01 iea-task33 intro&recommendations

Implementing Agreement for Co-operation in the Research, Development, and Deployment of Wind Energy Systems

Data collection and reliability assessment for O&M optimization of wind turbinesBerthold Hahn, Fraunhofer IWES, Operating agent of IEA Wind Task 33Dr. Thomas Welte, SINTEF Energy researchProf. Dr. Gerard van Bussel, Delft University of Technology

New IEA Windrecommended practices

WindEurope 2016 side event, 27th Sep, Hamburg, Germany

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Data collection and reliability assessment forO&M optimization of wind turbines

Agenda

• Introduction to IEA Wind and task 33

• Detailed findings on analyses and data

• Recommendations to operators and wind community

IEA Wind

International Energy Agency (IEA) Implementing Agreement for Co-operation in the Research, Development, and Deployment of Wind Energy Systems - IEA Windwww.ieawind.org

• Founded in 1974• 20 member countries• Cooperative research • Forum for international

discussion of R&D issues

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B. Hahn, Fraunhofer IWESM. Asgarpour, ECN Energy research Centre of the NetherlandsP. Bangalore, Chalmers University GothenburgS. Faulstich, Fraunhofer IWESK. Harrisson, ORE CatapultB. Karlson, Sandia National LabsF. O’Connor, ServusNetL. Pettersson, Vattenfall SwedenC. Soraghan, ORE CatapultC. Stock-Williams, ECN Energy research Centre of the NetherlandsJ. Dalsgaard Sorensen, DTU / University AalborgG. van Bussel, Delft University of TechnologyJ. Vatn, NTNU University TrondheimT. Welte, SINTEF Energy Research

Main contributors

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IEA Wind Task 33

Task 33 - Reliability Data:

Standardizing data collection for wind turbine reliability and O&M analyses

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Goal of IEA Wind Task33

New Recommended Practices answering the questions: Which information do

operators and other stake holders need?

What analyses can provide the requested information?

Which data has to get recorded to feed these analyses?

for

WIND FARM DATA COLLECTION AND

RELIABILITY ASSESSMENT FOR O&M OPTIMIZATION

1.

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The Task 33 Recommended practices do not intend to:

- Deal with health and safety issues- Deal with structural reliability testing- Provide the best reliability data base- Provide the standard for data collection

IEA Task 33 Reliability Data

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Task 33 – Participants

Country OrganisationChina Chinese Wind Energy Association – CWEA

Goldwind Science & Technology Co., Ltd.Denmark DTU University Denmark / University AalborgFinland Technical Research Centre of Finland – VTT France Maia EolisIreland ServusNet InformaticsGermany Fraunhofer Institute for Wind Energy and

Energy System Technology – IWES Netherlands Delft University of Technology – TUDelft

ECN Energy research centre of the NetherlandsNorway Norwegian University of Science and Technology – NTNU

SINTEF Energy ResearchSweden Chalmers University of Technology

Vattenfall Research and Development ABUK Offshore Renewable Energies Catapult

AtkinsglobalUSA Sandia National Laboratories

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Management of O&M data

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3

4 1

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Wind energy plant(s)

controller/ SCADA technical operation

Automatic data acquisition

Collection of data during maintenance & inspection

O&M Management

WInD-Pool

Interfaces of datacommunication

service/ maintenancetechnician

laboratory

Commercial operation

forecast (wind& weather)

component supplier

plant manufacturer

independentauthorised expert

analysis for optimising design and optimising the O&M-process

technical data

commercial data

Standards on:- Component designation- Failure/fault description- Defined data sets- Communication protocol

Data analysts /Shared database

IEA Wind Task 33Recommended Practices for Reliability

Data

StandardsTaxonomies

DataEntries______________________________

Data Groups

Roles

ObjectivesAnalyses

From roles to taxonomies

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Complexity Level

Possibleapplication

Possibleanalyses

Neededdata groups

Requirement for organizational foundation of reliability

APerformance

Availability

Statistical calculations

Simple plots

Equipment data

Operational data & measurement values

Assessment of assets is recognized as important.

