Process SCADA and downtime data#
This notebook demonstrates the merging of all four CSV files containing SCADA and downtime data into single dataframes. Two files are older datasets, and the other two are newer datasets. Both old and new datasets have most of their timestamps in common.
The older SCADA datasets were found to have some errors in the rotor speed readings. This merging replaces the old errorred data points with the new ones, and removes incomplete rows.
This merging ensures the downtime data has the same range as the SCADA data, and removes incomplete rows.
import glob
import os
import pandas as pd
# create directory to store processed data
os.makedirs("data/processed/", exist_ok=True)
Downtime categories#
# read and view data
data = pd.read_excel("data/Melogale Downtime Categories.xlsx")
data
| Turbine Categories | Unnamed: 1 | Environmental Categories | Unnamed: 3 | Grid Categories | Unnamed: 5 | Infrastructure Categories | Unnamed: 7 | Availability Categories | Unnamed: 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | id | OLD name | id | name | id | name | id | name | id | name |
| 1 | 0 | Unknown | 0 | Unknown | 0 | Unknown | 0 | Unknown | 0 | Unknown |
| 2 | 1 | OK | 1 | OK | 1 | OK | 1 | OK | 1 | Available/Non-penalising |
| 3 | 2 | Anemometry | 2 | High Wind Shutdown | 2 | Planned Outage | 2 | Planned Outage | 2 | NOT available/penalising |
| 4 | 3 | Rotor Brake | 3 | Icing | 3 | Unplanned Outage | 3 | Unplanned Outage | NaN | NaN |
| 5 | 4 | Main Shaft | 4 | Turbulence | NaN | NaN | NaN | NaN | NaN | NaN |
| 6 | 5 | Gearbox | 5 | Work Halted | NaN | NaN | NaN | NaN | NaN | NaN |
| 7 | 6 | Generator | 6 | Lightning | NaN | NaN | NaN | NaN | NaN | NaN |
| 8 | 7 | Yaw System | 7 | Sector Management | NaN | NaN | NaN | NaN | NaN | NaN |
| 9 | 8 | Electrical Controls | 8 | Low Temperature | NaN | NaN | NaN | NaN | NaN | NaN |
| 10 | 9 | Hydraulics | 11 | Low Wind Shutdown | NaN | NaN | NaN | NaN | NaN | NaN |
| 11 | 10 | Electrical System | 12 | Shadow casting stop | NaN | NaN | NaN | NaN | NaN | NaN |
| 12 | 11 | Pitch Control | 13 | Ambient temperature high | NaN | NaN | NaN | NaN | NaN | NaN |
| 13 | 12 | Unlogged manual stop | 14 | Cable unwind | NaN | NaN | NaN | NaN | NaN | NaN |
| 14 | 13 | Customer Stop | 15 | Wildlife | NaN | NaN | NaN | NaN | NaN | NaN |
| 15 | 14 | Noise Constraints | 16 | Other | NaN | NaN | NaN | NaN | NaN | NaN |
| 16 | 15 | Scheduled Maintenance | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 17 | 16 | Tower | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 18 | 17 | Retrofit | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 19 | 18 | Cable Unwind | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 20 | 19 | Hub | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 21 | 20 | Rotor Blades | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 22 | 21 | Delayed Startup | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 23 | 22 | Other | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# drop first row
data = data.drop([0])
# function to filter data for each category type
catData = {}
def categorise_data(cat, number):
catData[cat] = data.filter(
items=[cat + " Categories", "Unnamed: " + str(number)]
)
catData[cat].rename(
columns={
cat + " Categories": "Category",
"Unnamed: " + str(number): "Name",
},
inplace=True,
)
catData[cat]["Type"] = cat
catData[cat].dropna(inplace=True)
# filtering
categorise_data("Turbine", 1)
categorise_data("Environmental", 3)
categorise_data("Grid", 5)
