windpowerlib.modelchain.ModelChain¶

class windpowerlib.modelchain.ModelChain(power_plant, wind_speed_model='logarithmic', temperature_model='linear_gradient', density_model='barometric', power_output_model='power_curve', density_correction=False, obstacle_height=0, hellman_exp=None, **kwargs)[source]¶

Model to determine the output of a wind turbine

The ModelChain class provides a standardized, high-level interface for all of the modeling steps necessary for calculating wind turbine power output from weather time series inputs.

Parameters:

power_plant (WindTurbine) – A WindTurbine object representing the wind turbine.
wind_speed_model (str) –
Parameter to define which model to use to calculate the wind speed at hub height. Valid options are:
- ’logarithmic’ - See logarithmic_profile() for more information. The parameter obstacle_height can be used to set the height of obstacles in the surrounding area of the wind turbine.
- ’hellman’ - See hellman() for more information.
- ’interpolation_extrapolation’ - See linear_interpolation_extrapolation() for more information.
- ’log_interpolation_extrapolation’ - See logarithmic_interpolation_extrapolation() for more information.
Default: ‘logarithmic’.
temperature_model (str) –
Parameter to define which model to use to calculate the temperature of air at hub height. Valid options are:
- ’linear_gradient’ - See linear_gradient() for more information.
- ’interpolation_extrapolation’ - See linear_interpolation_extrapolation() for more information.
Default: ‘linear_gradient’.
density_model (str) –
Parameter to define which model to use to calculate the density of air at hub height. Valid options are:
- ’barometric’ - See barometric() for more information.
- ’ideal_gas’ - See ideal_gas() for more information.
- ’interpolation_extrapolation’ - See linear_interpolation_extrapolation() for more information.
Default: ‘barometric’.
power_output_model (str) –
Parameter to define which model to use to calculate the turbine power output. Valid options are:
- ’power_curve’ - See power_curve() for more information. In order to use the density corrected power curve to calculate the power output set parameter density_correction to True.
- ’power_coefficient_curve’ - See power_coefficient_curve() for more information.
Default: ‘power_curve’.
density_correction (bool) – This parameter is only used if the parameter power_output_model is ‘power_curve’. For more information on this parameter see parameter density_correction in power_curve(). Default: False.
obstacle_height (float) – This parameter is only used if the parameter wind_speed_model is ‘logarithmic’. For more information on this parameter see parameter obstacle_height in logarithmic(). Default: 0.
hellman_exp (float) – This parameter is only used if the parameter wind_speed_model is ‘hellman’. For more information on this parameter see parameter hellman_exponent in hellman(). Default: None.

power_plant¶

A WindTurbine object representing the wind turbine.

Type:: WindTurbine

wind_speed_model¶

Defines which model is used to calculate the wind speed at hub height.

Type:: str

temperature_model¶

Defines which model is used to calculate the temperature of air at hub height.

Type:: str

density_model¶

Defines which model is used to calculate the density of air at hub height.

Type:: str

power_output_model¶

Defines which model is used to calculate the turbine power output.

Type:: str

density_correction¶

Used to set density_correction parameter in power_curve().

Type:: bool

obstacle_height¶

Used to set obstacle_height in logarithmic().

Type:: float

hellman_exp¶

Used to set hellman_exponent in hellman().

Type:: float

power_output¶

Electrical power output of the wind turbine in W.

Type:: pandas.Series

Examples

>>> from windpowerlib import modelchain
>>> from windpowerlib import wind_turbine
>>> enerconE126={
...    'hub_height': 135,
...    'rotor_diameter': 127,
...    'turbine_type': 'E-126/4200'}
>>> e126=wind_turbine.WindTurbine(**enerconE126)
>>> modelchain_data={'density_model': 'ideal_gas'}
>>> e126_mc=modelchain.ModelChain(e126, **modelchain_data)
>>> print(e126_mc.density_model)
ideal_gas

__init__(power_plant, wind_speed_model='logarithmic', temperature_model='linear_gradient', density_model='barometric', power_output_model='power_curve', density_correction=False, obstacle_height=0, hellman_exp=None, **kwargs)[source]¶

Methods

`__init__`(power_plant[, wind_speed_model, ...])
`calculate_power_output`(wind_speed_hub, ...)	Calculates the power output of the wind power plant.
`density_hub`(weather_df)	Calculates the density of air at hub height.
`run_model`(weather_df)	Runs the model.
`temperature_hub`(weather_df)	Calculates the temperature of air at hub height.
`wind_speed_hub`(weather_df)	Calculates the wind speed at hub height.