BPlus:

Root cause analysis

Fault-tree-analysis

Pareto-analysis

Basic physical models

Plus:

Failure data

Reliability is recognized as important, some processes around reliability exist

C

Plus:

Design optimization

Maintenance optimization

Degradation monitoring

Degradation models

Advanced physical models

Maintenance and logistics optimization

Data mining

Vibration analysis

Optimized renewal

Optimized stock-keeping

Plus:

Maintenance and inspection data

(Costs)

A clear and formal reliability process is defined and regularly reviewed with stakeholders

Levels of complexity

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An Operator

has to report KPIProduction based availability

Operational data needed

Suggested taxonomie(s)

Basic calculations necessary

IEC 61400-26

has to report KPILosses due to component

has to report KPILosses due to part and failure

+RDS-PP, GADS +ZEUS, ISO 14224

Additionally equipment data needed

Additionally failure data needed

Finding individual solutions, level A + B

Complexity level A Complexity level B

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An Operator

Detailed equipment data, operational data, failure data and maintenance data needed

Suggested Taxonomies IEC 61400-26 +RDS-PP, GADS

wants to optimize maintenance by grouping preventive measures

Modelling failure behavior needed

f(x)

+ZEUS, ISO 14224

Finding individual solutions, level C

Complexity level C

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Data processing and pre-processing

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Model class Description(Frequency-based)Stochastic models (SM)

• Based on probability theory and statistical methods

• Does not represent a physical process or mechanism

Physical models (PM)

• Models of physical mechanism (failure mechanism) or process (failure process) that finally leads to failure

Data-driven models and machinelearning (ML)

• Models are based on methods to identify abstract information and/or relationships from large sets of data

• Different techniques used such as classical statistics models, kernels and classification methods and classification methods (ARIMA, Gaussian Processes, Artificial Neural Networks – ANNs, random forests).

• Well suited for data from continuous monitoring (time series data)

Model classes

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 1 2 3 4

MTBFλ

E(T)

2010-07-01 2010-08-01 2010-09-01 2010-10-01

25

30

35

40

(a)

Turbine Bearing Temperature (t-1)

2010-07-01 2010-08-01 2010-09-01 2010-10-010

5

10

15

(b)

Turbine Speed

2010-07-01 2010-08-01 2010-09-01 2010-10-010

10

20

30

(c)

Nacelle Temperature

2010-07-01 2010-08-01 2010-09-01 2010-10-010

1000

2000

3000

Active Power

(d)

Time (yyyy-mm-dd)

2010-08-01 2010-09-01 2010-10-01 2010-11-01 2010-12-01 2011-01-01 2011-02-01 2011-03-01

10

20

30

40

50

60

(a)

Actual and Estimated Temperature

EstimatedTempBearTemp

2010-08-01 2010-09-01 2010-10-01 2010-11-01 2010-12-01 2011-01-01 2011-02-01 2011-03-01-5

0

5

10

15Difference Between Actual and Estimated Temperature

(b)

Time (yyyy-mm-dd)

4

52 3

1

4

1.5

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Factors influencing model choice

System complexity

System novelty

Prediction horizon

Quantitative vs. qualitative

Single vs. multiple faults

Single vs. population of components

Time or sequence dependent behavior

Usability for dependent events

Bottom-up vs. top-down

Level of experience requiredAcceptance and commonality of method

Need for and availability of tools

Standardization

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Prediction horizon and size of population

Modelshort term prediction horizon long term

<<MTTF < MTTF MTTF ≥ 2 MTTF

Stochastic models (SM)SM: Failure rate modelsSM: Lifetime distributions

SM: Stochastic degradation models

Physical modelsMachine learning

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Prediction horizon and size of population

Modelshort term prediction horizon long term

<<MTTF < MTTF MTTF ≥ 2 MTTF

Stochastic models (SM)SM: Failure rate modelsSM: Lifetime distributions

SM: Stochastic degradation models

Physical modelsMachine learning

Model

small size of population largesingle unit,item

group, population

Stochastic models (SM)SM: Failure rate modelsSM: Lifetime distributions SM: Stochastic degradation modelsPhysical modelsMachine learning

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Model classes – main differencesPM SL ML+ White-box model

+ Clear meaning of model parameters

- Problem-specific model

- Challenging if good model is not available

+ General model

+ Takes uncertainty into account

+ Requires a group consisting of comparable items

- Parameter estimation requires observations related to lifetime/reliability

+ General model

+ Large data sets

+ Short-term predictions

+ Identification of faults

+ Fault prediction and diagnosis

- Often black-box model

- Requirements of qualitydata

- Few examples on lifetime prediction and reliability estimation

?

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+ General model




+ General model

+ Large data sets







Glass

Granite

h

t

g=9.81

ω

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+ General model




+ General model

+ Large data sets







Drop it!

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+ General model




+ General model

+ Large data sets







Drop it!

And again!

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+ General model




+ General model

+ Large data sets







Drop it!

And again!

And again!

......

Pdest=...

