How does the real world performance of wind turbines compare with

How does the real world performance of wind turbines compare
with sales power curves?
EWEA: Lyon, July 2012 – Keir Harman
Background
Asset Management and Optimisation Services
(AMOS)
• Turbine performance monitoring
• SCADA-based condition monitoring
• Fault diagnosis and forensic analysis of SCADA data
• Post-construction energy forecasts
• Warranty calculations
• End of warranty inspection analyses
• O&M advice
• Reliability profiling and benchmarking
Over 30 GW of operating wind farms assessed to date
What do we typically see in operating data?
• Power curves rarely lie on the sales power curve
WTG 1
WTG 2
WTG 3
WTG 4
WTG 5
WTG 6
WTG 7
Average Power [kW]
WTG 8
WTG 9
WTG 10
WTG 11
WTG 12
WTG 13
WTG 14
WTG 15
WTG 16
Warranted power
0
5
10
Nacelle anemometer wind speed [m/s]
15
20
Real world power curve losses/gains categorised
Category
Typical range of
loss/gain
(nominal energy %)
Most likely
(nominal energy %)
1) Generic power curve performance
2) Mechanical sub-optimal performance
3) Environmental: icing and dirty blades
4) Wind conditions: turbulence intensity, shear
and flow inclination
???
Category 1: Generic power curve performance
Proportion of database
• 115 project power curve tests using IEC guidelines [61400 pt 12-1]
< 92%
92% to
94%
94% to
96%
96% to
98%
98% to
100%
100% to
102%
102% to
104%
104% to
106%
106% to
108%
Percentage of warranted power curve energy achieved (NME/NWE)
• Average of results = 99%
• IEC measurement uncertainty typically 5%
>108%
Category 2: Mechanical sub-optimal performance – common causes
1) De-rating
2) Non-optimal controller settings
Power vs.
Rotor speed
Average Power [kW]
Power vs.
Wind speed
Rotor speed [rpm]
3) Component misalignment /
Sensor error
Category 2: Mechanical sub-optimal performance - What can be expected?
Definition
Database
62 wind farms across Europe
Between 1 and 6 years of operation
134 wind farm years
Energy produced
Operating Efficiency (OE) =
Energy expected with ‘normal’ power curve
60
Target
Operating Efficiency =100%
50% of database with OE of
99% or higher (Median)
40
Mean = 98%
30
20
Annual Operating Efficiency
101%
99%
98%
97%
96%
95%
94%
93%
92%
91%
90%
89%
88%
87%
86%
85%
84%
83%
82%
81%
0
100%
10
80%
Number of winf farm years
50
Category 3: Environmental - causes
Bugs
High impact for short periods
Wind speed
Dirty blades
Subtle impact but persistent
power
power
power
Icing
High impact on some sites
Wind speed
• Typical range -3% to -0.2% and very region specific
Wind speed
Category 4: Wind conditions
Average power [kW]
The power curve is impacted by:
• Flow inclination
• Turbulence intensity (TI)
• Shear profile
• Air density
Influenced by:
• Atmospheric stability (TI, shear, density)
• Complex terrain (flow inclination, TI, and shear)
• Forestry (TI and shear)
1600
1500
1400
1300
1200
1100
1000
900
800
700
600
500
400
300
200
100
0
0
30
60
90
120
150
180
210
240
270
300
330
0
2
4
6
8
10
12
14
16
Wind speed [m/s]
18
20
22
24
26
3
1
2
Category 4: Wind conditions:
Flow inclination impact on power curve (Extremes)
GLGH validation of Madsen/Pederson research for MW-scale wind turbines
Yaw error observations for MW-scale turbines (GLGH)
Category 4: Wind conditions
Turbulence Intensity (TI) and Shear impact on power curve (Extremes)
1600
900
1400
800
80%
700
Distribution
14%
15%
16%
17%
18%
19%
20%
21%
22%
23%
24%
25%
1000
800
600
400
70%
600
500
400
300
200
200
100
0
Power [% of Rated]
1200
Power [kW]
90%
60%
50%
40%
High TI
30%
Mid TI
0
0
5
10
15
20
Wind speed [m/s]
High TI case:
• 2% drop in nominal energy between TI of
14% and 20% due to ‘rounded knee’ for a
high wind speed site
25
Low TI
20%
7
8
9
Wind Speed [m/s]
10
11
Low TI case:
• 3% drop in nominal energy during periods of
low TI (<8%), which corresponds to stable
atmospheric conditions
Conclusions
• Real world turbine performance does generally deviate from sales power curves
• Causes can be grouped and quantified based on observations from operational analyses
Category
Typical range of
loss(-ve)/gain(+ve)
(nominal energy %)
Median
(nominal energy %)
1. Generic power curve performance
-5% to +3%
-1% (model specific)
2. Mechanical sub-optimal performance
-5% to +0%
-1% (operator specific)
3. Environmental
-3% to -0.2%
-0.5% (region specific)
4. Wind conditions – turbulence intensity,
shear and flow inclination
-5% to +1%
-1% (site specific)
Questions?
Keir Harman
keir.harman@gl-garradhassan.com