2. October 29th 2018
Performance Transparency Project (PTP): Enabler for innovation & performance improvement

3. Auctions, larger turbines, experience and innovations have substantially driven down LCOE for new turbines
Source: Polenergia
Page 3

4. Why don’t we deploy all the learnings, innovations and optimizations from new turbines to the existing fleet?

5. Large potential to apply innovations to existing fleet but the challenge is to prove & quantify improvements ? ✔
540GW global wind energy generation capacity at the end of 2017
1% improvement = 5.4 GW “extra” capacity
2% improvement = 10.8 GW “extra” capacity
3% improvement = 16.2 GW “extra” capacity …
The challenge is to prove and quantify the effect of innovations.
If you cannot prove the business case of an innovation no one will adopt it…
Source: GWEC
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6. Why is it so difficult to quantify the extra production from innovations?

7. Power performance improvement =
Simplified: The lack of a reliable wind measurement complicates the validation of performance improvement
Power performance improvement =
Input (wind) / output (MWh) after innovation
compared with
Input (wind) / output (MWh) before innovation
Large uncertainties
Easy to measure
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8. How much would a better wind measurement improve our ability to verify performance and performance changes?

9. Performance Transparency Project: Demonstrate the effect of better wind measurement on performance validation ? ✔ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺
 Performance Transparency Project (PTP): Demonstrate that it has become possible with iSpin to measure and directly compare the performance of wind turbines across wind farms & portfolios with a low level of uncertainty
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10. PTP set-up: Measure and compare performance across 89 turbines spread over 9 wind farms in different terrain
Simple terrain
Semi complex terrain
Complex terrain
Offshore
Turbine Type A (30)
Wind farm in Romania (10)
Wind farm in Croatia (10)
Wind farm in Belgium (10)
Turbine Type B (29)
Wind farm in Poland (10)
Wind farm in Portugal (9)
Wind farm in Portugal (10)
Turbine Type C* (30)
Wind farm in France (8)
Wind farm in Portugal (10)
Wind farm in NL (12)
All wind farms have IEC compliant met mast as reference (Danish Technical University)
Power curve measurements follow IEC to the extent possible with review by DNV-GL, UL (DEWI) and Deutsche Wind Guard
Results and reports to be made public (
Supported by EUDP (Denmark)
*) Wind farms not confirmed

11. PTP: Can we demonstrate that wind measurements are largely unaffected by time and location?
Uncertainties mostly from nacelle anemometer dependent on turbine, location and season
Need measurement unaffected by time and place
Wind = energy input
Can we demonstrate that wind measurement characteristic (Spinner Transfer Function – STF) by iSpin is (largely) unaffected by location and time?
Question 1: Is the STF stable across all seasons (time)?
Question 2: Is the STF the same everywhere (site class)?
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12. PTP Answer to question 1: No seasonal effect on the STF visible!
IEC operates with default 2% uncertainty for seasonal variation
Negligible uncertainty with iSpin on differences in performance due to different season
Direct comparison between winter / spring / summer / autumn performance possible

13. PTP Answer to question 2: STF robust across different locations!
No site calibration needed. Enables direct comparison of turbines across fleet regardless of location
Substantial reduction in the uncertainties when comparing the performance of a turbine to another
Reliable measurements in all wind sectors (360 degree power curves)

14. What is the increased transparency on performance from a comparable energy input (wind) measurement?

15. Nacelle anemometer power curves
Inter wind farm power curve comparison for turbine type B (29 turbines)
iSpin power curves
Nacelle anemometer power curves

16. Comparing apples to apples; what would the turbines produce (output) if they had the same wind (input)
iSpin
Nacelle anemometer (SCADA)
All turbines perform within +/- 3% band (95% confidence interval reduced by half)
Changed view on under/over-performing turbines
No visual differences between wind farms

17. Changes of performance over time
Performance band (+/- 2 std dev) does not change over time with iSpin
Performance can be easily tracked over time compared with SCADA data

18. Nacelle anemometer (SCADA)
Benchmarking within a wind farm - Identification of real underperformer
iSpin
Nacelle anemometer (SCADA)
Although the operator was closely following the performance of the wind farm using SCADA data in collaboration with the OEM they could not identify turbine #5 as underperforming
Deeper investigation reveal different parameter settings on the underperforming turbine

19. Preliminary conclusions
It is possible to significantly reduce the uncertainties when comparing performance “before” and “after” deploying an innovation / optimization measure so that a more solid business case can be built.
It is possible to improve performance monitoring by directly comparing wind turbines across fleets
Over- and underperforming turbines can be directly identified
Performance changes over time can be monitored and quantified
 It is possible to verify and quantify performance improvements on the existing fleet

20. Check Performance Transparency Project website for more info: www
Check Performance Transparency Project website for more info:

21. Jan Nikolaisen