categorise_data("Infrastructure", 7)
categorise_data("Availability", 9)
# concatenate data
data = pd.concat(catData.values(), ignore_index=True)
data
| Category | Name | Type | |
|---|---|---|---|
| 0 | 0 | Unknown | Turbine |
| 1 | 1 | OK | Turbine |
| 2 | 2 | Anemometry | Turbine |
| 3 | 3 | Rotor Brake | Turbine |
| 4 | 4 | Main Shaft | Turbine |
| 5 | 5 | Gearbox | Turbine |
| 6 | 6 | Generator | Turbine |
| 7 | 7 | Yaw System | Turbine |
| 8 | 8 | Electrical Controls | Turbine |
| 9 | 9 | Hydraulics | Turbine |
| 10 | 10 | Electrical System | Turbine |
| 11 | 11 | Pitch Control | Turbine |
| 12 | 12 | Unlogged manual stop | Turbine |
| 13 | 13 | Customer Stop | Turbine |
| 14 | 14 | Noise Constraints | Turbine |
| 15 | 15 | Scheduled Maintenance | Turbine |
| 16 | 16 | Tower | Turbine |
| 17 | 17 | Retrofit | Turbine |
| 18 | 18 | Cable Unwind | Turbine |
| 19 | 19 | Hub | Turbine |
| 20 | 20 | Rotor Blades | Turbine |
| 21 | 21 | Delayed Startup | Turbine |
| 22 | 22 | Other | Turbine |
| 23 | 0 | Unknown | Environmental |
| 24 | 1 | OK | Environmental |
| 25 | 2 | High Wind Shutdown | Environmental |
| 26 | 3 | Icing | Environmental |
| 27 | 4 | Turbulence | Environmental |
| 28 | 5 | Work Halted | Environmental |
| 29 | 6 | Lightning | Environmental |
| 30 | 7 | Sector Management | Environmental |
| 31 | 8 | Low Temperature | Environmental |
| 32 | 11 | Low Wind Shutdown | Environmental |
| 33 | 12 | Shadow casting stop | Environmental |
| 34 | 13 | Ambient temperature high | Environmental |
| 35 | 14 | Cable unwind | Environmental |
| 36 | 15 | Wildlife | Environmental |
| 37 | 16 | Other | Environmental |
| 38 | 0 | Unknown | Grid |
| 39 | 1 | OK | Grid |
| 40 | 2 | Planned Outage | Grid |
| 41 | 3 | Unplanned Outage | Grid |
| 42 | 0 | Unknown | Infrastructure |
| 43 | 1 | OK | Infrastructure |
| 44 | 2 | Planned Outage | Infrastructure |
| 45 | 3 | Unplanned Outage | Infrastructure |
| 46 | 0 | Unknown | Availability |
| 47 | 1 | Available/Non-penalising | Availability |
| 48 | 2 | NOT available/penalising | Availability |
Downtime time series#
dt = {}
dtList = glob.glob("data/*downtime*.csv")
for num, df in enumerate(dtList):
dt[num] = pd.read_csv(df)
print(num, df, dt[num].shape)
0 data/Last_six_months_downtime.csv (5367, 12)
1 data/Prior_two_years_downtime.csv (16958, 24)
dt[0].head(5)
| id | timestamp_start | timestamp_end | turbine_id | alarm_id | GridCategory_id | InfrastructureCategory_id | EnvironmentalCategory_id | TurbineCategory_id | AvailabilityCategory_id | comment | workorder_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 715463 | 2016-10-05 11:27:58.000 | 2016-10-05 11:28:37.000 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 1 | 715464 | 2016-10-05 11:28:37.000 | 2016-10-05 11:58:15.000 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 2 | 715465 | 2016-10-05 11:58:15.000 | 2016-10-05 12:00:04.000 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 3 | 715466 | 2016-10-05 12:00:50.000 | 2016-10-05 12:48:00.000 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 4 | 715467 | 2016-10-26 04:56:44.000 | 2016-10-26 05:20:00.000 | 18 | 391 | 1 | 1 | 1 | 18 | 1 | FM1003Yaw CableRewind | NaN |
dt[1].head(5)
| id | timestamp_start | timestamp_end | turbine_id | alarm_id | GridCategory_id | InfrastructureCategory_id | EnvironmentalCategory_id | TurbineCategory_id | AvailabilityCategory_id | ... | timestamp_end.1 | turbine_id.1 | alarm_id.1 | GridCategory_id.1 | InfrastructureCategory_id.1 | EnvironmentalCategory_id.1 | TurbineCategory_id.1 | AvailabilityCategory_id.1 | comment.1 | workorder_id.1 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 85851 | 2015-01-09 01:31:23.000 | 2015-01-09 01:42:07.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | ... | 2015-01-09 01:42:07.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | NaN | NaN |
