def __init__(self, config):
dateConverter = lambda x: datetime.datetime.strptime(x, config.dateFormat)
self.windSpeedBin = "WindSpeedBin"
self.windSpeedBins = binning.Bins(1.0, 1, 30.0)
self.aggregations = binning.Aggregations(minimumCount=1)
dataFrame = pd.read_csv(config.inputTimeSeriesPath, index_col=config.timeStamp, parse_dates = True, date_parser = dateConverter, sep = '\t', skiprows = config.headerRows).replace(config.badData, np.nan)
dataFrame[self.windSpeedBin] = dataFrame[config.inputHubWindSpeed].map(self.windSpeedBins.binCenter)
powers = dataFrame[config.actualPower].groupby(dataFrame[self.windSpeedBin]).aggregate(self.aggregations.average)
stdErrorPowers = dataFrame[config.actualPower].groupby(dataFrame[self.windSpeedBin]).aggregate(self.aggregations.standardError)
catBPowerUncertainty = {}
uPdyn_Applied = True
uPVT_Applied = True
catBPowerUncertainty = {}
for windSpeed in self.windSpeedBins.centers:
if windSpeed in powers:
power = powers[windSpeed]
catBPowerUncertainty[windSpeed] = CategoryBPowerUncertainty(power, uPdyn_Applied = uPdyn_Applied, uPVT_Applied = uPVT_Applied)
print catBPowerUncertainty[windSpeed].catBPowerUncertainty
def __init__(self, config, rotorGeometry, analysisConfig):
self.relativePath = configuration.RelativePath(config.path)
self.nameColumn = "Dataset Name"
self.name = config.name
self.timeStepInSeconds = config.timeStepInSeconds
self.timeStamp = config.timeStamp
self.actualPower = "Actual Power"
self.hasAllPowers = None not in (config.powerMin, config.powerMax,
config.powerSD)
self.powerMin = "Power Min"
self.powerMax = "Power Max"
self.powerSD = "Power SD"
self.hubWindSpeed = "Hub Wind Speed"
self.hubTurbulence = "Hub Turbulence"
self.hubDensity = "Hub Density"
self.shearExponent = "Shear Exponent"
self.referenceShearExponent = "Reference Shear Exponent"
self.turbineShearExponent = "Turbine Shear Exponent"
self.windDirection = "Wind Direction"
self.profileRotorWindSpeed = "Profile Rotor Wind Speed"
self.profileHubWindSpeed = "Profile Hub Wind Speed"
self.profileHubToRotorRatio = "Hub to Rotor Ratio"
self.profileHubToRotorDeviation = "Hub to Rotor Deviation"
self.residualWindSpeed = "Residual Wind Speed"
self.hasShear = len(config.shearMeasurements) > 1
self.hasDirection = config.referenceWindDirection not in (None, '')
self.shearCalibration = "TurbineLocation" in config.shearMeasurements.keys(
) and "ReferenceLocation" in config.shearMeasurements.keys()
self.hubWindSpeedForTurbulence = self.hubWindSpeed if config.turbulenceWSsource != 'Reference' else config.referenceWindSpeed
self.turbRenormActive = analysisConfig.turbRenormActive
self.turbulencePower = 'Turbulence Power'
self.rewsDefined = config.rewsDefined
self.sensitivityDataColumns = config.sensitivityDataColumns
dateConverter = lambda x: datetime.datetime.strptime(
x, config.dateFormat)
dataFrame = pd.read_csv(self.relativePath.convertToAbsolutePath(config.inputTimeSeriesPath), index_col=config.timeStamp, \
parse_dates = True, date_parser = dateConverter, sep = getSeparatorValue(config.separator), \
skiprows = config.headerRows).replace(config.badData, np.nan)
if config.startDate != None and config.endDate != None:
dataFrame = dataFrame[config.startDate:config.endDate]
elif config.startDate != None:
dataFrame = dataFrame[config.startDate:]
elif config.endDate != None:
dataFrame = dataFrame[:config.endDate]
dataFrame[self.nameColumn] = config.name
dataFrame[self.timeStamp] = dataFrame.index
if self.hasDirection:
dataFrame[self.windDirection] = dataFrame[
config.referenceWindDirection]
if self.hasShear:
if not self.shearCalibration:
dataFrame[self.shearExponent] = dataFrame.apply(
ShearExponentCalculator(
config.shearMeasurements).shearExponent,
axis=1)
else:
dataFrame[self.turbineShearExponent] = dataFrame.apply(
ShearExponentCalculator(
config.shearMeasurements["TurbineLocation"]).
