Performance monitoring of offshore wind turbines using a ...€¦ · Sri Harsha Vemuri Supervisors:...
Transcript of Performance monitoring of offshore wind turbines using a ...€¦ · Sri Harsha Vemuri Supervisors:...
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Performance monitoring of offshore wind turbines using a real-time wind
farm power curve
Sri Harsha Vemuri
Supervisors:
Gregor Giebel, DTU Wind Energy
Tuhfe Göçmen, DTU Wind Energy
Jesper Runge, Vattenfall
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29 June 2017DTU Wind Energy, Technical University of Denmark
Outline
29 October, 2018
1
Motivation
2
Objective
3
Background
•Wake Characteristics
•Larsen Model
4
Methodology
•Wind Farm Case
•Data Processing
•QDWS
•Anomaly WT Identification
5
Results & Analysis
6
Summary & Contribution
s
• Motivation
• Objective
• Background
• Wake Characteristics
• Larsen Model
• Methodology
• Wind Farm Case
• Data Processing
• QDWS (Quasi-Dynamic Wake Model)
• Anomaly WT Identification
• Results and Analysis
• Summary and Major Contributions
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29 June 2017DTU Wind Energy, Technical University of Denmark
1. Motivation
• Wind Turbines [WT] total installed capacity: 540 GW
• Operations & Management [O&M] significant part of Levelized Cost of Energy
• Poor accessibility of offshore Wind farms
• O&M could reach 30% for offshore WT
• System Reliability: Operational availability and achievement of predicted power output
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29 June 2017DTU Wind Energy, Technical University of Denmark
2. Objective
• Use SCADA data to Identify
Anomaly and underperforming WT
• Identify the appropriate data
processing techniques to reduce
uncertainties
• Identify Underperforming wind
turbines
• Recommendations for future
research
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29 June 2017DTU Wind Energy, Technical University of Denmark
3. Background: Larsen Model
• Larsen and FUGA have better performance for current case[1]
• Far wake (>4D): Gaussian profile, Rotor induced turbulence,
Atmospheric turbulence
• Simple and Fast method for modelling of wake
• Assumes wake to be steady, self similar and axisymmetric flow
• The parameters of model can be calibrated for site specific conditions
• Can estimate power production for wind farm with an uncertainty less
than 10%
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: Wind Farm Case
• Horns Rev-I wind farm
• Vestas V-80 WT
• Rated power: 2MW @ 13m/s
• Cut-in 5m/s
• Rotor Diameter: 80m
• Minimum Spacing: 7D
• Pitch Regulated
• Two asynchronous generators
– Low speed ~ 1.4 rad/s
– High speed ~ 1.9 rad/s
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: Wind Farm CaseData Description
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• HRData-1: 04/ 10/ 2013 to 10/10/2013
• HRData-2: 03/ 11/ 2013 to 10/11/2013
• Western Wind Direction
• Bias in wind direction of upstream WT’s corrected using
wake depth
• Mean wind direction of downstream WT’s assumed to
be same as the upstream WT’s
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: Data Processing
• Data Smoothening to reduce noise in the high resolution data
• Full wake condition is used to avoid using wake meandering model
• Effective wind speeds are selected from the linear region of power curve
• High Turbulence data is avoided
• Average wind speeds are used to approximate for time delay.
