Scanning lidar measurements of offshore wind turbine wakes. Peter Clive Senior Scientist, SgurrEnergy All Energy, Glasgow
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1 Scanning lidar measurements of offshore wind turbine wakes Peter Clive Senior Scientist, SgurrEnergy All Energy, Glasgow
2
3 Scanning lidar is a very powerful instrument It provides information we would not otherwise have This includes the detailed precise measurements of offshore wind turbine wakes that this talk is about
4 IEA lidar use cases Data requirements Lidar use case Situation Method
5 IEA lidar use cases Data requirements Situation Method
6 IEA lidar use cases Data requirements What does my project need? What question am I asking? Can I specify my requirements? Can I quantify the benefit of fulfilling these requirements? Method Have I adequately documented the measurement procedure? Does the method provide the data I require? Does it answer the question I am asking and fulfil my requirements? Situation Do I understand the accuracy of the method under the circumstances in which it is used? Is the accuracy sufficient? Can I do a complete and unbiased uncertainty analysis?
7 IEA lidar use cases IEA lidar use cases provide A formalism that has been adopted as a way of aggregating and organising diverse lidar methods by IEA Wind Energy Task 32 A means to ensure well-documented repeatable methods are applied appropriately with the consistency necessary to ensure investor confidence A basis for exploiting the new measurement opportunities scanning lidar represents and the new datasets it provides A framework to re-assess established wind industry practices and procedures to integrate effectively new methods and datasets
8 Why measure wakes? Why not to measure wakes: proposition (fallacious, but the basis of many of our data requirements anyway) what we can t measure can t have been important in the first place Consequence The cost of new data is known but the benefit remains obscure Brutal false economies are inflicted on measurement campaigns New measurement opportunities are ignored Unexpected negative project outcomes occur Incomplete uncertainty analyses are treated as though complete New data seems to introduce rather than reduce uncertainty Accidental short term success is no fortification against long term failure Conclusion It works today does not imply it will work tomorrow if you don t know why
9 Why measure wakes? If lidar is deployed, you can acquire data that: Allows understanding of wind conditions to be developed during earlier, pre-construction phases of delivery which would not normally possible until post-construction performance is observed Using data you would not otherwise obtain Directly measure adverse wind conditions rather than rely on indirect inference through observation of their consequences It turns out what we weren t previously measuring is important, for example: Complex wind shear and veer Wakes losses in stable atmospheres Etc. Data is insurance Better data coverage = lower risk
10 SgurrEnergy offshore experience Lenders technical advisor Advisory service
11 SgurrEnergy offshore experience Lenders technical advisor Advisory service Selected ongoing lender's engineer assignments for offshore wind farms include Galloper, Neart Na Gaoithe, Westermost Rough, Veja Mate, NordSee 1, Nordergrunde, Lynn & Inner Dowsing, Lincs, London Array, Gunfleet Sands, Butendiek, Meerwind, Borkum West II, Global Tech I, MEG I, Noerdlicher Grund, etc. Independent engineer and acquisition due diligence assignments for offshore wind farms include London Array, Belwind and Northwind, Project Gemini (600MW Dutch project), Dudgeon, Confidential French offshore wind zone, Rodsand II, Gode Wind I, Gode Wind II, Ormonde, Q10, Neart na Gaoithe, Veja Mate, Riffgat, London Array, Gwynt y Mor, Rhyl Flats, Walney I & II, Gunfleet Sands, Kentish Flats, Sandbank 24, Sheringham Shoal, Barrow, etc. Owner s engineer and technical advisor assignments include monitoring of Demowfloat floating WTG, O&M strategy advice to Taiwanese developer, Horseshoe Shoal (Cape Wind) owner s engineer, Egmond an Zee technical specifications, Nord Zee Ost TSA and O&M specifications and negotiations, North Hoyle post-warranty maintenance specifications and negotiations, UK Round 3 preparation of technical sections of client s bid to Crown Estate, interface and feasibility assessment of a large Chinese offshore wind farm development for a confidential European supplier, construction cost review for confidential UK Round 2 development, etc.
