Summer 2018 · MW). Comparing the previous five quarters, the number of service interventions was significantly lower. Wind turbine service duration (hours) per installed MW 0.00hrs

WIND ENERGY BENCHMARKING SERVICES

Sample Report Summer 2018

This is the WEBS sample report for summer 2018 working with data from January to March. This report includes quarterly indices on production, availability and reliability. The deep dive this quarter focusses on scheduled maintenance trends to reveal how wind turbine annual service has evolved over a 10-year period.

QuarterlyIndices


Sample Report: Summer 2018

Data volume this quarter

Installed capacity by year

The current age profile of the wind farms in the WEBS population

856Reporting MW

1340Reporting months

384Turbine count

23Wind farm count

5Countries

0

200

400

600

800

1000

0

200

400

600

800

1000

Total201720162015201420132012201020092008

Increase Total

Tota

l ins

talle

d ca

paci

ty (M

W)

When the farms in the WEBS population started reporting data

0

5

10

15

20

25

5 5-10 10-20

Win

d fa

rm c

ount

Years of operation

0

2

4

6

8

10

2018 Qtr 12017 Qtr 42017 Qtr 32017 Qtr 22017 Qtr 1

Hub

-hei

ght w

inds

peed

(m/s

)

7.03

6.06

7.387.11

5.69

Interpretation:Capacity factor and availability trends over the past 15 months. Each quarterly KPI shown is an average of monthly wind farm values provided by the WEBS population.

Compared to the same quarter last year, capacity factor is up 0.2% and production-based availability is down 0.3%.

Production and Availability Mean hub-height windspeed

Insights: This capacity factor increase is primarily due to an increased hub-height wind speed to an average of 7.11m/s; up 0.08m/s from the same quarter in 2017. This increase in capacity factor would, in normal circumstances, lead to a similar increase in availability. However, in this case the rise in the downtime due to forced outages has reduced availability in this quarter; compared to the same quarter in the previous year. A breakdown of these forced outages is provided through the analysis of failure rates and downtime per failure.

0

5

10

15

20

25

30

35

40

Capacity factor Production based availability

2018 Qtr 12017 Qtr 42017 Qtr 3

Capa

city

fact

or (%

)

Avai

labi

lity

(%)

2017 Qtr 2201t Qtr 1

90

92

94

96

98

100

Time weighted run time availability

35.136.921.725.4

96.3

34.8

96.2

96.9

96.8

95.9

96.1

96.0

96.4

96.3

96.7

Sample Report: Summer 2018WIND ENERGY BENCHMARKING SERVICES

2017 Qtr 1

2017 Qtr 2

2017 Qtr 3

2017 Qtr 4

2018 Qtr 1

0K 2K 4K 6K 8K 10K 12K 14K 16K 18K

Ancillary system

Balance of plant system

Blade adjustment system

Central hydraulics system

Control and protection system

Drive train system

Generator system

Equal potential bonding system

Other subsystem

Rotor system

Structure and machinery enclosure system

Transmission system

Unknown subsystem

Yaw system

Sum of downtime (hours)

A breakdown of these forced outages is provided through the analysis of failure rates and downtime per failure. The main contributors to this increase in downtime, from the previous quarter, are the rotor system, balance of plant and the yaw system. This increase in downtime could be an outcome of particularly unusual weather activity over the winter months of 2017 and early 2018.

Failure statistics


Interpretation:This chart shows downtime due to major component repairs across the WEBS population this quarter. These values have been normalised with respect to the installed capacity of each wind farm to provide a more meaningful benchmark.

This quarter there were 11 major component repairs (repair of a major component with over two weeks of downtime).

Rotor and cable repairs accounted for approximately 19% of downtime each, with main bearing (14%) and gearbox (16%) failures accounting for significant downtime also.

Insight:These 11 major system repairs were conducted over four wind farms. The site that conducted the majority of these major system repairs experienced significant effects on performance. The repair activity at this site led to over 80


Ancillary system

Balance of plant system

Blade adjustment system

Control and protection system

Drive train system

Generator system

Equal potential bonding system

Other subsystem

Rotor system

Structure and machinery enclosure system

Transmission system

Unknown subsystem

Yaw system

turbine downtime days. Compared to the same quarter in the previous year, this wind farm’s capacity factor dropped by 4%, with a reduction in production-based availability of 5%.

