FLASC data format#
Data used by FLASC adheres to the following conventions:
timerepresents the time, preferably in UTCturbines are sequentially numbered, starting from 0, and numbers are always 3 digits long (e.g. the "8th" turbine is represented as
007)pow_000represents the power output of turbine 0ws_000represents the wind speed at turbine 0wd_000represents the wind direction at turbine 0wdrepresents the wind direction chosen for example to represent the overall inflow directionwsrepresents the wind speed chosen for example to represent the overall inflow speedpow_refrepresents the power output of the reference turbine (or average of reference turbines)pow_testrepresents the power output of the test turbine (or average of test turbines)
import pandas as pd
# This dataframe adhere's to FLASC's data formatting requirements and could be used for
# FLASC analysis
df = pd.DataFrame(
{
"time": [0, 1, 2, 3, 4, 5],
"pow_000": [100, 100, 100, 100, 100, 100],
"pow_001": [100, 100, 100, 100, 100, 100],
"ws_000": [10, 10, 10, 10, 10, 10],
"ws_001": [10, 10, 10, 10, 10, 10],
"wd_000": [270, 270, 270, 270, 270, 270],
"wd_001": [270, 270, 270, 270, 270, 270],
}
)
FlascDataFrame#
FLASC has historically used a pandas.DataFrame to store the data to be processed, as demonstrated above. Beginning in version 2.1, the FlascDataFrame class was introduced to provide additional methods and functionality to the data. FlascDataFrame is a subclass of pandas.DataFrame and can be used in place of a pandas.DataFrame. The following code cells provide an overview of the FlascDataFrame class and its methods. Support is added for converting between "FLASC" style data formatting and "user" formats, to make adhering to FLASC's data formatting conventions more straightforward.
Using FlascDataFrame#
# The above pandas.DataFrame can be converted to a FlascDataFrame directly
from flasc import FlascDataFrame
fdf = FlascDataFrame(df)
print(fdf.head())
FlascDataFrame in FLASC format
time pow_000 pow_001 ws_000 ws_001 wd_000 wd_001
0 0 100 100 10 10 270 270
1 1 100 100 10 10 270 270
2 2 100 100 10 10 270 270
3 3 100 100 10 10 270 270
4 4 100 100 10 10 270 270
# The FlascDataFrame includes a few helper functions added to the base pandas dataframe.
# The following returns the number of turbines found in the dataframe.
print(fdf.n_turbines)
2
Creating a FlascDataFrame from User Data#
More value from a FlascDataFrame is obtained when using it convert back and forth between user-formatted data and Flasc Data.
import numpy as np
# Suppose the we have a 3 turbine farm with turbines names 'TB01', 'TB02', 'TB03'
# For each turbine we have power, wind speed and wind direction data
# Assume that in the native data collection system,
# the signal names for each channel are given below
N = 20 # Number of data points
# Wind speeds
wind_speed_TB01 = np.random.rand(N) + 8.0
wind_speed_TB02 = np.random.rand(N) + 7.5
wind_speed_TB03 = np.random.rand(N) + 8.5
# Wind directions
wind_dir_TB01 = 10 * np.random.rand(N) + 270.0
wind_dir_TB02 = 10 * np.random.rand(N) + 270.0
wind_dir_TB03 = 10 * np.random.rand(N) + 270.0
# Power
power_TB01 = wind_speed_TB01**3
power_TB02 = wind_speed_TB02**3
power_TB03 = wind_speed_TB03**3
# Time
time = np.arange(N)
# Create a dictrionary storing this data, which could be used to instantiate a pandas.DataFrame
# or a FlascDataFrame
data_dict = {
"time": time,
"wind_speed_TB01": wind_speed_TB01,
"wind_speed_TB02": wind_speed_TB02,
"wind_speed_TB03": wind_speed_TB03,
"wind_dir_TB01": wind_dir_TB01,
"wind_dir_TB02": wind_dir_TB02,
"wind_dir_TB03": wind_dir_TB03,
"power_TB01": power_TB01,
"power_TB02": power_TB02,
"power_TB03": power_TB03,
}
The data is currently stored using the the channel and turbine names of the user. By supplying additional metadata to the FlascDataFrame, the data can be converted to and from the FLASC format.
