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Efficient Wind Farm Performance
Analysis & Optimisation
Christopher Gray
November 2012
© Uptime Engineering GmbH 07.11.2012
Contents
Complete process
Software & automated analysis
Performance indices
Detailed analysis
Field inspection, corrective action
Online Monitoring
Continuous Improvement
Summary
© Uptime Engineering GmbH 07.11.2012
The root of the matter...
Consider rho in analysis (pressure, temperature, humidity, altitude)
Treat U with care (measurement error, wake effects, turbulence, sheer, veer)
Maximise A (swept area) avoid yaw offset
Maximise Cp (power coefficient) avoid losses
Focus on reducing losses: aerodynamic, drivetrain, electrical system, transmission
1) Energy Yield = f (Installed Capacity, Availability, Capacity Factor)
2) Capacity Factor = f (Wind Speed Probability Distribution, Power Curve)
3) Power Curve = U3 x rho x 0.5 x A x Cp
© Uptime Engineering GmbH 07.11.2012
Complete Optimisation Process
The improvement process must be systematic and efficient to keep costs down
MONITOR CONFIRM CORRECT INSPECT ANALYSE FOCUS
A systematic process is required to efficiently identify and solve problems
The main steps include analytical techniques as well as practical field work
An investigative approach is required, applying technical understanding
© Uptime Engineering GmbH 07.11.2012
Software
Efficient performance assessment to identify problem turbines
Automated data processing, flexible reporting tools
Customised software tools support the analyst in root cause investigation
power
curve
cross
checks
KPI’s stats
Data Validation
& Processing
Analysis Tasks
Monthly
Data Results
Reporting Tools
© Uptime Engineering GmbH 07.11.2012
Top Level Analysis
Turbine production ratio energy produced at turbine vs expectation according to power curve
Park production ratio energy metered at grid connection vs expectation
Automatic detection of outlier power curves (statistical Jackknife)
Quantified energy loss due to non-optimised performance (below power curve)
KPI‘s are automatically generated and analysed to quickly identify problem areas
© Uptime Engineering GmbH 07.11.2012
Sensor specification Control software
versions & parameters Power curtailment
Resource assessment
Sensor calibration certificates
Service history
Multiple Data Sources
Consider all available data sources, look for differences, check for known issues
Various sources of information should be considered during investigative analysis
SCADA 10-Minute Logs
Vibration CMS
Alarm records
Bill of Materials
Power performance test reports
Monthly performance reports
© Uptime Engineering GmbH 07.11.2012
Bill of Materials
Centralised information accessible via intuitive software saves time!
Specification of main components should be ideally logged in a company-wide database
Queries can provide immediate information concerning specification or build differences
Database query to identify differences in build
© Uptime Engineering GmbH 07.11.2012
Power Curve Analysis
Power curves should be corrected to account for air density effects (take care of altitude!)
Filters should be applied to remove start-up / shut-down events
Standard techniques: directional sector filtering, multi-turbine comparisons
Power curve analysis should be carried out carefully and using corrected data
© Uptime Engineering GmbH 07.11.2012
Pitch [deg]
>
Multidimensional Analysis
High quality visualisation tools help the analyst to interpret multidimensional problems
Po
we
r [k
W]
Transient Power Curve
Power Curve vs Direction
vs Pitch
Po
we
r [k
W]
© Uptime Engineering GmbH 07.11.2012
Sources of Uncertainty
Wake effects, turbulence, sheer & veer, time constants
Varying turbulence intensity across wind speed range
Sensors strongly affected by wake of passing blades
Wind speed sensors built for high reliability, some
limitations in accuracy (+/- 0.5 m/s)
Sensors can be adjusted via a lookup table (source: FT
Technologies Ltd)
Power curve analysis vs manufacturer power curve must be treated with care!
0 5 10 15 20 250
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Wind Speed [m/s]
Turb
ulen
ce In
tens
ity [-
]
0 5 10 15 20 250
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Wind Speed [m/s]
Win
d S
peed
Std
ev [m
/s]
Mean wind speed [m/s]
Mean Wind speed [m/s] S
tde
v w
ind
sp
ee
d [m
/s]
Turb
ule
nce Inte
nsity [-]
© Uptime Engineering GmbH 07.11.2012
Sensor inaccuracy
Check anemometer accuracy using multiple variables
Power, Pitch, Rotor Speed vs Wind Speed
If offsets are similar in all parameters, sensor error is likely
Power cross checks remove wind speed influence altogether
Additional checks can improve confidence in results despite wind speed measurement issues
T1 T2 T3 T4 T5 T6 T7 T8 T9
wind speed
wind speed
wind speed
offset
offset
offset
pow
er
pitch
ro
tor
sp
ee
d
T1
T2
T3
T4
T5
T6
T7
T8
T9
Monthly average power differences
© Uptime Engineering GmbH 07.11.2012
Windspeed Cross Checks
Erroneous wind speed measurements can be detected by comparing with neighbours
Example: compare the time series for each turbine with the median of the others
Visualise distribution of “residual” using statistical box plots
Cross checks vs. neighbouring turbines can identify outlying windspeed measurements
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
1 2 3 4 5 6 7 8 9 10
Turbine ID
Win
dspee
d(N
) –
Win
dspee
d(m
edia
n)
[m/s
]
Box plots (source: wikipedia)
© Uptime Engineering GmbH 07.11.2012
Case Study (1)
Direct comparison of power vs neighbour turbine confirms loss of production
why is my
turbine
performing so
badly?
