Continuum Wind Energy • Singapore-Based IPP • Founded in 2009 • 165MW Operating in India

General overview of steps involved

Defining campaign design / optimization strategy by selecting the most under-performing turbines

Based on synchronous SCADA data, Energy Yield Assessment Reports along with terrain data, the least performing turbines are identified and the optimization schedule is defined on that basis.

Pre-installation requirements

Communication and synchronization protocols are established to define the most appropriate communication protocol for data retrieval (LAN, 3G,…).

Deployment of Wind Iris on the first selected turbine

Wind Iris is installed on the selected turbine on 02.06.2014 and the installation duration is 12 hours.

Collecting and monitoring the raw Wind IRIS data

Raw data is monitored in real-time and retrieved remotely through a dedicated software.

Analysing and Calculating the yaw error

A unique custom-made yaw error calculator analyses and calculates the yaw error.

Calculation procedure

for yaw error correction

What is the magnitude of the yaw error?

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Real Yaw Error

Estimation of Yaw Error- General Methodology

Filtering

10 Min Averaged data from LiDAR

Valid 10 min averaged yaw error

Time average

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10 Min Averaged data from Wind IRIS LiDAR

The table above shows a sample of the Wind IRIS raw data.

The raw data sets contains 10 min. averaged data for 10 distances

from 80m to 440m

The column titled ‘Direction’ gives the angle between nacelle axis and

actual wind direction which is by definition the Yaw Error.

Timestamp Distance Wind Speed Direction

Wind Speed availability

21/07/2014 00:00 80 5.78498 2.70267 0.63417

21/07/2014 00:10 80 6.31798 6.4309 0.64667

21/07/2014 00:20 80 6.62527 -6.54881 0.65083

21/07/2014 00:30 80 5.86214 -14.2325 0.685

21/07/2014 00:40 80 5.33299 -4.38071 0.7075

21/07/2014 00:50 80 6.72786 8.25018 0.71083

21/07/2014 01:00 80 8.37694 -16.995 0.71583

21/07/2014 01:10 80 7.86368 -16.8487 0.73917

21/07/2014 01:20 80 8.16199 -7.2046 0.73

… … … … …

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Four main filters integrated in the Yaw Error Calculator

• Wind Speed Limits

Data with wind speed above rated speed and below cut-in are removed.

• Wind Speed Availability > 0.3

• Root Mean Square Error < 0.5° (based on IEC61400-12-1)

To ensure flow homogeneity between the two beams

• Extreme Yaw Error

AYE (n-1) – 20° < Avg.Yaw Error < AYE (n-1) + 20°

To ensure any sudden changes or anamolies are ignored

Filtering

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After filtering, the resulting ‘n’ valid yaw errors are time averaged.

Averaged Yaw Error = 1/𝑛 (𝛴 Valid yaw error k)

Yaw error measurement campaign should be monitored and stopped based on the convergence of the Averaged Yaw error.

IC95 – A statistical tool is used to determine the convergence and optimize the duration.

95% 𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 𝑓𝑜𝑟𝑚𝑢𝑙𝑎:

2 ∗𝑟𝑚𝑠 𝑣𝑎𝑙𝑖𝑑 𝑦𝑎𝑤 𝑒𝑟𝑟𝑜𝑟

√𝑛 where n is the number of valid yaw erros.

Time Average- Real Yaw Error

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IC 95

Optimisation Tool and Campaign duration

95% chance that the Yaw Error calculated is with-in certain limits.

When IC 95 < 0.5, Yaw Error is with-in +/- 1° of the calculated value.

+/-1° has only 0.25% AEP impact.

So, IC-95 increases ROI by reducing campaign duration and increasing the number of optimized turbines.

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SURAJBARI WIND FARM PILOT PROJECT RESULT

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Campaign duration: 15 days

Yaw-Error at the end of 15 days : -7.55°

Negative sign indicates misalignment orientation

Yaw Error and its convergence

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Convergence of the Error

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Yaw Error Convergence table – After correction

Post correction error for a duration of 15 days

After Correction Yaw Error : -2.17°

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Comparison of the Error

POWER CURVE ANALYSIS

Quantifying the benefits of

Yaw Misalignment Correction

REFERENCE DOCUMENTS

IEC61400-12-2 Ed. 2013

DTU procedure for wind turbine power performance measurement with

a two-beam nacelle Lidar, 2013.

Inputs required for Power Curve Analysis

Required for filtering the raw data, performing wake analysis and normalising the wind speed.

The input quantities must be 10 minutes average

Synchronization between LiDAR and Turbine

Synchronization between the Wind turbine and the Wind Iris must be respected

Synchronized Non-Synchronized

Steps involved

Wind IRIS data

Turbine data

Filtering

Normalisation

BIN 0.5m/s

Power Curve

FILTERS APPLIED ON RAW SCADA AND LiDAR DATA

In accordance with IEC 61400-12-1, data for which the turbine indicates a failure status in SCADA are excluded.

Wind Speed Availability: Refers to the relative number of valid wind measurement on one line of sight in 10 minutes.

The DTU procedure advices a minimum wind speed availability for both line of sights ensuring the quality of the data.

If generator rotational speed is available in the database, data with RPM below RPMmin (minimal RPM for turbine to be operating) are discarded.

