Welcome to the windpowerlib documentation!¶

Contents:

Getting started¶

The windpowerlib is designed to calculate feedin time series of wind power plants. The windpowerlib is an out-take from the feedinlib (windpower and pv) to build up a community concentrating on wind power models.

The windpowerlib is ready to use but may have some teething troubles. The used model is still very simple but we found some new contributors and the windpowerlib will improve soon. If you are interested in wind models and want to improve the existing model, publish an alternative model or extend it to wind farms do not hesitate to contact us.

Table of contents

Introduction
Installation
Example
Basic Usage

Introduction ¶

Having weather data sets you can use the windpowerlib to calculate the electrical output of common wind power plants. Basic parameters for many manufacturers are provided with the library so that you can start directly using one of these parameter sets. Of course you are free to add your own parameter set.

The cp-values for the wind turbines are provided by the Reiner Lemoine Institut and can be found here:

http://vernetzen.uni-flensburg.de/~git/cp_values.csv

Actual Release¶

Download/Install: https://pypi.python.org/pypi/windpowerlib/

Documentation: http://pythonhosted.org/windpowerlib/

Developing Version¶

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

Documentation: http://windpowerlib.readthedocs.org/en/latest/

As the windpowerlib started with contributors from the oemof developer group we use the same developer rules: http://oemof.readthedocs.io/en/stable/developer_notes.html

Installation ¶

Using the windpowerlib¶

So far, the windpowerlib is mainly tested on python 3.4 but seems to work down to 2.7.

Install the windpowerlib using pip3.

pip3 install windpowerlib

Developing the Windpowerlib¶

If you have push rights clone this repository to your local system.

git clone git@github.com:oemof/windpower.git

If you do not have push rights, fork the project at github, clone your personal fork to your system and send a pull request.

If the project is cloned you can install it using pip3 with the -e flag. Using this installation, every change is applied directly.

pip3 install -e <path/to/the/windpowerlib/root/dir>

Optional Packages¶

To see the plots of the example file one should install the matplotlib package.

Matplotlib can be installed using pip3 but some Linux users reported that it is easier and more stable to use the pre-built packages of your Linux distribution.

http://matplotlib.org/users/installing.html

Example ¶

Download the .csv data and the example file and execute it:

https://github.com/wind-python/windpowerlib/tree/master/example

Basic Usage ¶

You need three steps to get a time series.

Warning¶

Be accurate with the units. In the example all units are given without a prefix.

pressure [Pa]
wind speed [m/s]
installed capacity [W]
nominal power [W]
area [m²]
temperature [°C]

You can also use kW instead of W but you have to make sure that all units are changed in the same way.

1. Initialise your Turbine or Module¶

To initialise your specific turbine you need a dictionary that contains your basic parameters.

The most import parameter is the name of the turbine to get technical parameters from the provided libraries. Use the get_wind_pp_types function to get a list of all available converter types.

from windpowerlib import basicmodel
basicmodel.get_wind_pp_types()

The other parameters are related to location of the plant like diameter of the rotor or the hub height. The existing model needs the following parameters:

h_hub: height of the hub in meters

d_rotor: diameter of the rotor in meters

wind_conv_type: Name of the wind converter according to the list in the csv file

your_wind_turbine = basicmodel.SimpleWindTurbine(**your_parameter_set)

If you pass a valid model the nominal_power and the cp-values are read from a file. If you want to use your own converter you can pass your own cp-series and nominal power instead of the converter type. This can be done with a dictionary (as shown above) or directly.

your_wind_turbine = basicmodel.SimpleWindTurbine(cp_values=my_cp_values,
                                                 nominal_power=my_nominal_power,
                                                 d_rotor=my_d_rotor,
                                                 h_hub=my_h_hub)

2. Get your Feedin Time Series¶

To get your time series you have to pass a weather DataFrame (or dictionary) to your model.The DataFrame needs to have pressure, wind speed, temperature and the roughness length. The following names are used:

‘pressure’

‘temp_air’

‘v_wind’

‘z0’

In an additional dictionary the height of the weather data has to be defined. The example shows a dictionary for the coasdat2 weather data set:

coastDat2 = {
    'dhi': 0,
    'dirhi': 0,
    'pressure': 0,
    'temp_air': 2,
    'v_wind': 10,
    'Z0': 0}

If your weather DataFrame has different column names you have to rename them. This can easily be done by using a conversion dictionary:

name_dc = {
    'your pressure data set': 'pressure',
    'your ambient temperature': 'temp_air',
    'your wind speed': 'v_wind',
    'your roughness length': 'z0'}

your_weather_DataFrame.rename(columns=name_dc)

Now you can pass the weather data to the output method:

your_wind_turbine.turbine_power_output(weather=weather_df, data_height=coastDat2)

You will get the output of one wind_turbine in [W] if you followed the united recommendations from above.