• Increased uncertainties and change in bias from wake superposition is observed
Data Processing
Data Smoothening Moving Mean of 60s
Full Wake Condition +/- 2.50 (TIeff < 2%)+/- 5.00 (TIeff > 4%)
Wind Speed 6 m/s < Ueff < 9 m/s
Turbulence Upstream TIeff <8%
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: QDWS, Hypothesis and Formulation
Hypothesis
• Wake losses experienced by WT is a
result of superimposed wakes
• Larsen wake model is valid for wakes
that are not superimposed
Formulation
• du1 = Uupwt - Ueff,inf
• duQDWS = a1 du1 + a2 du2
• UQDWS = Uinf + duQDWS
• Training data selected Leave-One-
Out method for WT’s in each column
• Training method: quantile regression
QDWS (a1,a2)
Training Data
TIeff
0 – 2 [%]
Training Data
TIeff
2 – 4 [%]
Training Data
TIeff
4 – 6 [%]
Training Data
TIeff
6 – 8 [%]
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: QDWS, Results
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29 June 2017DTU Wind Energy, Technical University of Denmark
• wt29 identified as anomaly in HRData-2
• It can be observed that the WT is operating below the expected wind speeds
• Hence, inferred as an underperforming WT
5. Results and AnalysisUnderperformance
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29 June 2017DTU Wind Energy, Technical University of Denmark
Major Contributions
• Anomaly & Underperformance WT detection
under full wake conditions
• QDWS Wake accumulation method that could
be used in algorithms such as PossPOW
Future Research
• Next step would be include wake meandering
model in QDWS
• Alternative QDWS training methods for power
forecasting
• Optimal SCADA frequency for wake based
performance monitoring
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Summary
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29 June 2017DTU Wind Energy, Technical University of Denmark
Thank You
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29 June 2017DTU Wind Energy, Technical University of Denmark
Supplementary Slides
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29 June 2017DTU Wind Energy, Technical University of Denmark
3. Background: Wake Characteristics
• Wind velocity deficit causes reduced power production downstream
• Increased turbulence causes unsteady loads
• Near wake (2-4D): rotor geometry, blade tip vortices, steep gradients of
pressure, velocity and wake expansion
• Far wake (>4D): Gaussian profile, Rotor induced turbulence,
Atmospheric turbulence
• Turbulence mixing accelerates wake recovery
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29 June 2017DTU Wind Energy, Technical University of Denmark
3. Background: Larsen Model (2009) [1]
• Reynolds-averaged Navier–Stokes equations, first order approximation
• Boundary conditions are defined experimentally
• The width of wake and the losses are calculated as functions of Coefficient of Thrust and the atmospheric Turbulence Intensity.
• The effective wind speeds at the wind turbine are calculated using linear approach (alternative: non-linear approach)
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29 June 2017DTU Wind Energy, Technical University of Denmark
3. Background: Larsen Model (re-caliberated) [1]
• The main variables that can be adjusted in the wake model are parameters
C1 and Xo
• The parameters are highly dependent on the TI and CT
• The same equations are formulated with new parameters
• The parameters can be trained specifically for each wind farm
• The mean wake losses are obtained by using 4-point gaussian integration
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[1] Tuhfe Gögmen.Possible Power Estimation of Down-Regulated Offshore Wind PowerPlants. PhD thesis, Technical University of Denmark (DTU), 2016.
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: Anomaly WTQDWS
• The median of error in QDWS is calculated
for WT’s
• Positive error suggests: UQDWS > Ueff
• WT’s with Quantiles greater than 85% and
less than 15% are considered as anomalies
• If a WT is identified as an anomaly, any of
the three WT data could be the reason
• Likely anomalies, wt4 and wt29
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29 June 2017DTU Wind Energy, Technical University of Denmark
4. Methodology: Anomaly WTCorrelation Coefficient Model & Ueff Similarity Model
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29 June 2017DTU Wind Energy, Technical University of Denmark
5. Results and AnalysisShort Term Anomalies
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29 June 2017DTU Wind Energy, Technical University of Denmark
5. Results and AnalysisShort Term Anomalies
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29 June 2017DTU Wind Energy, Technical University of Denmark
Turbulence Intensities
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29 June 2017DTU Wind Energy, Technical University of Denmark
Wake Depth
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29 June 2017DTU Wind Energy, Technical University of Denmark
Wake Summation Errors
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29 June 2017DTU Wind Energy, Technical University of Denmark
Jensen Model
• Commonly Used
• Linear wake expansion as function of
down stream WT distance
• Wake decay coefficient, calculated as
function of height, roughness,
atmospheric stability and turbulence
separation
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29 June 2017DTU Wind Energy, Technical University of Denmark
Rotor Effective Wind Speed
Formulation
- Haier’s Equation
- Parameters adjusted using ideal Cp curve
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29 June 2017DTU Wind Energy, Technical University of Denmark
Larsen Model (1988)
• First and second order approximation of RANS,
• Prandtl mixing length theory
• Wake perturbation in the axial and radial directions are calculated
• Boundary conditions: on the boundary of wake, inflow velocity much higher than the axial direction
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29 June 2017DTU Wind Energy, Technical University of Denmark
Larsen Model (2009)• Boundary conditions are defined experimentally
• Second order approximation is neglected
• Parameters tuned
• Incoming wind speeds are calculated linearly or quadratically
• In wake meandering wake perturbations are passed on to the turbulence field
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