Title_Sli de Quality Controlling Wind Power Data for Data Mining Applications Gerry Wiener Research Applications Laboratory Software Engineering Assembly,

Title_Slide

Quality Controlling Wind Power Data forData Mining Applications

Gerry WienerResearch Applications Laboratory

Software Engineering Assembly, NCAR April 13, 2015

Photograph by Carlye Calvin, UCAR

Required Forecasts

Overview

• Overall goal• System• Data setting• Challenges in quality control• Interpercentile range filtering• Conversion of wind to power

Required Forecasts

Overall Goal

• Xcel Energy came to NCAR in 2009 looking for better power forecasts (not wind forecasts!)

• 57 wind farms in Colorado, Minnesota, New Mexico and Texas

• 3096 turbines• 4.25 gigawatts

• 5 kw -> 1 home• 1 mw -> 200 homes• 4.25 gw -> 850000 homes

Northern States Power

Required ForecastsPublic Service Company of Colorado

Required ForecastsSouthwestern Public Service

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Overall Goal (cont.)

• Better Power Forecasts =>• At each wind farm:

• Forecast power production every 15 min out to 3 hours

• Forecast power production every hour out to 7 days

• Forecasts should be available automatically (meteorologists over the loop)

Forecast Types•1-3 hour forecasts - anticipate upcoming “ramp” adjustments•24 hour forecasts (energy trading & planning) •3-5 day forecasts (long term trading & planning)•7 day forecasts (account for weekends & holidays)

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Overall Goal (cont.)

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System

• Data ingest modules• Numerical model(s)• Model postprocessing• Power conversion • Output formatting• Displays• Monitoring• Implementation:

• Largely Fortran/C++/Python

NCAR / Xcel Wind Forecasting System Components

■ Secure VPN networking

■ Data Archiving and Storage

DICast wind

High Resolution Model RTFDDA WRF (3 km)

30 Member EnsembleModeling System (3 km)

External, PublicallyAvailable Models

StandardMeteorologicalObservations

Wind Farm & Turbine Observations

Multi-Mode Interactive Displays

Direct Digital Data Stream

MeteorologicalWeather Maps& Products

System Monitoringand MaintenanceCapabilities

+ power modules

■ Parallel hardware and data streams for redundancy & reliability

Customized NCAR model, optimized for wind energy forecasting applications.

Meteorological + power observations for model initialization, data mining and system optimization.

Customized output for both human users and for automatic processing.

WIND POWER FORECASTS

System monitoring and maintenance.

Publicly available output from external models.

■ Robust system that compensates for missing data or connections

+




DICast wind


External, PubliclyAvailable Models

NOAA-GFSNOAA-NAMGEMECMWFHRRR







+ power modules


Meteorological + power observations for model initialization, data mining and system optimization.





+




DICast wind



External, PublicallyAvailable Models







+ power modules


Customized NCAR models, optimized for wind energy forecasting applications.

Meteorological observations for model initialization, data mining and system optimization.




Publically available output from external models.


+

DICast®

Dynamic Integrated Forecast System

A consensus point forecast system that integrates available meteorological data, including wind farm and turbine observations as well as numerical model output (from multiple models) using the DMOS linear regression-based statistical method. DMOS: Dynamic Model Output Statistics DICast® is a multi-faceted, robust, self-monitoring system “learns” as statistical weights from past performance are updated daily.

DICast®

Dynamic Integrated Forecast System

A consensus point forecast system that integrates available meteorological data, including wind farm and turbine observations as well as numerical model output (from multiple models) using the DMOS linear regression-based statistical method. DMOS: Dynamic Model Output Statistics DICast® is a multi-faceted, robust, self-monitoring system “learns” as statistical weights from past performance are updated daily.

For this system, DICast® generates wind forecasts for every wind turbine in the Xcel domain (currently 3096 turbines distributed across 57 separate wind farms).

Fifteen minute forecasts are generated for the first three hours into the future, with hourly forecasts extending out to seven days, and updated every 15 minutes.

