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)

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 (string) – Defines the method for talking wake losses within the farm into consideration. Options: None, ‘power_efficiency_curve’ or ‘constant_efficiency’ or the name of a wind efficiency curve like ‘dena_mean’. Default: ‘dena_mean’. Use get_wind_efficiency_curve() for all provided wind efficiency curves.
• smoothing (boolean) – If True the power curves will be smoothed before or after the aggregation of power curves depending on smoothing_order. Default: False.
• block_width (float) – Width between the wind speeds in the sum of the equation in smooth_power_curve(). Default: 0.5.
• standard_deviation_method (string) – Method for calculating the standard deviation for the Gauss distribution. Options: ‘turbulence_intensity’, ‘Staffell_Pfenninger’. Default: ‘turbulence_intensity’.
• smoothing_order (string) – Defines when the smoothing takes place if smoothing is True. Options: ‘turbine_power_curves’ (to the single turbine power curves), ‘wind_farm_power_curves’. Default: ‘wind_farm_power_curves’.

Other Parameters:  

• wind_speed_model (string) – Parameter to define which model to use to calculate the wind speed at hub height. Valid options are ‘logarithmic’, ‘hellman’ and ‘interpolation_extrapolation’.
• temperature_model (string) – Parameter to define which model to use to calculate the temperature of air at hub height. Valid options are ‘linear_gradient’ and ‘interpolation_extrapolation’.
• density_model (string) – Parameter to define which model to use to calculate the density of air at hub height. Valid options are ‘barometric’, ‘ideal_gas’ and ‘interpolation_extrapolation’.
• power_output_model (string) – Parameter to define which model to use to calculate the turbine power output. Valid options are ‘power_curve’ and ‘power_coefficient_curve’.
• density_correction (boolean) – If the parameter is True the density corrected power curve is used for the calculation of the turbine power output.
• obstacle_height (float) – Height of obstacles in the surrounding area of the wind turbine in m. Set obstacle_height to zero for wide spread obstacles.
• hellman_exp (float) – The Hellman exponent, which combines the increase in wind speed due to stability of atmospheric conditions and surface roughness into one constant.

power_plant

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

wake_losses_model

string – Defines the method for talking wake losses within the farm into consideration. Options: None, ‘power_efficiency_curve’ or ‘constant_efficiency’ or the name of a wind efficiency curve like ‘dena_mean’. Default: ‘dena_mean’. Use get_wind_efficiency_curve() for all provided wind efficiency curves.

smoothing

boolean – If True the power curves will be smoothed before or after the aggregation of power curves depending on smoothing_order. Default: False.

block_width

float – Width between the wind speeds in the sum of the equation in smooth_power_curve(). Default: 0.5.

standard_deviation_method

string – Method for calculating the standard deviation for the Gauss distribution. Options: ‘turbulence_intensity’, ‘Staffell_Pfenninger’. Default: ‘turbulence_intensity’.

smoothing_order

string – Defines when the smoothing takes place if smoothing is True. Options: ‘turbine_power_curves’ (to the single turbine power curves), ‘wind_farm_power_curves’. Default: ‘wind_farm_power_curves’.

power_output

pandas.Series – Electrical power output of the wind turbine in W.

pandas.DataFrame or None

The calculated power curve of the wind farm.

wind_speed_model

string – Parameter to define which model to use to calculate the wind speed at hub height. Valid options are ‘logarithmic’, ‘hellman’ and ‘interpolation_extrapolation’.

temperature_model

string – Parameter to define which model to use to calculate the temperature of air at hub height. Valid options are ‘linear_gradient’ and ‘interpolation_extrapolation’.

density_model

string – Parameter to define which model to use to calculate the density of air at hub height. Valid options are ‘barometric’, ‘ideal_gas’ and ‘interpolation_extrapolation’.

power_output_model

string – Parameter to define which model to use to calculate the turbine power output. Valid options are ‘power_curve’ and ‘power_coefficient_curve’.

density_correction

boolean – If the parameter is True the density corrected power curve is used for the calculation of the turbine power output.

obstacle_height

float – Height of obstacles in the surrounding area of the wind turbine in m. Set obstacle_height to zero for wide spread obstacles.

hellman_exp

float – The Hellman exponent, which combines the increase in wind speed due to stability of atmospheric conditions and surface roughness into one constant.

__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)

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.