shearExponent,
axis=1)
dataFrame[self.referenceShearExponent] = dataFrame.apply(
ShearExponentCalculator(
config.shearMeasurements["ReferenceLocation"]).
shearExponent,
axis=1)
dataFrame[self.shearExponent] = dataFrame[
self.referenceShearExponent]
dataFrame[self.residualWindSpeed] = 0.0
if config.calculateHubWindSpeed:
if dataFrame[config.referenceWindSpeed].count() < 1:
raise Exception(
"Reference wind speed column is empty: cannot apply calibration"
)
if dataFrame[config.referenceWindDirection].count() < 1:
raise Exception(
"Reference wind direction column is empty: cannot apply calibration"
)
self.calibrationCalculator = self.createCalibration(
dataFrame, config, config.timeStepInSeconds)
dataFrame[self.hubWindSpeed] = dataFrame.apply(
self.calibrationCalculator.turbineValue, axis=1)
if dataFrame[self.hubWindSpeed].count() < 1:
raise Exception(
"Hub wind speed column is empty after application of calibration"
)
if (config.hubTurbulence != ''):
dataFrame[self.hubTurbulence] = dataFrame[config.hubTurbulence]
else:
dataFrame[self.hubTurbulence] = dataFrame[
config.referenceWindSpeedStdDev] / dataFrame[
self.hubWindSpeedForTurbulence]
if config.calibrationMethod != "Specified":
dataFrame[self.residualWindSpeed] = (
dataFrame[self.hubWindSpeed] -
dataFrame[config.turbineLocationWindSpeed]
) / dataFrame[self.hubWindSpeed]
windSpeedBin = "Wind Speed Bin"
turbulenceBin = "Turbulence Bin"
windSpeedBins = binning.Bins(analysisConfig.powerCurveFirstBin,
analysisConfig.powerCurveBinSize,
analysisConfig.powerCurveLastBin)
turbulenceBins = binning.Bins(
0.01, 0.01 / windSpeedBins.numberOfBins, 0.02)
aggregations = binning.Aggregations(
analysisConfig.powerCurveMinimumCount)
dataFrame[windSpeedBin] = dataFrame[self.hubWindSpeed].map(
windSpeedBins.binCenter)
dataFrame[turbulenceBin] = dataFrame[self.hubTurbulence].map(
turbulenceBins.binCenter)
self.residualWindSpeedMatrix = DeviationMatrix(
dataFrame[self.residualWindSpeed].groupby([
dataFrame[windSpeedBin], dataFrame[turbulenceBin]
]).aggregate(aggregations.average),
dataFrame[self.residualWindSpeed].groupby(
[dataFrame[windSpeedBin],
dataFrame[turbulenceBin]]).count())
else:
self.residualWindSpeedMatrix = None
else:
dataFrame[self.hubWindSpeed] = dataFrame[config.hubWindSpeed]
if (config.hubTurbulence != ''):
dataFrame[self.hubTurbulence] = dataFrame[config.hubTurbulence]
else:
dataFrame[self.hubTurbulence] = dataFrame[
config.referenceWindSpeedStdDev] / dataFrame[
self.hubWindSpeedForTurbulence]
self.residualWindSpeedMatrix = None
if self.shearCalibration and config.shearCalibrationMethod != "Reference":
self.shearCalibrationCalculator = self.createShearCalibration(
dataFrame, config, config.timeStepInSeconds)
dataFrame[self.shearExponent] = dataFrame.apply(
self.shearCalibrationCalculator.turbineValue, axis=1)
if config.calculateDensity:
dataFrame[self.hubDensity] = 100.0 * dataFrame[config.pressure] / (
273.15 + dataFrame[config.temperature]) / 287.058
self.hasDensity = True
else:
if config.density != None:
dataFrame[self.hubDensity] = dataFrame[config.density]
self.hasDensity = True
else:
self.hasDensity = False
if config.power != None:
dataFrame[self.actualPower] = dataFrame[config.power]
self.hasActualPower = True
else:
self.hasActualPower = False
if self.hasAllPowers:
dataFrame[self.powerMin] = dataFrame[config.powerMin]
dataFrame[self.powerMax] = dataFrame[config.powerMax]
dataFrame[self.powerSD] = dataFrame[config.powerSD]