# Declare a channel_name_map dictionary to map the signal names to the turbine names.
# The turbine numbers when 0-indexed in FLASC format should
# align with their numbering in the FLORIS model of the same farm.
channel_name_map = {
"time": "time",
"wind_speed_TB01": "ws_000",
"wind_speed_TB02": "ws_001",
"wind_speed_TB03": "ws_002",
"wind_dir_TB01": "wd_000",
"wind_dir_TB02": "wd_001",
"wind_dir_TB03": "wd_002",
"power_TB01": "pow_000",
"power_TB02": "pow_001",
"power_TB03": "pow_002",
}
We are now in a position to instantiate a FlascDataFrame
fdf = FlascDataFrame(data_dict, channel_name_map=channel_name_map)
print(fdf.head())
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.061603 7.793448 9.033996 277.456588
1 1 8.817900 7.529401 8.679348 274.616223
2 2 8.832154 8.320152 9.400810 277.997448
3 3 8.517560 8.016745 8.666481 271.543981
4 4 8.208883 7.743552 8.761684 276.995065
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 276.613977 277.229081 523.919107 473.357117 737.292287
1 271.922201 273.302332 685.639100 426.855977 653.824646
2 277.292730 275.014780 688.969376 575.961917 830.798824
3 275.400211 271.867816 617.938903 515.221825 650.921113
4 276.511116 277.621733 553.161789 464.323419 672.609121
Converting this to the FLASC format (and back) now simply requires calling the appropriate method. This makes it convenient to work with FLASC functions (that require the data to be in FLASC format) and user-provided functions (that may require the user's formatting) within the same workflow.
# Convert now into FLASC format (as a copy)
fdf_flasc = fdf.convert_to_flasc_format()
print(fdf_flasc.head(2))
print("\n\n")
# Convert back to user format (as a copy)
fdf_user = fdf_flasc.convert_to_user_format()
print(fdf_user.head(2))
print("\n\n")
# Conversions can also happen in place, if the inplace argument is set to True
fdf.convert_to_flasc_format(inplace=True)
print(fdf.head(2))
print("\n")
fdf.convert_to_user_format(inplace=True)
print(fdf.head(2))
FlascDataFrame in FLASC format
time ws_000 ws_001 ws_002 wd_000 wd_001 wd_002 \
0 0 8.061603 7.793448 9.033996 277.456588 276.613977 277.229081
1 1 8.817900 7.529401 8.679348 274.616223 271.922201 273.302332
pow_000 pow_001 pow_002
0 523.919107 473.357117 737.292287
1 685.639100 426.855977 653.824646
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.061603 7.793448 9.033996 277.456588
1 1 8.817900 7.529401 8.679348 274.616223
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 276.613977 277.229081 523.919107 473.357117 737.292287
1 271.922201 273.302332 685.639100 426.855977 653.824646
FlascDataFrame in FLASC format
time ws_000 ws_001 ws_002 wd_000 wd_001 wd_002 \
0 0 8.061603 7.793448 9.033996 277.456588 276.613977 277.229081
1 1 8.817900 7.529401 8.679348 274.616223 271.922201 273.302332
pow_000 pow_001 pow_002
0 523.919107 473.357117 737.292287
1 685.639100 426.855977 653.824646
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.061603 7.793448 9.033996 277.456588
1 1 8.817900 7.529401 8.679348 274.616223
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 276.613977 277.229081 523.919107 473.357117 737.292287
1 271.922201 273.302332 685.639100 426.855977 653.824646
Converting Wide and Long#
FlascDataFrame also provides methods to convert between wide and long formats. FLASC's native format is always "wide", that is, each channel has its own column. But FlascDataFrame can be used to convert to a user format that is "long" where each channel is a row in the dataframe.