| 1 | 85852 | 2015-01-09 02:01:28.000 | 2015-01-09 02:06:43.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | ... | 2015-01-09 02:06:43.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | NaN | NaN |
| 2 | 85853 | 2015-01-09 03:08:50.000 | 2015-01-09 03:20:00.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | ... | 2015-01-09 03:20:00.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | NaN | NaN |
| 3 | 85854 | 2015-01-09 04:21:58.000 | 2015-01-09 05:30:00.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | ... | 2015-01-09 05:30:00.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | NaN | NaN |
| 4 | 85855 | 2015-01-09 05:43:41.000 | 2015-01-09 05:54:57.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | ... | 2015-01-09 05:54:57.000 | 6 | 389 | 1 | 1 | 2 | 1 | 1 | NaN | NaN |
5 rows × 24 columns
dt[1].columns
Index(['id', 'timestamp_start', 'timestamp_end', 'turbine_id', 'alarm_id',
'GridCategory_id', 'InfrastructureCategory_id',
'EnvironmentalCategory_id', 'TurbineCategory_id',
'AvailabilityCategory_id', 'comment', 'workorder_id', 'id.1',
'timestamp_start.1', 'timestamp_end.1', 'turbine_id.1', 'alarm_id.1',
'GridCategory_id.1', 'InfrastructureCategory_id.1',
'EnvironmentalCategory_id.1', 'TurbineCategory_id.1',
'AvailabilityCategory_id.1', 'comment.1', 'workorder_id.1'],
dtype='object')
# drop duplicate columns
dt[1].drop(columns=list(dt[1].filter(regex=".1")), inplace=True)
# convert timestamps to datetime data type
for key in dt.keys():
for col in list(dt[key].filter(regex="timestamp")):
dt[key][col] = pd.to_datetime(dt[key][col])
# concatenate data
data = pd.concat(dt.values(), join="outer")
data.head(5)
| id | timestamp_start | timestamp_end | turbine_id | alarm_id | GridCategory_id | InfrastructureCategory_id | EnvironmentalCategory_id | TurbineCategory_id | AvailabilityCategory_id | comment | workorder_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 715463 | 2016-10-05 11:27:58 | 2016-10-05 11:28:37 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 1 | 715464 | 2016-10-05 11:28:37 | 2016-10-05 11:58:15 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 2 | 715465 | 2016-10-05 11:58:15 | 2016-10-05 12:00:04 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 3 | 715466 | 2016-10-05 12:00:50 | 2016-10-05 12:48:00 | 18 | 5 | 1 | 1 | 1 | 13 | 1 | Advanced rescue procedures | 108128.0 |
| 4 | 715467 | 2016-10-26 04:56:44 | 2016-10-26 05:20:00 | 18 | 391 | 1 | 1 | 1 | 18 | 1 | FM1003Yaw CableRewind | NaN |
data.shape
(22325, 12)
SCADA time series#
# old SCADA data
scada = {}
scadaList = glob.glob("data/*SCADA.csv")
for num, df in enumerate(scadaList):
scada[num] = pd.read_csv(df)
print(num, df, scada[num].shape)
0 data/Last_six_months_SCADA.csv (651600, 17)
1 data/Prior_two_years_SCADA.csv (2550346, 17)
scada[0].head(5)
| timestamp | turbine | ap_av | ap_dev | ap_max | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | rs_av | nac_pos | pitch | runtime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01/11/2016 15:40 | 3 | 80.37572 | 29.46033 | 178.0 | -9.292799 | 4.088211 | 4.126762 | 4.088211 | 290.4030 | -17.849820 | -4.077392 | 797.0712 | 0.0 | 294.4802 | 0.0 | 600.0 |
