind turbines
wt.get_turbine_types(print_out=False)

# get power coefficient curves
# write names of wind turbines for which power coefficient curves are provided
# to 'turbines' DataFrame
turbines = wt.get_turbine_types(filename='power_coefficient_curves.csv', print_out=False)
# find all Enercons in 'turbines' DataFrame
print(turbines[turbines["turbine_id"].str.contains("ENERCON")])

            turbine_id    p_nom
52   ENERCON E 70 2300  2300000
64  ENERCON E 101 3000  3000000
65  ENERCON E 126 7500  7500000
66  ENERCON E 115 2500  2500000
67    ENERCON E 48 800   800000
68   ENERCON E 82 2000  2000000
69    ENERCON E 53 800   800000
70   ENERCON E 58 1000  1000000
71   ENERCON E 70 2000  2000000
72   ENERCON E 82 2300  2300000
73   ENERCON E 82 3000  3000000
74   ENERCON E 92 2300  2300000
75  ENERCON E 112 4500  4500000

In [5]:

# specification of own wind turbine (Note: power coefficient values and
# nominal power have to be in Watt)
myTurbine = {
    'turbine_name': 'myTurbine',
    'nominal_power': 3e6,  # in W
    'hub_height': 105,  # in m
    'rotor_diameter': 90,  # in m
    'power_curve': pd.DataFrame(
            data={'values': [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
    }
# initialise WindTurbine object
my_turbine = WindTurbine(**myTurbine)

In [6]:

# specification of wind turbine where power curve is provided
# if you want to use the power coefficient curve add
# {'fetch_curve': 'power_coefficient_curve'} to the dictionary
enerconE126 = {
    'turbine_name': 'ENERCON E 126 7500',  # turbine name as in register
    'hub_height': 135,  # in m
    'rotor_diameter': 127  # in m
    }
# initialise WindTurbine object
e126 = WindTurbine(**enerconE126)

Use the ModelChain to calculate turbine power output¶

The ModelChain is a class that provides all necessary steps to calculate the power output of a wind turbine. If you use the ‘run_model’ method first the wind speed and density (if necessary) at hub height are calculated and then used to calculate the power output. 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’.

In [7]:

# power output calculation for my_turbine
# initialise 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 timeseries to WindTurbine object
my_turbine.power_output = mc_my_turbine.power_output

DEBUG:root:Calculating wind speed using logarithmic wind profile.
DEBUG:root:Calculating power output using power curve.

In [8]:

# 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

# initialise 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 timeseries to WindTurbine object
e126.power_output = mc_e126.power_output

DEBUG:root:Calculating wind speed using logarithmic wind profile.
DEBUG:root:Calculating temperature using temperature gradient.
DEBUG:root:Calculating density using ideal gas equation.
DEBUG:root:Calculating power output using power curve.

Plot results¶

If you have matplotlib installed you can visualise the calculated power output and used power (coefficient) curves.

In [9]:

# try to import matplotlib
try:
    from matplotlib import pyplot as plt
    # matplotlib inline needed in notebook to plot inline
    %matplotlib inline
except ImportError:
    plt = None

In [10]:

# plot turbine power output
if plt:
    e126.power_output.plot(legend=True, label='Enercon E126')
    my_turbine.power_output.plot(legend=True, label='myTurbine')
    plt.show()

In [11]:

# plot power (coefficient) curves
if plt:
    if e126.power_coefficient_curve is not None:
        e126.power_coefficient_curve.plot(
            x='wind_speed', y='values', style='*',
            title='Enercon E126 power coefficient curve')
        plt.show()
    if e126.power_curve is not None:
        e126.power_curve.plot(x='wind_speed', y='values', 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='values', 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='values', style='*',
                                    title='myTurbine power curve')
        plt.show()

In [12]:

