windpowerlib.turbine_cluster_modelchain.TurbineClusterModelChain¶

class windpowerlib.turbine_cluster_modelchain.TurbineClusterModelChain(power_plant, wake_losses_model='dena_mean', smoothing=False, block_width=0.5, standard_deviation_method='turbulence_intensity', smoothing_order='wind_farm_power_curves', **kwargs)[source]¶

Model to determine the output of a wind farm or wind turbine cluster.

Parameters:

power_plant (WindFarm or WindTurbineCluster) – A WindFarm object representing the wind farm or a WindTurbineCluster object representing the wind turbine cluster.
wake_losses_model (str or None) –
Defines the method for taking wake losses within the farm into
consideration.
- None - Wake losses are not taken into account.
- ’wind_farm_efficiency’ - The values of the wind farm power curve(s) are reduced by the wind farm efficiency, which needs to be set in the WindFarm class. Note: The wind farm efficiency has no effect if wake_losses_model is not set to ‘wind_farm_efficiency’. See wake_losses_to_power_curve() for more information.
- ’dena_mean’ or name of other wind efficiency curve - The values of the wind speed time series are reduced by the chosen wind efficiency curve in run_model() before the power output calculations. See reduce_wind_speed() for more information. Use get_wind_efficiency_curve() to get a DataFrame of all provided wind efficiency curves and see the provided example on how to plot the wind efficiency curves.
Default: ‘dena_mean’.
smoothing (bool) –
If True the power curves will be smoothed to account for the distribution of wind speeds over space. Depending on the parameter smoothing_order the power curves are smoothed before or after aggregating wind turbine power curves to one representative power curve of the wind farm or cluster. See smooth_power_curve() for more information.

Default: False.
block_width (float) –
Width between the wind speeds in the sum of the equation in smooth_power_curve(). This parameter is only used if smoothing is True. To achieve a smooth curve without steps a value not much higher than the step width between the power curve wind speeds should be chosen.

Default: 0.5.
standard_deviation_method (str) –
Method for calculating the standard deviation for the Gauss distribution if smoothing is True.
- ’turbulence_intensity’ - See smooth_power_curve() for more information.
- ’Staffell_Pfenninger’ - See smooth_power_curve() for more information.
Default: ‘turbulence_intensity’.
smoothing_order (str) –
Defines when the smoothing takes place if smoothing is True.
- ’turbine_power_curves’ - Smoothing is applied to wind turbine power curves.
- ’wind_farm_power_curves’ - Smoothing is applied to wind farm power curves.
Default: ‘wind_farm_power_curves’.

Other Parameters:

wind_speed_model – See ModelChain for more information.
temperature_model – See ModelChain for more information.
density_model – See ModelChain for more information.
power_output_model – See ModelChain for more information.
density_correction – See ModelChain for more information.
obstacle_height – See ModelChain for more information.
hellman_exp – See ModelChain for more information.

power_plant¶

A WindFarm object representing the wind farm or a WindTurbineCluster object representing the wind turbine cluster.

Type:	`WindFarm` or `WindTurbineCluster`

wake_losses_model¶

Defines the method for taking wake losses within the farm into consideration.

Type:	str or None

smoothing¶

If True the power curves are smoothed.

Type:	bool

block_width¶

Width between the wind speeds in the sum of the equation in smooth_power_curve().

Type:	float

standard_deviation_method¶

Method for calculating the standard deviation for the Gauss distribution.

Type:	str

smoothing_order¶

Defines when the smoothing takes place if smoothing is True.

Type:	str

power_output¶

Electrical power output of the wind turbine in W.

Type:	pandas.Series

power_curve¶

The calculated power curve of the wind farm.

Type:	pandas.Dataframe or None

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

__init__(power_plant, wake_losses_model='dena_mean', smoothing=False, block_width=0.5, standard_deviation_method='turbulence_intensity', smoothing_order='wind_farm_power_curves', **kwargs)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(power_plant[, wake_losses_model, …])	Initialize self.
`assign_power_curve`(weather_df)	Calculates the power curve of the wind turbine cluster.
`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.