Statistical Characteristics of Winds at Xcel Wind...

Statistical Characteristics of Winds

at Xcel Wind Farms

Yuewei Liu, Yubao Liu, Will Y.Y. Cheng and Gregory Roux

Wind Energy Prediction - R & D Workshop

11-12 May 2010

© 2009, University Corporation for Atmospheric Research. All rights reserved.

1. Introduction

2. Classification of intra-farm wind variation patterns

3. Node Level Power Curve (NLPC)

4. Wind/power persistence

5. Summary

Data:Met-tower measurementsTurbine nacelle wind speeds© 2009, University Corporation for Atmospheric Research. All rights reserved.

XXXX Farm2 Met-towers (A & H)

and274 Wind Turbines

A

H

4 – 6 m/s6 – 8 m/s

2 – 4 m/s8 – 10 m/s0 – 2 m/s

12 – 14 m/s> 14 m/s

10 – 12 m/s

• A major farm• Good historical and

real-time data• Modeling testbed• Complex terrain


Average Nacelle Wind Speed at XXXX Wind Farm

8.6 – 8.9 m/s8.9 – 9.2 m/s

8.3 – 8.6 m/s9.2 – 9.5 m/s9.5 – 9.8 m/s9.8 – 10.1 m/s

> 10.1 m/s

8.0 – 8.3 m/s

5.4 – 5.6 m/s5.6 – 5.8 m/s

5.2 – 5.4 m/s5.8 – 6.0 m/s6.0 – 6.2 m/s6.2 – 6.4 m/s

> 6.4 m/s

5.0 – 5.2 m/s


Wind Patterns and Frequencies of Intra-farm Flows at XXXX Farm

Objectives: Understand ingredients of farm power production Obtain climatologies of farm-wide microscale flows

(turbines sit 400-700m apart) Use the knowledge for model output post-processing Provide guidance for farm data assimilation

(Power FCSTWeather Regime Weather Pattern)

Methodology and data: SOMs (Self-Organizing Maps): A neural network

scheme for pattern recognition and classification (automated data feature extraction). 10-min average nacelle and met-tower measurements


4 Patterns Based on Raw SpeedsWinter Season: 20081023-20090209

4 – 6 m/s6 – 8 m/s2 – 4 m/s

8 – 10 m/s10 – 12 m/s

12 – 14 m/s> 14 m/s

0 – 2 m/s


Spatial Patterns (SOMs): 20081023-20090210

4 – 6 m/s6 – 8 m/s2 – 4 m/s

8 – 10 m/s10 – 12 m/s

12 – 14 m/s> 14 m/s

0 – 2 m/s

8 Patterns based on Raw Speeds

1 32

5

4

76 8


Objective: Relative speed distribution

N _ speedi =speedi

speed

speed =1n

speedii=1,274∑


4 – 6 m/s6 – 8 m/s2 – 4 m/s

8 – 10 m/s10 – 12 m/s

12 – 14 m/s> 14 m/s

0 – 2 m/s

4 Patterns Based on Normalized SpeedWinter 20081023-20090209


4 – 6 m/s6 – 8 m/s2 – 4 m/s

8 – 10 m/s10 – 12 m/s

12 – 14 m/s> 14 m/s

0 – 2 m/s

8 Patterns Based on Normalized SpeedWinter 20081023-20090209

1 32

5

4

76 8


Self Organizing Maps (SOMs) Analysis: Findings

1. SOMs is a very effective tool for intra-farm flow statistical property analysis.

2. Raw wind speed: Wind events dominate the patterns (useful for ramp event analysis)

3. Normalized speed: Spatial distribution dominates the patterns (useful for sub-grouping nacelle data, and can be used provide guidance for data assimilation)


Motivations: Wind power prediction Node level power vs. individual turbine generation

Methodology & data: Node level power curve: Referred to nacelle average wind speed Referred to nacelle medium wind speed Piecewise Linear Regression (Node level power

prediction) XXXX Farm: 20081023-20081123 Apply the regression equations to winter 2009