What’s New¶

These are new features and improvements of note in each release

Releases

v0.0.6 (July 07, 2017)
v0.0.5 (April 10, 2017)

v0.0.6 (July 07, 2017)¶

Documentation¶

added basic usage section and jupyter notebook

Testing¶

added tests for different data types and wind_turbine module

added example to tests

added and fixed doctests

Other changes¶

various API changes due to having better comprehensible function and variable names

renamed Modelchain to ModelChain

moved functions for temperature calculation to temperature module

new function for interpolation and extrapolation

Contributors¶

Sabine Haas

Birgit Schachler

v0.0.5 (April 10, 2017)¶

New features¶

complete restructuring of the windpowerlib

power curves for numerous wind turbines are provided

new function for calculation of air density at hub height (rho_ideal_gas)

new function for calculation of wind speed at hub height (v_wind_hellman)

density correction for calculation of power output

modelchain for convenient usage

Documentation¶

added references

Testing¶

tests for all modules were added

Contributors¶

Sabine Haas

Birgit Schachler

API¶

Classes¶

`wind_turbine.WindTurbine`(turbine_name, ...)	Defines a standard set of wind turbine attributes.
`modelchain.ModelChain`(wind_turbine[, ...])	Model to determine the output of a wind turbine

Temperature¶

Function for calculating air temperature at hub height.

temperature.linear_gradient(temperature, ...) Calculates the temperature at hub height using a linear gradient.

Density¶

Functions for calculating air density at hub height.

`density.barometric`(pressure, ...)	Calculates the density of air at hub height using the barometric height equation.
`density.ideal_gas`(pressure, pressure_height, ...)	Calculates the density of air at hub height using the ideal gas equation.

Wind speed¶

Functions for calculating wind speed at hub height.

`wind_speed.logarithmic_profile`(wind_speed, ...)	Calculates the wind speed at hub height using a logarithmic wind profile.
`wind_speed.hellman`(wind_speed, ...[, ...])	Calculates the wind speed at hub height using the hellman equation.

Wind turbine data¶

Functions and methods to obtain the nominal power as well as power curve or power coefficient curve needed by the WindTurbine class.

`wind_turbine.WindTurbine.fetch_turbine_data`()	Fetches data of the requested wind turbine.
`wind_turbine.get_turbine_types`([print_out])	Get the names of all possible wind turbine types for which the power coefficient curve or power curve is provided in the data files in the directory windpowerlib/data.
`wind_turbine.read_turbine_data`(**kwargs)	Fetches power (coefficient) curves from a file.

Power output¶

Functions for calculating power output of a wind turbine.

`power_output.power_coefficient_curve`(...[, ...])	Calculates the turbine power output using a power coefficient curve.
`power_output.power_curve`(wind_speed, ...[, ...])	Calculates the turbine power output using a power curve.
`power_output.power_curve_density_correction`(...)	Calculates the turbine power output using a density corrected power curve.

ModelChain¶

Creating a ModelChain object.

modelchain.ModelChain(wind_turbine[, ...]) Model to determine the output of a wind turbine

Running the ModelChain.

modelchain.ModelChain.run_model(weather_df) Runs the model.

Methods of the ModelChain object.

`modelchain.ModelChain.temperature_hub`(weather_df)	Calculates the temperature of air at hub height.
`modelchain.ModelChain.density_hub`(weather_df)	Calculates the density of air at hub height.
`modelchain.ModelChain.wind_speed_hub`(weather_df)	Calculates the wind speed at hub height.
`modelchain.ModelChain.turbine_power_output`(...)	Calculates the power output of the wind turbine.

Tools¶

Additional functions used in the windpowerlib.

tools.linear_interpolation_extrapolation(df, ...) Inter- or extrapolates between the values of a data frame.

Example¶

The basic example consists of the following functions.

`basic_example.get_weather_data`([filename])	Imports weather data from a file.
`basic_example.initialise_wind_turbines`()	Initialises two `WindTurbine` objects.
`basic_example.calculate_power_output`(...)	Calculates power output of wind turbines using the `ModelChain`.
`basic_example.plot_or_print`(my_turbine, e126)	Plots or prints power output and power (coefficient) curves.
`basic_example.run_basic_example`()	Run the basic example.

Welcome to the windpowerlib documentation!¶

Getting started¶

Introduction¶

Documentation¶

Installation¶

Optional Packages¶

Examples and basic usage¶

Contributing¶

License¶

Basic usage¶

Import necessary packages and modules¶

Import weather data¶

Initialise wind turbine¶

Use the ModelChain to calculate turbine power output¶

Plot results¶

What’s New¶

v0.0.6 (July 07, 2017)¶

Documentation¶

Testing¶

Other changes¶

Contributors¶

v0.0.5 (April 10, 2017)¶

New features¶

Documentation¶

Testing¶

Contributors¶

API¶

Classes¶

Temperature¶

Density¶

Wind speed¶

Wind turbine data¶

Power output¶

ModelChain¶

Tools¶

Example¶

Indices and tables¶