Presentation on theme: "Un-freezing the turbulence: improved wind field modelling for investigating Lidar-assisted wind turbine control Ervin Bossanyi."— Presentation transcript:
1Un-freezing the turbulence: improved wind field modelling for investigating Lidar-assisted wind turbine controlErvin Bossanyi
2Contents Possibility of Lidar-assisted control for wind turbines Reasons for current interestSimulation modelling including Lidar sensorsTaylor’s Frozen Turbulence hypothesisUnfreezing the turbulenceEvolution of wind fieldModelling along-wind decorrelationExample simulation resultsDecorrelation effectEnhancement of collective pitch controlReducing extreme loadsTurbulence with embedded gustsConclusions
3Possibility of Lidar-assisted control for wind turbines Lidars now well developed for site assessmentTurbine-mounted Lidars could be cheaperLaser beam points ahead of turbineDoppler-shifted reflection from particles reveals ‘line-of-sight’ wind speedScanning or multiple beams sample the swept areaSignificant potential for load reductionWind field preview circumvents delays in feedback controlPossibility to reduce both fatigue and extreme loadsLimited potential for direct increase in energy outputReoptimisation of turbine design will lead to improved cost-effectiveness……perhaps enough to pay for the Lidar, at least on large turbines
4Simulation modelling including Lidar sensors Wind field is sampled at points along the beam lineWind vector resolved in direction of beamSamples averaged using a weighting functionSingle scanning beam or multiple fixed beamsSingle or multiple focal points along beam (pulsed or continuous-wave Lidar)Existing simulation models assume Taylor’s Frozen Turbulence hypothesis: turbulent variations convect downwind at mean wind velocityIn reality, turbulence measured by the Lidar will evolve and change before it reaches the turbine.Control action based on the measurement will be incorrect.
5Unfreezing the turbulence yx, or t = x/UFrozen turbulence:3-dimensional wind fieldEvolution of the wind fieldzytx = x0x = x0 + xix4-dimensional wind field
6Modelling along-wind decorrelation Kristensen model of turbulent eddy decayEddies decay with time constant (n): function of frequency n ( = U/)P1 = probability of eddy surviving for time t, or for distance x = UtP1(n) = e- t/(n) = e - x/(U(n))Transversal diffusion: P2 = probability that an eddy seen at the first point will actually pass through the second point x downstream without drifting sidewaysTotal probability is a measure of coherence:P1P2 =Assumptions: (axisymmetric Gaussian transversal diffusion, isoptropy and neutral lapse rate)and G() = 2 ( + 1/121)0.5 / ( + 1/33)11/6
7Modelling along-wind decorrelation Turbulence modelled by Veers method:SpectrumRandom phase (correlated between grid points to represent cross-wind coherence)Use two uncorrelated realisations with i,1 and i,2 (different random number seeds).First realisation might evolve into the second one an infinite distance upstream.fi = f(ni) represents the extent to which eddies from realisation 1 have evolved intorealisation 2. fi = P1P2 =
8Modelling along-wind decorrelation Could also be used with “non-Veers” wind fields (e.g. Mann): just use FFT to decompose turbulence into Veers form first; store spectrum & phase for each grid point and each frequencyLength scale L calculated by integrating the autocorrelation function, calculated as FFT of the power spectrum.Can use other coherence models than Kristensen: all we need is an expression for the coherence fi =Applied similarly to other two components of turbulence: just use the appropriate spectrum and length scale.Still no mass flow continuity
9Example simulation results (1) ‘Ideal’ sensor measuring longitudinal component at a single point 150m ahead of the turbineLow frequencies remain similar;High frequencies evolve more towards second realisation
10Example simulation results (2) Two ‘Ideal’ sensors:measuring lateral component at a single point 75m ahead of the turbine and 45m to the sidemeasuring vertical component at a single point 75m ahead of the turbine and 45m above centreline
11Example: Lidar-assisted collective pitch control Simple Lidar feed-forward results in tighter speed controlFeedback gains then reduced to restore speed control and reduce loadsPreview information results in smoother, calmer control actionCW Lidar, 75m range, circular scan with 30º half-angle giving scan circle radius 37.5m (60% of turbine radius)
12Example: Lidar-assisted collective pitch control Small effect of decorrelation, as high frequencies already filtered out:Averaging along beam (with weighting function)Averaging around the circular scan (50 points in 1 second)Further averaging to remove high frequencies, so that frozen turbulence is not “cheating” (cut-off frequency U/min where min = 17.3m chosen where Kristensen coherence = 0.2)No cheating with unfrozen turbulence, so we can remove that last filter if it’s helpful.
13Example: Lidar-assisted collective pitch control. Time history Example: Lidar-assisted collective pitch control Time history DEL for this simulationSteelTower base DEL sensitive to random number seed due to light dampingDEL reduced by 18.7% with frozen turbulence; average 16.9% if unfrozenDEL reduced by 26.2% if filter removed
14Reducing extreme loads What is an extreme gust???Even if it doesn’t evolve, with what speed and direction does the gust convect between Lidar measurement and turbine?Could an extreme gust sneak up from the side, undetected?
15Turbulence with embedded gust? Gust shape consistent with spectrum and coherence modelMay not represent the worst real events:Non-Gaussian extreme turbulenceLow-level jetsThunderstorm down-draughtsEddies from topographyMay not even generate particularly severe loadsHere, pitch action follows gustTower loading actually reducesSome increase in asymmetricloads
16Embedded gust with unfrozen turbulence Line-of-sight velocity during passage of gust, as measured by a LidarNot much evolution over 75m
17ConclusionsMethod proposed to simulate evolution of turbulent wind field, avoiding the “idealism” of frozen turbulenceImplementation based on Kristensen/Veers is easily generalisedVery simple Lidar feed-forward scheme has significant potential to reduce fatigue loadsResult is affected by evolution of the turbulenceAs the evolution is now modelled, low-pass filtering can be minimised, which leads to significant further reductions in fatigue loadsWhether Lidar can be relied on to reduce extreme loads depends on definition of extreme load cases (standard gusts not really valid)Embedded gusts may be more realistic, but are they sufficiently extreme?
18Thank you for your attention Feedback / criticism / questions / discussion?