Across these four sites, the capacity factor dropped 6% on average (from the same quarter in 2017). Similarly, time weighted run time availability and production-based availability both dropped by 3% on average. For a wind farm with an installed capacity of 100 MW, a reduction in production based availability of 3% can translate to around 2 GWh of lost energy production.

Quarters 1 and 4 generally have the highest average wind speeds throughout a year and are therefore not preferred for conducting major system repairs that can lead to extended periods of downtime. These results show that unforeseen failures, which require heavy lift cranes on site for repair, can severely degrade the production and availability of individual turbines and wind farms as a whole.

Reliability: Downtime (hours) due to major system repairs per MW

Rotor 363.33

Electrical 95.97

Cables 347.99

Other 484.57

Gearbox 306.55

Main bearing 263.14

Majorcomponent

repairs

Central hydaulics system

Sub-system failure rates and downtime per failure

Interpretation:It should be noted that unlike previous studies into failure rates of wind turbine systems, failure rates in this report are high due to ‘forced outages’ being used as a measure of failure. With a forced outage defined as “an immediate action to disable the generating function of the WTGS, required as unforeseen damage, faults, failures or alarms are detected”.

The tornado chart presents forced outage information collected from the WEBS population this quarter. For each wind farm component in the taxonomy, the blue bars on the left show the failure rate as the average number of forced outages per turbine per month, while the green bars on the right show the average downtime per forced outage.

5,736Number of forced

outages

Failure rate (avg. forced-outages/turbine/year) Downtime per failure (downtime(h)/forced-outage)

Unknown

Equal potential bonding

Generator

Other

Ancillary

Balance of plant

Rotor

Central Hydraulics

Drive train

Control and protection

Yaw

Blade adjustment

Structure and machinery enclosure

Transmission

0.00

1.80

0.00

1.81

1.82

1.94

2.38

3.66

4.76

6.42

10.99

11.44

15.50

20.05

0.00

2.91

0.00

4.32

2.91

16.34

12.72

1.96

9.87

3.83

2.57

3.09

0.94

2.11



Failure rate and downtime

Downtime per failure

0

5

10

15

20

25


Failure rate (avg. forced-outages/turbine/year) Downtime per failure (downtime(h)/forced-outage)

2.01.5

2.81.8 2.0

23.7 24.0

12.2

20.6

15.7

Dow

ntim

e pe

r fai

lure

(h)

0

1

2

3

4

5

6

7

8

9


Blade adjustment Structure and machinery enclosure Transmission

1.9

1.2

2.9

3.0

2.4 2.6

2.1

2.1

3.1

0.91.2

2.7

1.0

3.4


Interpretation:The above graph shows the downtime and failure rates over the last year for the top three most occurring failures: transmission system, structure and machinery enclosure system and the blade adjustment system. (Q3 has been an unfortunate period for some farms in terms of downtime, with the majority of the structure and machinery downtime only being caused by two farms.)

Insights: This quarter there were 5,736 sub-system failures across the population. The highest failure rate was associated with the transmission system, but on average these stoppages cause very little downtime. These transmission failures consist of converter, transformer, unknown and other systems (where a clear identification of the cause of the fault should be investigated further).


Failure rate per sub-system

0

10

20

30

40

50


Failu

re ra

te (a

vg. f

orce

d-ou

tage

s/tu

rbin

e/ye

ar)

Blade adjustment Structure and machinery enclosure Transmission

18

18

35

46

1414

27

19

16

11

16

20

11

12

12

Scheduled maintenance (annual service)

Interpretation:The blue gauge shows the average monthly downtime due to wind turbine service using data collected this quarter. This value has been normalised by installed capacity to provide a more meaningful benchmark. The grey inset mark indicates the same metric for the equivalent quarter in the previous year.

Insight:There were 610 interventions due to wind turbine service across the population this quarter, translating to an average of 14.7 hours per installed MW of downtime due to wind turbine annual service per month. This is lower than for the same quarter in 2017 (16.92 hours/service/MW). Comparing the previous five quarters, the number of service interventions was significantly lower.

Wind turbine service duration (hours) per installed MW

0.00hrs

Average

14.7hrsQ1 2018

Average

16.92hrsQ1 2017

A monthly average of

14.7hrsper installed MW of downtime

due to wind turbine annual service

Questions must be asked of these results, however; specifically how such decreases have occurred?

Initial interpretation of these results would suggest that servicing has been underutilised or that the turbines are operating better than expected. Considering all results from this report, it is evident that increased downtime due to forced outages and major system repairs has led to less service activity (given that addressing unforeseen forced outages will typically be prioritised over scheduled maintenance such as annual service) and have therefore had a knock-on effect to the service schedules of wind farms.

