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 8.241440 8.003992 8.917611 274.826147
1 1 8.843000 7.739532 9.320591 277.036990
2 2 8.178345 8.148292 8.696230 271.217757
3 3 8.820021 7.692683 8.549292 278.452988
4 4 8.266727 7.585501 9.297603 278.281031
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 272.967518 274.680450 559.769608 512.766815 709.162299
1 273.000244 272.631223 691.510682 463.600712 809.711510
2 272.138165 275.963550 547.011191 541.002999 657.647312
3 270.806260 277.343966 686.133917 455.232820 624.871052
4 273.595365 274.750421 564.938090 436.468373 803.735191
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 8.24144 8.003992 8.917611 274.826147 272.967518 274.680450
1 1 8.84300 7.739532 9.320591 277.036990 273.000244 272.631223
pow_000 pow_001 pow_002
0 559.769608 512.766815 709.162299
1 691.510682 463.600712 809.711510
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.24144 8.003992 8.917611 274.826147
1 1 8.84300 7.739532 9.320591 277.036990
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 272.967518 274.680450 559.769608 512.766815 709.162299
1 273.000244 272.631223 691.510682 463.600712 809.711510
FlascDataFrame in FLASC format
time ws_000 ws_001 ws_002 wd_000 wd_001 wd_002 \
0 0 8.24144 8.003992 8.917611 274.826147 272.967518 274.680450
1 1 8.84300 7.739532 9.320591 277.036990 273.000244 272.631223
pow_000 pow_001 pow_002
0 559.769608 512.766815 709.162299
1 691.510682 463.600712 809.711510
FlascDataFrame in user (wide) format
time wind_speed_TB01 wind_speed_TB02 wind_speed_TB03 wind_dir_TB01 \
0 0 8.24144 8.003992 8.917611 274.826147
1 1 8.84300 7.739532 9.320591 277.036990
wind_dir_TB02 wind_dir_TB03 power_TB01 power_TB02 power_TB03
0 272.967518 274.680450 559.769608 512.766815 709.162299
1 273.000244 272.631223 691.510682 463.600712 809.711510
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 8.241440
1 1 wind_speed_TB01 8.843000
2 2 wind_speed_TB01 8.178345
3 3 wind_speed_TB01 8.820021
4 4 wind_speed_TB01 8.266727
.. ... ... ...
175 15 power_TB03 635.212886
176 16 power_TB03 733.144229
177 17 power_TB03 855.636821
178 18 power_TB03 621.397147
179 19 power_TB03 796.757871
[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 8.241440
1 1 wind_speed_TB01 8.843000
2 2 wind_speed_TB01 8.178345
3 3 wind_speed_TB01 8.820021
4 4 wind_speed_TB01 8.266727
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 0 559.769608 512.766815 709.162299 274.826147 272.967518
1 1 691.510682 463.600712 809.711510 277.036990 273.000244
wd_002 ws_000 ws_001 ws_002
0 274.680450 8.24144 8.003992 8.917611
1 272.631223 8.84300 7.739532 9.320591
FlascDataFrame in user (long) format
time channel value
0 0 power_TB01 559.769608
1 0 power_TB02 512.766815
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
0 559.769608 274.826147
1 691.510682 277.036990
2 547.011191 271.217757
3 686.133917 278.452988
4 564.938090 278.281031
raw_WindSpeedMean TurbineName PitchAngleMean \
TimeStamp_StartFormat
0 8.241440 000 0
1 8.843000 000 0
2 8.178345 000 0
3 8.820021 000 0
4 8.266727 000 0
GenRpmMean raw_ShutdownDuration ActivePowerMean \
TimeStamp_StartFormat
0 1000 0 559.769608
1 1000 0 691.510682
2 1000 0 547.011191
3 1000 0 686.133917
4 1000 0 564.938090
WindSpeedMean YawAngleMean ShutdownDuration
TimeStamp_StartFormat
0 8.241440 274.826147 0
1 8.843000 277.036990 0
2 8.178345 271.217757 0
3 8.820021 278.452988 0
4 8.266727 278.281031 0