12 SgurrEnergy offshore experience SgurrEnergy has about two thirds market share as lender s advisors Advantages in relation to lidar products and services Lenders technical advisor Advisory service In depth knowledge, experience and expertise regarding areas where lidar can contribute to improved project delivery, through understanding of Real opportunities to lower risk and enhance revenues using lidar data Wind energy data requirements Artificially restricted requirements of established procedures defined by the limitations of conventional data acquisition methods and Real requirements related to project outcomes that can now be fulfilled as new measurement opportunities become available due to lidar Device agnosticism: In addition to Galion, SgurrEnergy has used ZephIR, Streamline and WindCube lidars and Triton and AQ500 sodars Disadvantages Other advisors are conflicted in relation to SgurrEnergy lidar products and services This limits the availability of reliable advice regarding Galion lidar
13 Ground-breaking offshore lidar wind measurements Europe Substation Baltic I Mast platform FINO1 Transition piece Alpha Ventus AV07 Sheringham Shoal Wind turbine nacelle Worldwide Alpha Ventus AV07 Fixed platform South China Sea Onshore to offshore Gulf of Mexico South Korea North East USA Japan Lenders technical advisor Advisory service Galion measurement ZephIR measurement
14 Ground-breaking lidar wind measurements all around the world
15 15
16 16
17 Galion G4000 mounted in a wind turbine nacelle 17 Slide 38
18 Wind speed (m/s) Plan view of wind turbine and measured wake Wind direction Wake Wind turbine
19 Velocity Deficit The wake takes approximately twice as long to recovery in stable night time atmospheres than in unstable day time conditions Horizontal Distance Bin (x 100m) Stable Atm Unstable Atm
20 Wake assessment Galion G4000 Offshore lidar installed on substation of Baltic I offshore wind farm SWE, Stuttgart
21 Wake assessment Substation Wind turbines Cables
22 Wake assessment First lidar scan of multiple offshore wakes, 2012 Wake image SWE, Stuttgart
23 Wake assessment Wake image SWE, Stuttgart
24 Gerrit Wolken-Möhlmann, Fraunhofer IWES North Sea
25 North Sea Galion G4000 Offshore on transition piece 2 x Galion G4000 Offshore on nacelle, one facing forward to survey inflow, one facing back for wakes Gerrit Wolken-Möhlmann, Fraunhofer IWES
26
27 FINO1 Reference mast ~100m AV7: AREVA M5000 Hub height 90m Rotor diameter 116m 1 x G4000 on T-piece 2 x G4000 on nacelle
28 4 km
29 4 km Offshore wakes can go a long way AV10
30 Northing from AV7 (m) Individual wake study REPower 5MW Areva Multibrid FINO1 Reference Mast AV Easting from AV7 (m)
31 Measured wind speed (% of freestream) Measured wake deficit 120% 110% 100% 90% 80% 70% 60% 50% Inflow wind speed bin 4-6 (m/s) 6-8 (m/s) 8-10 (m/s) (m/s) (m/s) (m/s) (m/s) Counts % 30% Distance (rotor diameters)
32 Measured wind speed (% of freestream) Measured wake deficit 120% 110% 100% 90% 80% 70% 60% 50% Inflow wind speed bin 4-6 (m/s) 6-8 (m/s) 8-10 (m/s) (m/s) (m/s) (m/s) (m/s) Counts % 30% Distance (rotor diameters)
33 Difference between lidar and park (% of inflow wind speed) Discrepancy 30% 25% 20% 15% 10% 5% 0% -5% -10% -15% -20% Inflow wind speed bin 4-6 (m/s) 6-8 (m/s) 8-10 (m/s) (m/s) (m/s) (m/s) (m/s) -25% -30% Distance (rotor diameters)
34 Measured wind speed (% of freestream) Measured wind speed (% of freestream) Wind speed dependence of discrepancy 110% 100% 90% 8m/s 10m/s 80% 70% 60% 50% 40% 30% Lidar Data PARK Data % 100% 90% 16m/s 18m/s 80% 70% 60% 50% 40% 30% Lidar Data PARK Data Distance (Rotor Diameters)
35 Wind profiles in the wake
36 Height (m) Wind profiles in the wake 300 6m/s 8m/s Distance from rotor (rotor diameters) Top Tip Hub Height Bottom Tip 0 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% Wind speed (% of 0 elevation +2.5D inflow)
37 Northing from AV07 (m) Wake interactions AV REpower 5M Areva M5000 Fino1 Reference Mast AV7 0.0 AV08 + AV Easting from AV07 (m) AV11
38 Wake Cross-Section AV07 AV08 AV09 This is a measurement, not a computer model and the first ever observation of this type
39
40 Normalised wind speed (m/s) Wake interaction AV07 AV08 AV Distance downwind (RD)
41 Normalised wind speed deviation (measured - modelled) Normalised wind speed Discrepancy with model AV07 AV08 AV Distance downwind (RD) Lidar Average Park Model Average AV07 AV08 AV Distance downwind (RD)
42 Wind turbine wake interactions
43 Height (m) Wind turbine wake profiles m/s RD upwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV05 WTG RD downwind of AV05 WTG Normalised wind speed
44 Height (m) Wind turbine wake profiles m/s RD upwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV05 WTG RD downwind of AV05 WTG Normalised wind speed
45 Height (m) Wind turbine wake profiles m/s RD upwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV07 WTG RD downwind of AV05 WTG RD downwind of AV05 WTG Normalised wind speed
46 Height (m) Wind turbine wake profiles Onset of cumulative deep array effect, with energy from above the array balancing cumulative wake losses within the array 8-10 m/s RD upwind of AV07 WTG RD downwind of AV07 WTG Top tip height 6.24 RD downwind of AV07 WTG RD downwind of AV05 WTG RD downwind of AV05 WTG Normalised wind speed
47 Height (m) Wind turbine wake profiles Onset of cumulative deep array effect, with energy from above the array balancing cumulative wake losses within the array 8-10 m/s RD upwind of AV07 WTG RD downwind of AV07 WTG Top tip height 6.24 RD downwind of AV07 WTG RD downwind of AV05 WTG RD downwind of AV05 WTG Normalised wind speed
48 Wind turbine wake interactions Top tip Compression zone
49 Conclusions Observations of offshore wind turbine wakes show Scanning lidar in the reference frame of the wake gives unprecedented direct and detailed measurements The distance wakes propagate is very sensitive to atmospheric stability Wind farm arrays modify ambient wind conditions. As a consequence Wind farm wake effects cannot be predicted on the basis of individual wind turbine wakes As the wind penetrates the array the wake is an emergent phenomenon which is a property of the array It is necessary to consider the wind farm as a single system whose individual wind turbines are interconnected components
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