df = pd.DataFrame(
{
"time": time,
"wind_speed_TB01": wind_speed_TB01,
"wind_speed_TB02": wind_speed_TB02,
"wind_speed_TB03": wind_speed_TB03,
"wind_dir_TB01": wind_dir_TB01,
"wind_dir_TB02": wind_dir_TB02,
"wind_dir_TB03": wind_dir_TB03,
"power_TB01": power_TB01,
"power_TB02": power_TB02,
"power_TB03": power_TB03,
}
)
# Convert to "long" format; this is taken to be the user's desired format in this example.
df = pd.melt(df, id_vars=["time"], var_name="channel", value_name="value")
print(df)
time channel value
0 0 wind_speed_TB01 8.061603
1 1 wind_speed_TB01 8.817900
2 2 wind_speed_TB01 8.832154
3 3 wind_speed_TB01 8.517560
4 4 wind_speed_TB01 8.208883
.. ... ... ...
175 15 power_TB03 839.126419
176 16 power_TB03 797.651737
177 17 power_TB03 646.260739
178 18 power_TB03 623.388230
179 19 power_TB03 647.364554
[180 rows x 3 columns]
# This time include in the specification of the FlascDataFrame the name of the
# columns of the long data
fdf = FlascDataFrame(
df,
channel_name_map=channel_name_map,
long_data_columns={"variable_column": "channel", "value_column": "value"},
)
print(fdf.head())
FlascDataFrame in user (long) format
time channel value
0 0 wind_speed_TB01 8.061603
1 1 wind_speed_TB01 8.817900
2 2 wind_speed_TB01 8.832154
3 3 wind_speed_TB01 8.517560
4 4 wind_speed_TB01 8.208883
The data can still be converted to FLASC format (and back)
fdf_flasc = fdf.convert_to_flasc_format()
print(fdf_flasc.head(2))
print("\n\n")
fdf_user = fdf_flasc.convert_to_user_format()
print(fdf_user.head(2))
# As before, conversions can also happen in place, if the inplace argument is set to True
FlascDataFrame in FLASC format
time pow_000 pow_001 pow_002 wd_000 wd_001 \
0 0 523.919107 473.357117 737.292287 277.456588 276.613977
1 1 685.639100 426.855977 653.824646 274.616223 271.922201
wd_002 ws_000 ws_001 ws_002
0 277.229081 8.061603 7.793448 9.033996
1 273.302332 8.817900 7.529401 8.679348
FlascDataFrame in user (long) format
time channel value
0 0 power_TB01 523.919107
1 0 power_TB02 473.357117
Exporting to wind-up format#
Another use case for FlascDataFrame is to export the data into the "wind-up" format. Wind-up is an open source tool for assessing uplift provided by RES. This conversion provides a convenient way to assess the data, in the case of uplift assessment, using the wind-up tool, which is imported by FLASC. A full demonstration of the usage of the wind-up tool in FLASC is provided within the Smarteole example set.
fdf = fdf.convert_to_flasc_format()
df_windup = fdf.export_to_windup_format() # df_windup is a pandas DataFrame
print(df_windup.head())
raw_ActivePowerMean raw_YawAngleMean \
TimeStamp_StartFormat
0 523.919107 277.456588
1 685.639100 274.616223
2 688.969376 277.997448
3 617.938903 271.543981
4 553.161789 276.995065
raw_WindSpeedMean TurbineName PitchAngleMean \
TimeStamp_StartFormat
0 8.061603 000 0
1 8.817900 000 0
2 8.832154 000 0
3 8.517560 000 0
4 8.208883 000 0
GenRpmMean raw_ShutdownDuration ActivePowerMean \
TimeStamp_StartFormat
0 1000 0 523.919107
1 1000 0 685.639100
2 1000 0 688.969376
3 1000 0 617.938903
4 1000 0 553.161789
WindSpeedMean YawAngleMean ShutdownDuration
TimeStamp_StartFormat
0 8.061603 277.456588 0
1 8.817900 274.616223 0
2 8.832154 277.997448 0
3 8.517560 271.543981 0
4 8.208883 276.995065 0