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+ General model




+ General model

+ Large data sets







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Summary models

Many different models No model serves all purposes Choice of model depends on different factors Difficult to recommend specific models

Overview of different model classes Typical application areas Typical input data requirements Recommendations on model choice

Recommended practices provide:

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Suggestions for improved reliability analysesType of model Comments/examples

Stochastic failure rate models for non-constant failure rates (ROCOF)

Models for repairable componentse.g. non-homogeneous Poisson process (NHPP)

Lifetime distributions with non-constant failure/hazard rates (FOM)

For non-repairable componentse.g. Weibull distribution

Degradation modelsFor components with observable degradation.e.g. Markov or gamma process,

Physical models

For components where we have a good understanding of the physical mechanisms leading to failuree.g. Paris' law

Data-driven models and machine learning

For (continuous) condition monitoring datae.g. Artificial Neural Networks (ANN)

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Data groups and sub-groupsData groups Sub-groups / objects

Equipment data (ED)IdentificationTime dataTechnical information

Operating data /measurement values (OP)

Time stampMeasurement values (SCADA, etc)Operational states

Failure / fault data (FD)

IdentificationTime dataFailure descriptionFailure effectFailure detectionFault properties

Maintenance & inspection data (MD)

IdentificationTime dataTask / measure / activityResourcesMaintenance results

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Data group equipment data

Equipment data (per hierarchical level of the plant)Sub-groups/objects Entries Complexity levelIdentification

Identification code A, B, CCoordinates, location A, B, C

Time dataStart of operation A, B, CStart of observation C

Technical informationOEM A, B, C Type A, B, C Serial number B, C Design data relevant for each equipment class and item

B, C

Maintenance manual C

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Data groups /taxonomies Equipment data

Operating /measurement

data

Failure data

Maintenance & inspection data

VGB RDS-PP® o

NERC GADS o - -

ReliaWind o

ISO 14224 o* +* +*

FGW ZEUS o + +

IEC 61400-25 +

IEC 61400-26 o

Standards covering data groups and entries

+ entries with a high level of detailo entries with a medium level of detail- entries on a more general level* not wind-specific

Summary data groups and taxonomies

No combination of the taxonomies gives a complete set of data entries

ISO 14224 is very comprehensive, but not wind specific and not complete

Missing entries in ISO 14224 can get completed by wind-specific guidelines such as ZEUS

RDS-PP and GADS provide component designation systems IEC 61400-25 (communication), -26 (availability) are recommended to

apply

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Main recommendationsTo developers / owners / operators1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis

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To developers / owners / operators1. Make sure you get all data during contract negotiation

- high value for wind farm asset management and operations 2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis

Main recommendations

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To developers / owners / operators1. Make sure you get all data during contract negotiation 2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators1. Make sure you get all data during contract negotiation 2. Identify your use-case and be aware of the resulting data needs

- design comparison (developer)- (non) availability assessment- grouping of maintenance tasks- monitoring of degradation processes

3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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- design comparison- (non) availability assessment (owner/ operator)- grouping of maintenance tasks- monitoring of degradation processes



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- design comparison- (non) availability assessment- grouping of maintenance tasks (operator)- monitoring of degradation processes (operator / asset engineer)



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To developers / owners / operators 1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators 1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for

- motivates - shows benefits of systematical data collection and evaluation- saves time and effort in the longer run

4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators :1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system

- IEC 61400-25- RDS-PP- GADS

5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-26

- time- and production-based availability assessment 6. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis

Name IEC 61400-26Intended application

Common understanding of operational periods during lifetime of a wind turbine and suggestions for availability calculation

Domain Wind energy

Scope: Wind turbine

Objects Operational states

Granularity: 14 mutually exclusive operational states are defined and spread across 5 levels of hierarchy.

Rule for dividing

Each operational state has well defined entry and exit points within the standard.

Flexibility Generic names are used for the operational states that are not tied to specific turbine models.

Reference https://webstore.iec.ch/publication/5445


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To developers / owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means

- reduces human errors, improves data quality & data analysis7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basis


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To developers / owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basisTo wind industry in general:8. Develop comprehensive wind-specific standard based on existing

guidelines/standards, such as ISO 14224-2006 & FGW ZEUS9. Develop component- / material-specific definition of faults, location,

and severity


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To owners / operators:1. Make sure you get all data during contract negotiation2. Identify your use-case and be aware of the resulting data needs3. Train your staff understanding, what data collection is helpful for4. Map all WT components to one taxonomy / designation system5. Align operating states to IEC 61400-266. Support data quality by making use of computerized means7. Share reliability data to achieve a broad statistical basisTo wind industry in general:8. Develop comprehensive wind-specific standard based on existing


and severity

No Hierarchical levels Exemplary items

1 Plant Wind power plant

2 System Wind turbine

3 Sub-System Drive train

4 Assembly Shaft assembly

5 Maintainable item Bearing

6 Part Roller

Wind specific following industry standard ISO 14224


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and severity- evaluate initial defects and propagation, identify fault mechanisms, & enable an estimation of residual life time


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and severity


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IEA Wind Task 33

Thank you

[email protected]

01 iea-task33 intro&recommendations

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