What’s New¶

These are new features and improvements of note in each release

Releases

v0.0.4 ()
v0.0.3 (November 18, 2016)
v0.0.1 (August 29, 2016)

v0.0.4 ()¶

New features¶

cp-values database file is now part of the repository and will be installed using pip/setup.py. The former download server was down due to technical problems and it might be safer not to be reliant on an external server.

Documentation¶

Testing¶

Bug fixes¶

Other changes¶

Contributors¶

Uwe Krien

v0.0.3 (November 18, 2016)¶

Other changes¶

Allow installation of windpowerlib for python versions >3.4 Import requests package instead of urllib5

Contributors¶

Uwe Krien

Stephen Bosch

Birgit Schachler

Comment¶

Release of v0.0.2 has been skipped due to a mistake in the release process.

v0.0.1 (August 29, 2016)¶

The wind part of the feedinlib was transferd to the winpowerlib.

Contributors¶

Uwe Krien

Model Description¶

So far only two simple models are provided but there will improvements and a larger variety in future version hopefully with the help of the github community.

Wind Model¶

The wind model is a simple model based on the cp-values of a specific type of a wind turbine. The cp-values are provided by the manufacturer of the wind turbine as a list of cp-values for discrete wind speeds in steps of 0.5 or 1 m/s. The feedinlib makes an linear interpolation of these values to get a continuous cp-curve over the wind speeds.

$P_{wpp}=\frac{1}{8}\cdot\rho_{air,hub}\cdot d_{rotor}^{2}\cdot\pi\cdot v_{wind,hub}^{3}\cdot cp\left(v_{wind,hub}\right)$

with $d_{rotor}$ the diameter of the rotor in meters, $\rho_{air,hub}$ the density of the air at hub height, $v_{wind,hub}$ the wind speed at hub height and $cp$ the cp-values against the wind speed.

The wind speed at hub height is determined by the following equation, assuming a logarithmic wind profile.

$v_{wind,hub}=v_{wind,data}\cdot\frac{\ln\left(\frac{h_{hub}}{z_{0}}\right)}{\ln\left(\frac{h_{wind,data}}{z_{0}}\right)}$

with $v_{wind,hub}$ the wind speed at the height of the weather model or measurement, $h_{hub}$ the height of the hub and $h_{wind,data}$ the height of the wind speed measurement or the height of the wind speed within the weather model.

The density of the air is calculated assuming a temperature gradient of -6.5 K/km and a pressure gradient of -1/8 hPa/m.

$T_{hub}=T_{air, data}-0.0065\cdot\left(h_{hub}-h_{T,data}\right)$

with $T_{air, data}$ the temperature at the height of the weather model or measurement, $h_{hub}$ the height of the hub and $h_{T,Data}$ the height of temperature measurement or the height of the temperature within the weather model.

$\rho_{air,hub}=\left(p_{data}/100-\left(h_{hub}-h_{p,data}\right)*\frac{1}{8}\right)/\left(2.8706\cdot T_{hub}\right)$

with $p_{data}$ the pressure at the height of the weather model or measurement, $T_{hub}$ the temperature of the air at hub height, $h_{hub}$ the height of the hub and $h_{p,data}$ the height of pressure measurement or the height of pressure within the weather model.

Weather Data¶

The weather data manly used by the oemof developing group is the coastDat2 of the Helmholtz-Zentrum Geesthacht

http://wiki.coast.hzg.de/pages/viewpage.action?pageId=4751533

Due to licence problems we are not allowed to ship the dataset with the windpowerlib. Please contact the Helmholtz-Zentrum Geesthacht (HZG), the data set for Europe might be free for non commercial use.

windpowerlib¶

windpowerlib package¶

Submodules¶

windpowerlib.basicmodel module¶

class windpowerlib.basicmodel.SimpleWindTurbine(wind_conv_type=None, h_hub=None, d_rotor=None, cp_values=None, nominal_power=None)[source]¶

Bases: object

Model to determine the output of a wind turbine

Parameters:

wind_conv_type (string) – Name of the wind converter type. Use get_wind_pp_types() to see a list of all possible wind converters.
h_hub (float) – Height of the hub of the wind turbine.
d_rotor (float) – Diameter of the rotor.
cp_values (pandas.DataFrame) – The index should be the wind speed and a column should be named ‘cp’.
nominal_power (float) – The nominal output of the wind power plant.

wind_conv_type¶: string – Name of the wind converter type. Use get_wind_pp_types() to see a list of all possible wind converters.

h_hub¶: float – Height of the hub of the wind turbine.

d_rotor¶: float – Diameter of the rotor.

cp_values¶: pandas.DataFrame – The index should be the wind speed and a column should be named ‘cp’.

nominal_power¶: float – The nominal output of the wind power plant.