(20090902-20091014)


Node Level Power

09-02_02:00-10-14_04:45 (2009)

0

50

100

150

200

250

300

350

0 5 10 15 20 25

Mean-Wind (m/s)

Tota

l Pow

er(M

W)

09-02_02:00--10-14_04:45 (2009)

0

50

100

150

200

250

300

350

0 5 10 15 20 25

Medium-Wind (m/s)

Tota

l Pow

er (M

W)

Referred to Mean Referred to Medium


10-23_07:00-11-23_06:50 (2008)

0

50

100

150

200

250

300

350

0 5 10 15 20 25

Mean-Wind(m/s)

Tota

l Pow

er(M

W)

0

50

100

150

200

250

300

350

1 97 193 289 385 481 577 673 769 865 961 1057

1153

Modeled (red) and observed (blue) power during the validation period

p=25.x-87.5 (3.5<=x<6.5)p=30.x-120. (6.5<=x<8)p=38.33x-186.667 (<8=x<=11)p=31.5x-111.5 (11<x<13)p=305 (13<=x<=25)p=0 (x<3.5 or x>25)

Node level power (MW) curve referred to Nacelles averaged wind speed (m/s)09-02_02:00-10-14_04:45 (2009)

0

50

100

150

200

250

300

350

0 5 10 15 20 25

Mean-Wind (m/s)

Tota

l Pow

er(M

W)


Findings: The statistical analysis showed a stable regression

between the farm-wide mean wind and the node level power output for XXXX farm.

The node level piecewise linear regression equations of winter 2008 fit winter 2009.

The robustness of NLPR suggests that reliable power forecasts can be achieved by accurately forecasting the farm-wide mean hub-height wind speed.

Future work: Investigate node level power curve referred to average

speed for other wind farms


• Persistence Properties:

Persistent forecast performs “surprisingly well” up to 4 hours (Giebel et al.) – A Rule of Thumb

Persistence property is of high interest and value.

How rapidly winds vary in a wind farm? Persistence is the reflection of weather/wind regime:• among different wind farms• different periods at the same farm This important information can be used to guide the usage of

wind farm data for NWP model data assimilation and/or for statistical predictions.


Method:

Data: winter (Oct. 23, 2008 – Feb. 10, 2009)Persistence error: 10min, 0.5hr, 1hr, 2hr, 3hr

summer (July 14, 2009 – Aug. 31, 2009)Persistence errors:15min, 0.5hr, 1h, 2hr,3hr

y

c h a n g ep = pt − pt−1

c h a n g ep : p o w e rc h a npt : p o w e ra tt i m etpt−1 : p o w e ra tt i m et −1


10min

0.5hr

15min

0.5hr

Winter (10/23/2008 – 2/10/2009) Summer (7/14/2009 – 8/31/2009)


1hr

2hr

1hr

2hr

Winter (10/23/2008 – 2/10/2009) Summer (7/14/2009 – 8/31/2009)


• Findings Overall: > 50% cases change by 50% within 1 – 2 hr

Summer: Unstable, changes much faster than in winter

Strong power events occur in less frequency in summer; the strong power typically last for very short (< 1h) life.

• Future Works Variation of persistence for major weather regimes,

diurnal variations and for major SOMs wind patterns.

Provide guidance for NWP wind farm data assimilation Provide guidance for enhanced (RTFDDA) nowcasting

through post-processing using the wind farm data.


Met-tower and wind turbine data from XXXX Farm are analyzed to study

• Microscale flow patterns and occurrence frequencies • Node Level Power Curve and its potential usage• Wind/Power persistence properties


On-going work includes:• How to make use of the information to guide data

assimilation and model output statistical post-processing• Conduct analysis on the dependency of these statistical

properties on seasonal and diurnal weather regimes.

Statistical Characteristics of Winds at Xcel Wind...

Documents

Transcript of Statistical Characteristics of Winds at Xcel Wind...