This is evident when assessing the total downtime of the assets, where downtime due to forced outages, major system repairs and serviced interventions are aggregated.


This deep dive analysis draws on data from the full WEBS database, spanning over ten years, to reveal trends and insights for specific aspects of wind farm performance.

Key question: are scheduled maintenance trends emerging and what insights/recommendations can be extracted from these trends?

Deep DiveScheduledMaintenance

Seasonal variation

Interpretation:The blue/green bars show the average number of wind turbine service visits per turbine and the red line shows the average downtime due to wind turbine service per turbine. All WEBS data has been combined in such a way that all January values are combined across multiple years, and similarly for all February values across multiple years etc. The purpose being to reveal seasonal variations.

Insight:There is a slight upward trend in both occurrence and duration of wind turbine service throughout the year. An optimised service schedule would have more visits and longer time servicing in summer when wind is low so that the turbines are ready for the strong and profitable winds in winter, however it is interesting that this is not observed in the data. Service does not appear to be optimised which represents a missed opportunity across the population.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

0.5

1

1.5

2

2.5

DecNovOctSepAugJulJunMayAprMarFebJan

Aver

age

occu

renc

ies

per

turb

ine

Aver

age

serv

ice

dura

tion

(hou

rs p

er tu

rbin

e)

Visits Duration


610Number of windturbine serviceinterventions

Previous quarters2018 Qtr 1

2017 Qtr 1

1106

2017 Qtr 3

1497

2017 Qtr 2

1591

2017 Qtr 4

1139

0K

5K

10K

15K

20K

25K

30K


Service interventions Major system repairs Forced outages

Sum

of d

ownt

ime

(hou

rs)

Total categorised downtime

Impact of turbine size

Interpretation:The WEBS wind farm population has been separated into groups based on the turbine rating in the farm. The blue/green bars show the average normalised downtime due to wind turbine service per month. The red line shows the average normalised lost production due to wind turbine service per month. All values have been normalised by installed capacity.


Impact of number of turbines in site

Interpretation:The WEBS wind farm population has been separated into groups based on the number of turbines in the farm. The blue bars show the average duration of wind turbine service per turbine and the green bars show the average number of service visits per month. For both, there is a dotted trend line.

Insight:As the size of wind farms increase in terms of number of turbines, the turbines are being stopped fewer times on a per turbine basis for annual service. This could be an indication of more efficient operations or a sign that maintenance resource is not scaling up with the increase in turbine numbers leading to a stretch of technical resources. At the same time, more time is spent per turbine on carrying out annual service. But to identify if the increase in service downtime is scaling well with the increase in wind farm size, a deeper analysis is required. The next chart focuses on wind farms with similar technology (2-3 MW wind turbines) and presents average monthly number of hours of wind turbine service per MW for a new set of groups based on number of wind turbines at the farm.

Insight:Service duration per MW is decreasing as turbine rating increases, revealing that as wind farms deploy larger turbines, less time is being spent servicing wind farms on a per installed MW basis. Lost production per MW is increasing as turbine rating increases. In particular, there is a significant increase for turbines exceeding 3 MW rating. This suggests that more efficient scheduled maintenance is increasingly important for large turbines for minimising lost production.

0.0

0.5

1.0

1.5

2.0

2.5

3+ MW2-3 MW1-2 MW0-1 MW

0.0

0.2

0.4

0.6

0.8

1

1.2

1.4

Nor

mal

ised

ser

vice

dur

atio

n (h

ours

per

MW

per

mon

th)

Lost

pro

duct

ion

(MW

h pe

r MW

per

mon

th)

Turbine rating

Average of per MW service hours Lost production per MW (MWh)

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

45+30-4415-290-14

Average service site visits Average duration (hrs)

Aver

age

serv

ice

dura

tion

(hou

rs p

er tu

rbin

e pe

r mon

th)

Aver

age

num

ber o

f ser

vice

vis

its p

er tu

rbin

e pe

r mon

th

0

0.5

1.0

1.5

2.0

2.5

3.0

Number of turbines in site


Service duration per MW (grouped by wind farm turbine count)

Targeted analysisThis targeted analysis only includes wind farms from the WEBS population with similar technology (2-3 MW turbines). The chart reveals that wind farms with larger numbers of turbines require significantly more downtime to allow wind turbine service even when the same technology is being used. So for example, a wind farm with five 2.5 MW wind turbines will spend approximately 7.5 hours per month for annual service of one of the turbines whereas a wind farm with 30 of the same turbines will spend approximately 42.5


Long term trend

Interpretation:The WEBS data has been grouped by the calendar year of operation of the wind farm. The blue/green bars show the average monthly downtime due to wind turbine service. This value has been normalised by installed capacity. The red line is the average annual availability for that year of data provision.