It ensures that no blade stands in front of the laser beam during a long time in a 10 minute interval.

𝑅𝑃𝑀𝑡𝑢𝑟𝑏𝑖𝑛𝑒 > (𝑅𝑃𝑀𝑚𝑖𝑛)

Measurement height

IEC61400-12-1:

“This anemometer [MM anemometer] shall be within 2,5 % of hub height (HH)”

Measurement height is calculated with the help of ‘Tilt Sensor’ inside the Wind

IRIS (during installation)

Data measured outside the +/-2,5% HH:

Could be considered in the study, but with a higher uncertainty (cf. DTU procedure)

Could be discarded of the study.

+/- 2,5% HH

HH

During installation, the pre-tilt helps keeping the measurement in +/-2.5%

HH interval.

Measurement height

WAKE SECTORS

Neighboring wind turbine/obstacle generate wakes. Those sectors have to be excluded.

The width of wind sectors to be excluded need to be defined.

DTU formula (Very Conservative)

α𝐿 = 1,3 ∗ 𝐴𝑟𝑐𝑡𝑎𝑛 2,5 ∗𝐷𝑛

𝐿𝑛−𝐿𝑏+ 0,15 + 10

MeteoPole formula (Enough for all practical purposes)

α𝐿 = 1,3 ∗ 𝐴𝑟𝑐𝑡𝑎𝑛 2,5 ∗𝐷𝑛

𝐿𝑛+ 0,15 + 10

The sector to be excluded shall be centered on the direction from the neighboring wind turbine.

Power Normalization

Normalization is applied to the wind speed

Where

Vn is the normalised wind speed

Vfree is the measured nacelle wind speed

𝜌𝑜 is the reference air density

The reference air density, 𝜌0, shall be the average of

the measured air density of the valid collected data

set at the site during the test period.

𝑉𝑛 = 𝑉𝑓𝑟𝑒𝑒 ∗ 𝜌10𝑚𝑖𝑛

𝜌 𝑜

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Normalisation using temperature

Pressure and Relative Humidity are considered close to

standard conditions and hence the previous equation can be

expressed in terms of temperature.

𝑉𝑛 = 𝑉10𝑚𝑖𝑛 ∗𝑇0

𝑇10𝑚𝑖𝑛

1/3

After normalisation the wind speeds are binned and the

power curve is drawn with the averaged power in each bin

Before Yaw Error Correction…

After Yaw Error Correction…

Pre & Post correction power curves comparison

Averaged power in each bin

BIN CENTER (m/s)

PRE-CORRECTION POWER (kW)

POST -CORRECTION POWER (kW)

BIN CENTER (m/s)

PRE-CORRECTION POWER (kW)

POST-CORRECTION POWER (kW)

4 12.7 12.23333 9.5 1001.7 1044.743

4.5 52.5 62.13333 10 1093.3 1168.063

5 121.1 122.7208 10.5 1163.3 1282.377

5.5 197.7 181.2579 11 1259.5 1367.664

6 282 256.8807 11.5 1327 1460.453

6.5 344.3 351.6 12 1395.5 1529.395

7 449.4 465.2881 12.5 1448.4 1582.89

7.5 550.8 544.3584 13 1446.6 1625.943

8 655.2 692.617 13.5 1436.6 1645.449

8.5 776.3 811.0343 14 1662.5 1657.477

9 902.1 906.6478 - - -

AEP CALCULATION

Total 10-min counts is calculated for each bin.

It is multiplied by power in each bin for both pre and post

correction.

AEP in each bin is summed up to get total AEP for both pre

and post-correction.

Net AEP gain is calculated from the actual site wind data.

Energy in Each bin

BIN CENTER (m/s)

PRE CORRECTION Total (kWhr)

POST CORRECTION Total (kWhr)

BIN CENTER (m/s)

PRE CORRECTION Total (kWhr)

POST CORRECTION Total (kWhr)

4 61950.6 59674.2 9.5 1011717 1055190

4.5 267330 316382.9 10 729231.1 779097.8

5 566021.4 573597.2 10.5 587466.5 647600.4

5.5 951925.5 872756.8 11 434527.5 471844.2

6 1265052 1152367 11.5 307864 338825

6.5 1413007.2 1442966 12 245608 269173.5

7 1588179.6 1644328 12.5 150633.6 164620.6

7.5 1601175.6 1582450 13 92582.4 104060.4

8 1664863.2 1759940 13.5 51717.6 59236.16

8.5 1582875.7 1653699 14 38237.5 38121.97

9 1244898 1251174 Total (MWhr)

2642.811 2706.184

GAIN IN AEP

Gain in AEP = 𝑃𝑜𝑠𝑡 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝐴𝐸𝑃− 𝑃𝑟𝑒 𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝐴𝐸𝑃

𝑃𝑟𝑒 𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝐴𝐸𝑃

= 2706.184−2642.811

2642.811

Gain in AEP = 2.4% ( for -7.55° yaw error)

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Upstream wind measurements provide solutions to

solve wind turbine under-performance

Within two weeks trial, improvement in AEP is 2.4%

after yaw angle misalignment correction

A solid return on investment is obtained with an all

inclusive model

Conclusions on the pilot project