1.08 1.082 1.084 1.086 1.088 1.09
x 105
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Time Step
Pow
er
[kW
]
T02
T03
Po
we
r [
kW
]
Time Step
!
T02 T03 T01
© Uptime Engineering GmbH 07.11.2012
Case Study (2)
Thorough desktop analysis can provide answers, avoiding expensive field work
0 50 100 150 200 250 300 350-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
Azimuth 20107 [deg]
Pow
er D
iffer
ence
201
08 -
2010
7 [k
W]
!
wake!
wake!
due to wake
effects in the
prevailing wind
direction! T02
T03 T01
Po
we
r (T
03
– T
02
) [k
W]
Wind Direction [deg]
© Uptime Engineering GmbH 07.11.2012
Field Inspection
Based on data analysis and investigative work, derive an inspection programme
Where possible, involve the OEM in the inspection process
Bring the right tools for the job
Examples of focus areas include
Alignment: pitch and yaw
Wind sensor condition and alignment
Generator settings (small stage, large stage)
Controller settings (noise limitation)
General condition of blades
Systematically record findings in a database
Focused turbine inspection can reveal the root cause of performance problems
© Uptime Engineering GmbH 07.11.2012
Correction
Sometimes corrective measures can be carried out immediately, for example:
Realignment of wind speed / wind direction sensors
Blade pitch realignment
Noise control settings
In some cases follow-up service activities may be required, such as:
Blade repairs
Sensor replacement
Pitch drive repair
Often the close cooperation of the OEM is required, for example if:
The turbine is still under warranty
Software updates or modification to parameter settings is required
Corrective measures range from simple, quick fixes to more complex component repairs
© Uptime Engineering GmbH 07.11.2012
Confirmation
The effectiveness of performed corrective measures should be accurately quantified
T05 1 month before / 1 month after Software Update
before update after update
Wind speed [m/s]
Pow
er
[kW
]
Performance analysis should be repeated to confirm successful optimisation
IEC61400-12: 180h of data in total, at least 30 min in each bin. One month is preferable
Recommended basis for benchmarking:
Power curve, before & after correction
Metered Energy vs neighbours, before & after correction (filter: all turbines running)
Park Energy Yield
T05 relative
Gain of 5%
Energ
y Y
ield
[M
Wh]
Before Update After Update T
01
T0
2
T0
3
T0
4
T0
5
T0
1
T0
2
T0
3
T0
4
T0
5
© Uptime Engineering GmbH 07.11.2012
Online Monitoring
Online Monitoring of asset performance provides an early warning mechanism
Software automatically calculates KPI’s and statistics, reporting only in case of problems
Also power curves can be regularly analysed and monitored (daily)
Note: an online system requires online, automated data validation!
Online Monitoring allows early intervention, reducing energy losses and maximising profitability
© Uptime Engineering GmbH 07.11.2012
Visualisation
A clear and intuitive overview of the status of all assets supports rapid response
A Geographical Information System provides a clear overview of the fleet health status
Power consistently below
expected level !
© Uptime Engineering GmbH 07.11.2012
Know-How Database
Know-how accumulated during successive projects should be systematically captured
Database of known problems, root causes, indicators and corrective measures
A centralised database for systematic know-how storage supports long term improvement
Performance Deficit
System Component Root Cause Performance Impact Analysis Task
Recommended Field
inspection Corrective measures
Control
system Software
Incorrect noise
control mode
Turbine running on
incorrect power curve
Power curve analysis,
rotor speed & pitch
map analysis
Check of controller
parameterisation
Re-parameterisation of
controller
Control
system Software
Software mismatch to
hardware
Turbine aerodynamic
or electrical efficiency
not optimised Power curve analysis
Check software
versions and
parameterisation
Update software or
parameterisation to
match hardware
© Uptime Engineering GmbH 07.11.2012
Summary
There is potential for performance optimisation of many turbines
Problem areas need to be efficiently identified
Custom built software supports effective analysis
Focused inspection and corrective activities have been proven to bring results
The optimisation activity provides technological insight, with positive side effects
A know-how database provides a basis for long term improvement
The optimisation process can be integrated into standard working practices
MONITOR CONFIRM CORRECT INSPECT ANALYSE FOCUS
© Uptime Engineering GmbH 07.11.2012