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 8.819463 8.143603 9.143759 276.267989
1 1 8.651251 7.848077 8.855556 274.254916
2 2 8.769868 8.354315 8.738283 276.248912
3 3 8.374190 7.783613 8.626236 279.792350
4 4 8.234033 8.472874 8.617795 279.531329
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 277.721718 278.267233 686.003632 540.069719 764.494463
1 279.627606 273.665876 647.495586 483.381225 694.460360
2 276.667794 278.483416 674.495629 583.085975 667.234311
3 276.915828 273.567836 587.257344 471.567348 641.894940
4 277.227216 271.583554 558.261588 608.264229 640.012570
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 8.819463 8.143603 9.143759 276.267989 277.721718 278.267233
1 1 8.651251 7.848077 8.855556 274.254916 279.627606 273.665876
pow_000 pow_001 pow_002
0 686.003632 540.069719 764.494463
1 647.495586 483.381225 694.460360
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.819463 8.143603 9.143759 276.267989
1 1 8.651251 7.848077 8.855556 274.254916
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 277.721718 278.267233 686.003632 540.069719 764.494463
1 279.627606 273.665876 647.495586 483.381225 694.460360
FlascDataFrame in FLASC format
time ws_000 ws_001 ws_002 wd_000 wd_001 wd_002 \
0 0 8.819463 8.143603 9.143759 276.267989 277.721718 278.267233
1 1 8.651251 7.848077 8.855556 274.254916 279.627606 273.665876
pow_000 pow_001 pow_002
0 686.003632 540.069719 764.494463
1 647.495586 483.381225 694.460360
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.819463 8.143603 9.143759 276.267989
1 1 8.651251 7.848077 8.855556 274.254916
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 277.721718 278.267233 686.003632 540.069719 764.494463
1 279.627606 273.665876 647.495586 483.381225 694.460360
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 8.819463
1 1 wind_speed_TB01 8.651251
2 2 wind_speed_TB01 8.769868
3 3 wind_speed_TB01 8.374190
4 4 wind_speed_TB01 8.234033
.. ... ... ...
175 15 power_TB03 654.216809
176 16 power_TB03 793.997896
177 17 power_TB03 847.282010
178 18 power_TB03 623.109117
179 19 power_TB03 644.814703
[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 8.819463
1 1 wind_speed_TB01 8.651251
2 2 wind_speed_TB01 8.769868
3 3 wind_speed_TB01 8.374190
4 4 wind_speed_TB01 8.234033
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 0 686.003632 540.069719 764.494463 276.267989 277.721718
1 1 647.495586 483.381225 694.460360 274.254916 279.627606
wd_002 ws_000 ws_001 ws_002
0 278.267233 8.819463 8.143603 9.143759
1 273.665876 8.651251 7.848077 8.855556
FlascDataFrame in user (long) format
time channel value
0 0 power_TB01 686.003632
1 0 power_TB02 540.069719
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
0 686.003632 276.267989
1 647.495586 274.254916
2 674.495629 276.248912
3 587.257344 279.792350
4 558.261588 279.531329
raw_WindSpeedMean TurbineName PitchAngleMean \
TimeStamp_StartFormat
0 8.819463 000 0
1 8.651251 000 0
2 8.769868 000 0
3 8.374190 000 0
4 8.234033 000 0
GenRpmMean raw_ShutdownDuration ActivePowerMean \
TimeStamp_StartFormat
0 1000 0 686.003632
1 1000 0 647.495586
2 1000 0 674.495629
3 1000 0 587.257344
4 1000 0 558.261588
WindSpeedMean YawAngleMean ShutdownDuration
TimeStamp_StartFormat
0 8.819463 276.267989 0
1 8.651251 274.254916 0
2 8.769868 276.248912 0
3 8.374190 279.792350 0
4 8.234033 279.531329 0