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power_profile

Functions:

  • alpha_from_levels –

    Obtains the scaling factor given two wind speeds measured at two different known heights.

  • apply_power_profile_projection –

    Estimates wind speed values at target height based on another measured wind speed at a known height subject to a power-law scaling factor.

alpha_from_levels 

alpha_from_levels(
    low_wind_speed, low_height, high_wind_speed, high_height
)

Obtains the scaling factor given two wind speeds measured at two different known heights.

Parameters:

  • low_wind_speed

    (numeric or array_like) –

    The measured windspeed at the 'lower height'

  • low_height

    (numeric or array_like) –

    The measured height at the 'lower height'

  • high_wind_speed

    (numeric or array_like) –

    The measured windspeed at the 'higher height'

  • high_height

    (numeric or array_like) –

    The measured height at the 'higher height'

Returns:

  • numeric or array - like –

    The corresponding scaling factor The output dimensionality follows the broadcasting rules of Numpy

Notes 
The projection of wind speed values at a given height using the returned scaling factors can be computed using the following function:
        apply_power_profile_projection(measured_wind_speed, measured_height, target_height, alpha)
Source code in reskit/wind/core/power_profile.py 
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def alpha_from_levels(low_wind_speed, low_height, high_wind_speed, high_height):
    """
    Obtains the scaling factor given two wind speeds measured at two different known heights.

    Parameters
    ----------
    low_wind_speed : numeric or array_like
        The measured windspeed at the 'lower height'

    low_height : numeric or array_like
        The measured height at the 'lower height'

    high_wind_speed : numeric or array_like
        The measured windspeed at the 'higher height'

    high_height : numeric or array_like
        The measured height at the 'higher height'

    Returns
    -------
    numeric or array-like
        The corresponding scaling factor
        The output dimensionality follows the broadcasting rules of Numpy

    Notes
    -----
        The projection of wind speed values at a given height using the returned scaling factors can be computed using the following function:
                apply_power_profile_projection(measured_wind_speed, measured_height, target_height, alpha)
    """
    return np.log(low_wind_speed / high_wind_speed) / np.log(low_height / high_height)

apply_power_profile_projection 

apply_power_profile_projection(
    measured_wind_speed,
    measured_height,
    target_height,
    alpha=1 / 7,
)

Estimates wind speed values at target height based on another measured wind speed at a known height subject to a power-law scaling factor.

Parameters:

  • measured_wind_speed

    (array_like) –

    The raw wind speeds to be adjusted. If a single dimension array is given, it is assumed to represent timeseries values for a single location If a multidimensional array is given, the assumed dimensional context is (time, locations), and 'targetLoc' must be an iterable with the same length as the 'locations' dimension

    measured_height : array_like The measurement height of the raw windspeeds. Must either be a single value, or an array of values with the same length as the "locations" dimension of measured_wind_speed

    target_height : numeric or array_like The (hub) height to project each wind speed timeseries to Must either be a single value, or an array of values with the same length as the "locations" dimension of measured_wind_speed

    alpha : numeric or array_like, optional The scaling factor used to project each wind speed timeseries, by default 1/7 Must either be a single value, or an array of values with the same length as the "locations" dimension of measured_wind_speed

Notes 
The default scaling factor, alpha, equal to 1/7 corresponds to neutral stability conditions.
Alpha values can also be computed using the following function:
        alpha_from_levels(low_wind_speed, low_height, high_wind_speed, high_height)

Returns:

  • array_like –

    projected wind speeds with the same dimensions as measured_wind_speed

See Also 
apply_logarithmic_profile_projection(wind speeds, measured height, target height, roughness)
Source code in reskit/wind/core/power_profile.py 
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def apply_power_profile_projection(measured_wind_speed, measured_height, target_height, alpha=1 / 7):
    """
    Estimates wind speed values at target height based on another measured wind speed at a known height subject to a power-law scaling factor.

    Parameters
    ----------
    measured_wind_speed : array_like
        The raw wind speeds to be adjusted.
        If a single dimension array is given, it is assumed to represent timeseries values for a single location
        If a multidimensional array is given, the assumed dimensional context is (time, locations), and 'targetLoc' must be an iterable with the same length as the 'locations' dimension

        measured_height : array_like
        The measurement height of the raw windspeeds.
        Must either be a single value, or an array of values with the same length as the "locations" dimension of `measured_wind_speed`

        target_height : numeric or array_like
        The (hub) height to project each wind speed timeseries to
        Must either be a single value, or an array of values with the same length as the "locations" dimension of `measured_wind_speed`

        alpha : numeric or array_like, optional
        The scaling factor used to project each wind speed timeseries, by default 1/7
        Must either be a single value, or an array of values with the same length as the "locations" dimension of `measured_wind_speed`

    Notes
    -----
        The default scaling factor, alpha, equal to 1/7 corresponds to neutral stability conditions.
        Alpha values can also be computed using the following function:
                alpha_from_levels(low_wind_speed, low_height, high_wind_speed, high_height)

    Returns
    -------
        array_like
        projected wind speeds with the same dimensions as `measured_wind_speed`

    See Also
    --------
        apply_logarithmic_profile_projection(wind speeds, measured height, target height, roughness)

    """
    return measured_wind_speed * np.power(target_height / measured_height, alpha)