| 1 | 01/11/2016 15:40 | 4 | 141.68580 | 53.87635 | 242.0 | 1.339321 | 4.745911 | 5.071102 | 4.745911 | 316.6896 | -15.205220 | -6.955624 | 814.9814 | 0.0 | 323.6452 | 0.0 | 600.0 |
| 2 | 01/11/2016 15:40 | 5 | 14.85285 | 17.79933 | 91.0 | -2.037041 | 2.886049 | 0.000000 | 2.886049 | 259.8634 | 0.000000 | -2.367190 | 780.9207 | 0.0 | 262.2312 | 0.0 | 600.0 |
| 3 | 01/11/2016 15:40 | 6 | 139.71000 | 63.40798 | 273.0 | -55.802610 | 4.517820 | 4.584790 | 4.517820 | 282.0574 | -180.000000 | -6.154136 | 816.2123 | 0.0 | 288.2119 | 0.0 | 600.0 |
| 4 | 01/11/2016 15:40 | 7 | 37.40505 | 40.17854 | 161.0 | -145.587500 | 3.540881 | 3.540881 | 3.369349 | 228.9863 | -3.396181 | -4.141267 | 787.7513 | 0.0 | 232.3828 | 0.0 | 600.0 |
scada[1].head(5)
| timestamp | turbine | ap_av | ap_dev | ap_min | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | rs_av | nac_pos | pitch | runtime | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2014-11-01 02:20:00 | 8 | 204.6824 | 74.51731 | 419.0 | -38.03038 | 5.029291 | 5.029291 | 5.152810 | 204.4785 | 2.978069 | -5.033260 | 836.9521 | 12.28072 | 201.5000 | 0.0 | 600.0 |
| 1 | 2014-11-01 02:20:00 | 9 | 228.1198 | 82.34712 | 400.0 | 91.85419 | 5.506918 | 5.506918 | 5.225697 | 215.6716 | 0.671585 | -0.585945 | 845.9201 | 12.40917 | 215.0000 | 0.0 | 600.0 |
| 2 | 2014-11-01 02:20:00 | 10 | 159.3024 | 64.77182 | 302.0 | -932.91080 | 5.442306 | 5.442306 | 5.653325 | 223.2563 | 2.356911 | 0.643579 | 822.5547 | 0.00000 | 220.9000 | 0.0 | 600.0 |
| 3 | 2014-11-01 02:20:00 | 11 | 119.3055 | 31.69469 | 199.0 | -146.52910 | 4.281990 | 4.281990 | 4.095518 | 224.0360 | 0.852958 | 1.169835 | 808.5560 | 11.84853 | 223.1832 | 0.0 | 600.0 |
| 4 | 2014-11-01 02:20:00 | 12 | 168.3689 | 85.72588 | 387.0 | 77.63621 | 4.962106 | 4.962106 | 4.764067 | 240.3239 | -3.776284 | -5.501996 | 942.0538 | 13.78513 | 244.1000 | 0.0 | 600.0 |
# rename ap_min in scada[1] to ap_max
scada[1].rename(columns={"ap_min": "ap_max"}, inplace=True)
# fixing rotor speed readings due to errors in data
def fix_rs(c):
if c["turbine"] <= 20:
return c["rs_av"]
for df in scada.keys():
scada[df]["rs_av_old"] = scada[df].apply(fix_rs, axis=1)
scada[df] = scada[df].drop("rs_av", axis=1)
# concatenate old datasets
scadaOld = pd.concat(scada.values())
scadaOld.head(5)
| timestamp | turbine | ap_av | ap_dev | ap_max | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | nac_pos | pitch | runtime | rs_av_old | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01/11/2016 15:40 | 3 | 80.37572 | 29.46033 | 178.0 | -9.292799 | 4.088211 | 4.126762 | 4.088211 | 290.4030 | -17.849820 | -4.077392 | 797.0712 | 294.4802 | 0.0 | 600.0 | 0.0 |
| 1 | 01/11/2016 15:40 | 4 | 141.68580 | 53.87635 | 242.0 | 1.339321 | 4.745911 | 5.071102 | 4.745911 | 316.6896 | -15.205220 | -6.955624 | 814.9814 | 323.6452 | 0.0 | 600.0 | 0.0 |
| 2 | 01/11/2016 15:40 | 5 | 14.85285 | 17.79933 | 91.0 | -2.037041 | 2.886049 | 0.000000 | 2.886049 | 259.8634 | 0.000000 | -2.367190 | 780.9207 | 262.2312 | 0.0 | 600.0 | 0.0 |
| 3 | 01/11/2016 15:40 | 6 | 139.71000 | 63.40798 | 273.0 | -55.802610 | 4.517820 | 4.584790 | 4.517820 | 282.0574 | -180.000000 | -6.154136 | 816.2123 | 288.2119 | 0.0 | 600.0 | 0.0 |
| 4 | 01/11/2016 15:40 | 7 | 37.40505 | 40.17854 | 161.0 | -145.587500 | 3.540881 | 3.540881 | 3.369349 | 228.9863 | -3.396181 | -4.141267 | 787.7513 | 232.3828 | 0.0 | 600.0 | 0.0 |
# new SCADA data
scada = {}
scadaList = glob.glob("data/NS_SCADA*.csv")
for num, df in enumerate(scadaList):
scada[num] = pd.read_csv(df)
print(num, df, scada[num].shape)