Examples

>>> from windpowerlib import basicmodel
>>> enerconE126 = {
...    'h_hub': 135,
...    'd_rotor': 127,
...    'wind_conv_type': 'ENERCON E 126 7500'}
>>> e126 = basicmodel.SimpleWindTurbine(**enerconE126)
>>> print(e126.d_rotor)
127

cp_series(v_wind)[source]¶

Interpolates the cp value as a function of the wind velocity between data obtained from the power curve of the specified wind turbine type.

Parameters:	v_wind (pandas.Series or numpy.array) – Wind speed at hub height [m/s]
Returns:	numpy.array – cp values, wind converter type, installed capacity >>> import numpy >>> from windpowerlib import basicmodel >>> e126 = basicmodel.SimpleWindTurbine(‘ENERCON E 126 7500’) >>> v_wind = numpy.array([1,2,3,4,5,6,7,8]) >>> print(e126.cp_series(v_wind)) [ 0. 0. 0.191 0.352 0.423 0.453 0.47 0.478]

fetch_wpp_data(**kwargs)[source]¶

Fetch data of the requested wind converter.

Returns:	cp values and the nominal power of the requested wind converter
Return type:	tuple with pandas.DataFrame and float

Examples

>>> from windpowerlib import basicmodel
>>> e126 = basicmodel.SimpleWindTurbine('ENERCON E 126 7500')
>>> print(e126.cp_values.cp[5.0])
0.423
>>> print(e126.nominal_power)
7500000.0

rho_hub(weather, data_height)[source]¶

Calculates the density of air in kg/m³ at hub height.: (temperature in K, height in m, pressure in Pa)

Parameters:	weather (DataFrame or Dictionary) – Containing columns or keys with the timeseries for Temperature (temp_air) and pressure (pressure). data_height (DataFrame or Dictionary) – Containing columns or keys with the height of the measurement or model data for temperature (temp_air) and pressure (pressure).
Returns:	density of air in kg/m³ at hub height
Return type:	float

Notes

Assumptions:

Temperature gradient of -6.5 K/km
Pressure gradient of -1/8 hPa/m

The following equations are used [22]:

$T_{hub}=T_{air, data}-0.0065\cdot\left(h_{hub}-h_{T,data} \right)$

$p_{hub}=\left(p_{data}/100-\left(h_{hub}-h_{p,data}\right) *\frac{1}{8}\right)/\left(2.8706\cdot T_{hub}\right)$

with T: temperature [K], h: height [m], p: pressure [Pa]

ToDo: Check the equation and add references.

References

[22]	ICAO-Standardatmosphäre (ISA). http://www.deutscher-wetterdienst.de/lexikon/download.php?file=Standardatmosphaere.pdf

Welcome to the windpowerlib documentation!¶

Getting started¶

Introduction ¶

Actual Release¶

Developing Version¶

Installation ¶

Using the windpowerlib¶

Developing the Windpowerlib¶

Optional Packages¶

Example ¶

Basic Usage ¶

Warning¶

1. Initialise your Turbine or Module¶

2. Get your Feedin Time Series¶

What’s New¶

v0.0.4 ()¶

New features¶

Documentation¶

Testing¶

Bug fixes¶

Other changes¶

Contributors¶

v0.0.3 (November 18, 2016)¶

Other changes¶

Contributors¶

Comment¶

v0.0.1 (August 29, 2016)¶

Contributors¶

Model Description¶

Wind Model¶

Weather Data¶

windpowerlib¶

windpowerlib package¶

Submodules¶

windpowerlib.basicmodel module¶

Module contents¶

Indices and tables¶

Welcome to the windpowerlib documentation!¶

Getting started¶

Introduction¶

Actual Release¶

Developing Version¶

Installation¶

Using the windpowerlib¶

Developing the Windpowerlib¶

Optional Packages¶

Example¶

Basic Usage¶

Warning¶

1. Initialise your Turbine or Module¶

2. Get your Feedin Time Series¶

What’s New¶

v0.0.4 ()¶

New features¶

Documentation¶

Testing¶

Bug fixes¶

Other changes¶

Contributors¶

v0.0.3 (November 18, 2016)¶

Other changes¶

Contributors¶

Comment¶

v0.0.1 (August 29, 2016)¶

Contributors¶

Model Description¶

Wind Model¶

Weather Data¶

windpowerlib¶

windpowerlib package¶

Submodules¶

windpowerlib.basicmodel module¶

Module contents¶

Indices and tables¶

Introduction ¶

Installation ¶

Example ¶

Basic Usage ¶