Examples of the use of operational WTG data

Mark Žagar, Ph.D., Specialist

Plant Siting & Forecasting

Vestas Wind Systems A/S

mazag@vestas.com

[14 April 2016, EWEA Technology Workshop, Bilbao]

2

Vestas in brief

More than 54,000 turbines in 73 countries on all

continents55,000

Real-time monitoring of over 27,000 turbines29,000

Around 20,000 employees worldwide, more than 30

years of experience with wind energy20,000

6.9 billion EUR revenue in 2014€ 6.9b

Data as decision drivers – start with the best, not with a guess!

Smart database and supercomputer resources

Unique Global Mesoscale Climate Library

+

More than 29.000 turbines online

+

More than 55.000 turbines installed

+

More than 6500 met masts

+

Performance and diagnostics data

Observe, Understand, Predict

WTG event

• Under-performing

• Alarms/shut-downs

• …

Modelled data

(NWP, CFD, …)

provides meteoro-

logical context

Connect

• Physics

• Statistics

• Experience

Methods, Algorithms:

• Real-time

diagnostics

• Future prediction

(pre- and post-sales)

Often no other data

available …

o Validate, improve

models

o Plan and / or adjust

plant operations

o Identify and fill gaps in

measurement sensors

De-icing gain calculation

IEA Task19

Icing terminology and

schematics

Schematic de-icing system application

Example 1

Modelled ice accretion and observed production

De-icing

Example 1

Often the power != 0 during

an icing event.

The question is: how much

would a WTG produce

without the de-icing system

De-icing

Relative Power Curve during Icing (And a bit post)Observations from Icing Periods, from trigger to end and 100hrs more.

Data here from 21 turbines V112, 3 sites (lots of events)

Example 1

Relative Power Curve during Icing (And a bit post)Additive Smoothers = f1(wind speed) + f2( temp) +f3(Sun)+ f4( z_ice )

R^2 = 43% and MAE = 12%

Example 1

Relative Power Curve during Icing (And a bit post)Random Forrest = mean( Ensemble of Regression Trees )

R^2 = 92% and MAE = 4%

Example 1

Climate

LibraryWTG Data

Relative Power

Scoring Model

WTG Data

With

Relative

Power

DeIcing Gain

Algorithm

De-icing system performance

dashboard.

Updated every month.

Example 1De-icing gain calculation

Pre-sales production loss estimate

Method verification

against the observed

production loss

Example 1

30 April 2013

Extreme Yaw Error alarm

In the order of appearance,

~275 events, 90 minutes

Downslope storm, hydraulic jump

Page

Up

Page

Down,

to repeat

Example 2

30 April 2013

Extreme Yaw Error alarm

In the order of appearance,

~275 events

Downslope storm,

hydraulic jump 90 minutes

Note the retreating edge,

and alarms

Page

Up

Page

Down,

to repeat

Example 2

Downslope storm,

hydraulic jump

dx=100m

Holton, 1992

Limits of

microscale

Example 2

Sudden wind changes

Frequency of sudden

wind speed changes

indicates:

- hydraulic jumps

- thunderstorms

Example 2

Downslope storm,

hydraulic jump

dx=100m

Data from nacelle

anemometers overlaid

Power of real + modelled

data:

Events explained,

model validated

Example 2

VestasOnline® PowerForecast. IntraDay!

Site specific mixing

between forecast model

and real time data

improves the forecast for

the nearest hours

Setup phase:

Operational

Forecast Phase:

Wind Power forecast on given grid

Real time SCADA data

Mixing Function Intraday Forecast

Wind Power Forecast

model

”Real time” and stable

SCADA data from

turbines or other

measurement point

1. Data Feed: 2. Downscaling to turbine level 3. Transfer Function

Statistical methods

PWEA 2016 Warsaw, Poland17

Example 3

18

3 sites with stable SCADA feed

Intraday PowerForecast.There is a significant benefit of having fast real time

SCADA feed to improve the 0-6 hours forecast horizon.

0

2

4

6

8

10

12

14

29% improvement 43% improvement 16% improvement

AEP

%

Benefit of Intraday Forecast on the 0-6hour horizon

PWEA 2016 Warsaw, Poland

Example 3

3-5 year outlook

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