For this system, DICast® generates wind forecasts for every wind turbine in the Xcel domain (currently 3096 turbines distributed across 57 separate wind farms).

Fifteen minute forecasts are generated for the first three hours into the future, with hourly forecasts extending out to seven days, and updated every 15 minutes.




DICast wind



NOAA-GFSNOAA-NAMNOAA-RUCGEM (global)GEM (regional)







+ power modules






+External, PubliclyAvailable Models




DICast wind







Output Digital Data Stream



+ power modules




+External, PubliclyAvailable Models




DICast wind










+ power modules




+External, PublicallyAvailable Models

AdjustableTimescale




DICast wind










+ power modules




+External, PublicallyAvailable Models

Required Forecasts

Data Setting

Most Farms Provide:• Nacelle Anemometer Wind Speed• Turbine Power• Connection Node Power

Data Format:• Ascii• Site, Time, Data Value

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Data Setting

Data Issues•Data for any given farm/connection node can go out at any time for irregular stretches of time•Data can be late•Data can be incorrect

• Stuck values• PI system issue

• Time zone and time stamp format problems

• Handle these using ad hoc QC techniques

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Challenges in Quality Control

Required ForecastsIdealized Power Curves

Required ForecastsTurbine Wind/Power Scatter Plot

Required ForecastsConnection Node Wind/Power Scatter Plot

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Challenges in Quality Control

• Statistical modeling of the relationship of wind to potential power should exclude outlier power observations for given wind speeds Turbines produce reduced power when

curtailed (transmission issue) Turbines produce reduced power when

subject to icing (weather/turbine issue) Turbines produce reduced power after a high

wind speed cutout events (weather/turbine issue)

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Interpercentile Range Filtering

Procedure:•Collect ~1 year of wind power observations at a given wind farm

• Observations are from all turbines of the same type (some farms have multiple turbine types)

•Divide the wind speed range into 0.1 m/s bins• 0-0.1• 0.1-0.2• …• 24.9-25

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Interpercentile Range Filtering

Procedure:

•Place power values from wind, power pairs in appropriate m/s wind bins•Sort power values in each wind bin•Remove power values outside interpercentile range

• Options:• Interquartile range 25% - 75%• 15% - 95%

•Use remaining data set for data mining

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Filtering adjustments

• Incorporate curtailment filtering• Curtailment information may be available• Perform filtering prior to interpercentile range filtering

• Power curve filtering• Shift the power curve sideways and up/down• This “blackens out a region”• Remove power values outside the region• Can be done prior to interpercentile range filtering

Required ForecastsInterpercentile Range Filtering



-200-100

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0 2 4 6 8 10 12 14 16 18 20

m/s

kW

Actual Manufacturer Power Curve

-200-100

0100200300400500600700800900

10001100120013001400150016001700

0 2 4 6 8 10 12 14 16 18 20

m/s

kW


-200-100

0100200300400500600700800900

10001100120013001400150016001700

0 2 4 6 8 10 12 14 16 18 20

m/s

kW

Actual Manufacturer Power Curve-200-100

0100200300400500600700800900

10001100120013001400150016001700

0 2 4 6 8 10 12 14 16 18 20

m/s

kW


Empirical Power Curve Optimization by Data Mining: An example of empirical power curves for turbines of the same model & manufacturer at different wind farms

Required Forecasts

Conversion of Wind to Power

• Predict power production for individual turbine types using per-farm data mining models

• Roll up power for turbines at farm• Predictors:

• Wind speed• Temperature• Atmospheric pressure

• Target:• Filtered turbine power

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Data Mining Techniques

• Regression Tree • Cubist• Random Forest• Gradient Boosted Trees

• Errors:• 20-40 kw for a 1500 kw turbine• Can be reduced by

approximately 50% if previous power is used in data mining

Required Forecasts

Thank You!

Documents

Title_Sli de Quality Controlling Wind Power Data for Data Mining Applications Gerry Wiener Research Applications Laboratory Software Engineering Assembly,