Insight:There is a strong trend towards more service on a per MW basis over the last decade. The data shows that a 30 MW wind farm in 2010 received six hours of annual service per month across the farm. Whereas the same wind farm in 2016 received 36 hours of annual service per month. This is an alarming increase of 500%. Across the same period, average availability is remaining consistent at just above the 95% mark. This demonstrates that over the last decade, an increased servicing effort has been required in order to maintain levels of performance. The key drivers of this are the larger farms coming online that require more service and an aging population of technology.

hours per month for annual service of the same turbine technology. The scale of this difference is unexpected and is clear evidence that larger wind farms are taking much longer to carry out the same service tasks. Smaller wind farms can benefit from very targeted maintenance and a clear focus on completing annual service when it is due without postponing work due to unforeseen forced outages.

0

2

4

6

8

10

12

14

16

18

26+15-257-143-6

Turbine count (groups)

Serv

ice

dura

tion

per M

W (h

ours

)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

80

82

84

86

88

90

92

94

96

98

100

20172016201520142013201220112010

Service duration (hours) Wind farm availability (%)

Avai

labi

lity

(%)

Aver

age

serv

ice

dura

tion

(hou

rs p

er M

W p

er m

onth

)


Who is WEBS?WEBS is an independent performance benchmarking company. We apply a suite of web-based tools to assist wind farms owners and operators around the world to improve performance and reliability whilst driving down the cost of asset operations and maintenance.

WEBS provide a secure, anonymised, industry level, independent web-based benchmarking service for wind farms across several key performance indicators.

Production

• Capacity factor

• Lost production

Availability

• Production (energy) based availability

• Time based availability


Dimensional analysisBenchmarks can be sliced and diced by the following dimensions to provide comparisons against representative wind farms:

Reliability

• Major component repairs

• Sub-system failures (forced outages)

• Scheduled maintenance (annual service)

Logistics

• Number of people

• Accessibility

• Weather

KPI’s: WEBS provides industry benchmarks for:

Download our Brochure (pdf)

Benefits of benchmarking:Benchmarking is widely recognised as a critical tool in developing an effective improvement strategy within mature industries and by benchmarking your wind farm with WEBS, you will get an enhanced asset-level performance analysis; comparative rankings to show your assets position against your industry peers and leading performers.This information can be used as a boardroom dashboard, operational portfolio or analytical reporting tool to help drive performance and efficiency.By subscribing to our independent, anonymised, benchmarking programme, your company will be able to improve by:

• Accessing independent anonymised benchmarking data for the wind industry

• Developing targeted operational improvement plans and actions

• Optimising costs by recognising more efficient practices

• Increasing effectiveness through lessons learned

• Enhancing understanding of asset management practices in wind energy

DimensionalAnalysis

• Online date

• Turbine OEM warranty status

Farm size: Installed capacity .Farm size: Number of turbines .

Height above sea level .Turbine class .

Wind speed class .

• OEM

• Turbine type

• Turbine rated capacity

• Turbine rotor size

• Turbine service lift

• Region

• Country


Subscriber benefits

• Monthly refresh of all the benchmarks

• Graphical visualisations of key metrics

• Anonymised data behind the benchmarks provided should further analyses be desired

• Periodic analysis of the data under management that provides additional insight into trends and patterns across the performance, availability reliability and maintenance domain

• Option of requesting additional, bespoke analyses from the WEBS team (WEBS advisors will, given anonymity and security constraints, seek to answer the query on behalf of the subscriber)

• Preferential access to advisory services

WEBS also provides subscribers with a periodic analysis of the data under management. This “state of the nation” for wind farm operations and maintenance will provide periodic reports into trends and patterns across the performance, availability reliability and maintenance domain.

Subscribers also have the option of requesting additional analyses from the WEBS team. This “ask the advisor” service provides subscribers with the ability to request bespoke analyses.


In partnership with

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Contact:Jeff Bryan: [email protected]

Steve Ross: [email protected]

Documents

Summer 2018 · MW). Comparing the previous five quarters, the number of service interventions was significantly lower. Wind turbine service duration (hours) per installed MW 0.00hrs