0 data/NS_SCADA_2017_v2.csv (543425, 16)
1 data/NS_SCADA_v2.csv (3064604, 16)
scada[0].head(5)
| timestamp | turbine_id | ap_av | ap_dev | ap_max | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | rs_av | nac_pos | pitch | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2017-01-01 00:00:00.000 | 1 | 275.5676 | 79.71668 | 455 | -128.126100 | 5.950170 | 6.065521 | 5.950170 | 347.89090 | 3.216685 | 3.490916 | 868.2094 | 12.78084 | 344.40000 | 0.0 |
| 1 | 2017-01-01 00:00:00.000 | 2 | 280.9523 | 58.14750 | 433 | -141.462800 | 5.920139 | 5.920139 | 0.011563 | 125.38090 | 1.381044 | 4.931978 | 871.9911 | 12.73835 | 124.00000 | 0.0 |
| 2 | 2017-01-01 00:00:00.000 | 3 | 307.0966 | 54.00394 | 449 | -72.826970 | 6.408066 | 5.701910 | 6.408066 | 12.70238 | -9.518435 | 2.202345 | 878.9877 | 12.92154 | 10.50000 | 0.0 |
| 3 | 2017-01-01 00:00:00.000 | 4 | 348.8079 | 112.20640 | 633 | 1.584503 | 6.269817 | 6.569055 | 6.269817 | 18.58538 | -6.821929 | 2.269212 | 909.9926 | 13.39747 | 16.31618 | 0.0 |
| 4 | 2017-01-01 00:00:00.000 | 5 | 315.8827 | 60.93330 | 538 | -95.838270 | 5.848261 | 0.000000 | 5.848261 | 319.47270 | 0.000000 | 5.373057 | 882.6358 | 12.97570 | 314.10000 | 0.0 |
scada[1].head(5)
| timestamp | turbine_id | ap_av | ap_dev | ap_max | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | rs_av | nac_pos | pitch | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015-01-01 00:00:00.000 | 1 | 1478.246 | 269.6151 | 1780 | 0.181361 | 10.90739 | 10.90739 | 11.467810 | 246.6747 | 2.730545 | 4.884112 | 1142.79500 | 16.754220 | 243.9438 | 3.475755 |
| 1 | 2015-01-01 00:00:00.000 | 2 | 2237.340 | 146.2053 | 2501 | -8.899933 | 14.16283 | 14.16283 | 0.017031 | 211.1832 | 3.617529 | 9.506144 | 1155.91100 | 16.886540 | 207.5653 | 5.934744 |
| 2 | 2015-01-01 00:00:00.000 | 3 | 2076.400 | 465.8087 | 2769 | 334.474500 | 12.83834 | 12.83834 | 13.927340 | 242.8819 | 1.866124 | 9.465878 | 1141.49200 | 16.771280 | 241.0154 | 2.050259 |
| 3 | 2015-01-01 00:00:00.000 | 4 | 0.000 | 0.0000 | 0 | 0.000000 | 13.18276 | 13.18276 | 13.569020 | 164.8922 | 28.992340 | 35.905610 | 26.76113 | 0.481272 | 135.9000 | 87.000000 |
| 4 | 2015-01-01 00:00:00.000 | 5 | 1648.039 | 223.3687 | 1906 | 251.515400 | 11.84929 | 11.21665 | 11.849290 | 223.8225 | -68.455830 | 2.734230 | 1146.72100 | 16.831610 | 221.0888 | 3.055791 |
# concatenate new datasets
scadaNew = pd.concat(scada.values())
scadaNew.head(5)
| timestamp | turbine_id | ap_av | ap_dev | ap_max | reactive_power | ws_av | ws_1 | ws_2 | wd_av | wd_1 | wd_2 | gen_sp | rs_av | nac_pos | pitch | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2017-01-01 00:00:00.000 | 1 | 275.5676 | 79.71668 | 455 | -128.126100 | 5.950170 | 6.065521 | 5.950170 | 347.89090 | 3.216685 | 3.490916 | 868.2094 | 12.78084 | 344.40000 | 0.0 |
| 1 | 2017-01-01 00:00:00.000 | 2 | 280.9523 | 58.14750 | 433 | -141.462800 | 5.920139 | 5.920139 | 0.011563 | 125.38090 | 1.381044 | 4.931978 | 871.9911 | 12.73835 | 124.00000 | 0.0 |
| 2 | 2017-01-01 00:00:00.000 | 3 | 307.0966 | 54.00394 | 449 | -72.826970 | 6.408066 | 5.701910 | 6.408066 | 12.70238 | -9.518435 | 2.202345 | 878.9877 | 12.92154 | 10.50000 | 0.0 |
| 3 | 2017-01-01 00:00:00.000 | 4 | 348.8079 | 112.20640 | 633 | 1.584503 | 6.269817 | 6.569055 | 6.269817 | 18.58538 | -6.821929 | 2.269212 | 909.9926 | 13.39747 | 16.31618 | 0.0 |
| 4 | 2017-01-01 00:00:00.000 | 5 | 315.8827 | 60.93330 | 538 | -95.838270 | 5.848261 | 0.000000 | 5.848261 | 319.47270 | 0.000000 | 5.373057 | 882.6358 | 12.97570 | 314.10000 | 0.0 |