dataFrame = self.filterDataFrame(dataFrame, config.filters)
dataFrame = self.excludeData(dataFrame, config)
if self.rewsDefined:
dataFrame = self.defineREWS(dataFrame, config, rotorGeometry)
self.fullDataFrame = dataFrame.copy()
self.dataFrame = self.extractColumns(dataFrame).dropna()
if self.windDirection in self.dataFrame.columns:
self.analysedDirections = (
round(self.fullDataFrame[self.windDirection].min() +
config.referenceWindDirectionOffset),
round(self.fullDataFrame[self.windDirection].max() +
config.referenceWindDirectionOffset))
def calculate_hub_wind_speed(self, config, analysisConfig, dataFrame):
self.verify_column_datatype(dataFrame, config.referenceWindSpeed)
dataFrame[self.referenceWindSpeed] = dataFrame[
config.referenceWindSpeed]
if config.turbineLocationWindSpeed not in ('', None):
dataFrame[self.turbineLocationWindSpeed] = dataFrame[
config.turbineLocationWindSpeed]
if dataFrame[config.referenceWindSpeed].count() < 1:
raise Exception(
"Reference wind speed column is empty: cannot apply calibration"
)
if dataFrame[config.referenceWindDirection].count() < 1:
raise Exception(
"Reference wind direction column is empty: cannot apply calibration"
)
self.calibrationCalculator = self.createCalibration(
dataFrame, config, config.timeStepInSeconds)
dataFrame[self.hubWindSpeed] = dataFrame.apply(
self.calibrationCalculator.turbineValue, axis=1)
if dataFrame[self.hubWindSpeed].count() < 1:
raise Exception(
"Hub wind speed column is empty after application of calibration"
)
if (config.hubTurbulence != ''):
dataFrame[self.hubTurbulence] = dataFrame[config.hubTurbulence]
else:
dataFrame[self.hubTurbulence] = dataFrame[
config.referenceWindSpeedStdDev] / dataFrame[
self.hubWindSpeedForTurbulence]
if config.calibrationMethod != "Specified":
dataFrame[self.residualWindSpeed] = (
dataFrame[self.hubWindSpeed] -
dataFrame[config.turbineLocationWindSpeed]
) / dataFrame[self.hubWindSpeed]
dimensions = []
windSpeedBin = "Wind Speed Bin"
turbulenceBin = "Turbulence Bin"
windSpeedBins = binning.Bins(analysisConfig.powerCurveFirstBin,
analysisConfig.powerCurveBinSize,
analysisConfig.powerCurveLastBin)
turbulenceBins = binning.Bins(0.01,
0.01 / windSpeedBins.numberOfBins,
0.02)
dimensions.append(
PowerDeviationMatrixDimension(self.hubWindSpeed,
windSpeedBins.centerOfFirstBin,
windSpeedBins.binWidth,
windSpeedBins.numberOfBins))
dimensions.append(
PowerDeviationMatrixDimension(self.hubTurbulence,
turbulenceBins.centerOfFirstBin,
turbulenceBins.binWidth,
turbulenceBins.numberOfBins))
aggregations = binning.Aggregations(
analysisConfig.powerCurveMinimumCount)
windSpeedBins = binning.Bins(analysisConfig.powerCurveFirstBin,
analysisConfig.powerCurveBinSize,
analysisConfig.powerCurveLastBin)
turbulenceBins = binning.Bins(0.01,
0.01 / windSpeedBins.numberOfBins,
0.02)
aggregations = binning.Aggregations(
analysisConfig.powerCurveMinimumCount)
dataFrame[windSpeedBin] = dataFrame[self.hubWindSpeed].map(
windSpeedBins.binCenter)
dataFrame[turbulenceBin] = dataFrame[self.hubTurbulence].map(
turbulenceBins.binCenter)
self.residualWindSpeedMatrix = AverageOfDeviationsMatrix(
dataFrame[self.residualWindSpeed].groupby([
dataFrame[windSpeedBin], dataFrame[turbulenceBin]
]).aggregate(aggregations.average),
dataFrame[self.residualWindSpeed].groupby(
[dataFrame[windSpeedBin],
dataFrame[turbulenceBin]]).count(), dimensions)
else:
self.residualWindSpeedMatrix = None
return dataFrame