Performance monitoring of offshore wind turbines using a ...€¦ · Sri Harsha Vemuri Supervisors:...

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

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


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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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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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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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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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


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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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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4. Methodology: QDWS, Results

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• 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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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


Thank You

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Supplementary Slides

29 October, 2018


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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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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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.


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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4. Methodology: Anomaly WTCorrelation Coefficient Model & Ueff Similarity Model

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5. Results and AnalysisShort Term Anomalies

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Turbulence Intensities

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Wake Depth


Wake Summation Errors

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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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Rotor Effective Wind Speed

Formulation

- Haier’s Equation

- Parameters adjusted using ideal Cp curve

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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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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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Performance monitoring of offshore wind turbines using a ...€¦ · Sri Harsha Vemuri Supervisors:...

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