Advanced wind turbine health monitoring from … wind turbine health monitoring from SCADA...

Advanced wind turbine health monitoring from SCADAmonitoring from SCADA

R li Wi d S G j i id ReliaWind - SuperGen joint side event

Dr. VIHAROS Zsolt JánosHead: Manufacturing and business processes groupg p g [email protected], www.sztaki.hu/~viharos

Engineering and Management Intelligence (EMI)Engineering and Management Intelligence (EMI)MTA SZTAKI

4/19/2010

AgendaAgenda

•Introduction

•The ReliaWind project•The ReliaWind project

WP3 Al ith L i l A hit t f •WP3: Algorithms-Logical Architecture of an Advanced WTG Health Monitoring System• Goals• Results by result groups

•Summary picturey p

4/19/2010 Dr. Zsolt János VIHAROS: Advanced wind turbine health monitoring from SCADA 2

Computer and Automation Res. Inst., HAS (SZTAKI)

EU Centre of Excellence i I f ti

Main Research TopicsKey figuresin Information

Technology, Computer Science and Control

Mathematics and CS• Combinatorial CS• Operations research

y g

Budgetp

• Modeling multi-agent systems• Stochastic systems

Information technology

15 M euros34% basic funding

• Basic and applied research in the field of mathematics, CS, IT Information technology

• Analogic & neural computing• Distributed systems • Cluster and GRID computing

Staff

295

mathematics, CS, IT and automation

• Contract based R&Dactivity mainly on p g

• Component and agent-based prog.• Embedded systems• Human-computer interactions

29567% scientific

complex systems, turn-key realizations

• Transferring up-to-date results and research

Automation• Systems & control theory• Geometric modeling & reverse eng.

Cost structure

personnel 38 %

results and researchtechnology to industry and universities

• Intelligent manufacturing• Digital Enterprises, prod. networks

operational 51 %investments 11 %

SZTAKI’s role in Hungary

• The only research institute on IT in the country

g y

in the country• Key role in national research

projects in CS, IT and controlP ti i ti i th d t d • Participation in the graduate and postgraduate education

• Contract-based projects with the l t i d t i l fi i largest industrial firms in Hungary (GE, Nuclear Power Plant Paks, MOL, Knorr Bremse)C t t ki g g th • Computer networking: e.g the largest regional center

• Supercomputing CentreANN model(s)

Search foroptimal solutions

Near tooptimal

solution(s)

Optimisation fordetermining optimal

solution(s)

• GRID Competence CentreANN model(s)replacing the

simulation

Plant

Simulation

ANN learning

Addition of thetested solution(s)

to the trainingset of the ANN

Performancecalculation(s)

Parameters tobe optimised

Performancecalculation(s)

Parameters tobe optimised

Simulation based testing

Training parameters

Inputs

Endsolution

International cooperation

• ERCIM, WWW ConsortiumCIRP IFAC IFIP t • CIRP, IFAC, IFIP, etc.,

• > 30 projects in the 5th Framework Programme

• > 30 projects in the 6th Framework Programme

• > 25 projects in the 7th 25 projects in the 7th Framework Programme

• NSF, ONR, ARO projectsC t t b d k• Contract-based work

• Virtual Institute with IPA-Fraunhofer, Germany

Engineering and Management Intelligence Laboratorywww.emi.sztaki.hu

EMI’s missionResearch and elaboration of techniques applicable Research and elaboration of techniques applicable

for handling complex production and businesssystems working in an uncertain, changing

environment, with special emphasis on artificial intelligence and machine learning approaches.

EMI’s present activities•Intelligent manufacturing processes and systems•Management of complexity, changes and disturbances in productionproduction•Production planning and scheduling A t b d t l t t •Agent-based control structures

•Production networks, supply chain information systemsTh di it l f t f t l i d l i ti •The digital factory, factory planning and logistics

management•Quality management modelling and optimisation of •Quality management, modelling and optimisation of process chains•Data mining (production related databases)g (p )

•Department of Manufacturing Science and Technology, Budapest University of Technology and EconomicsBudapest University of Technology and Economics

ReliaWind: Reliability focused research on ti i i Wi d E t d ioptimizing Wind Energy systems design,

operation and maintenance: Tools, proof of concepts guidelines & methodologies for aconcepts, guidelines & methodologies for a new generation• Identify Critical Failures and Components Understand • Identify Critical Failures and Components, Understand Failures and Their Mechanisms

•Define the Logical Architecture of an Advanced Define the Logical Architecture of an Advanced WTG Health Monitoring System – Leader: SZTAKI

• Failure -mitigation, -detection, -location, and -prognosis, i t ti d l i d i i tmaintenance generation and planning, decision support


ReliaWind partnersp

•Wind turbine and wind farm producers

•Component manufacturers

•Research institutions engineering consulting•Research institutions, engineering, consulting

•Wind farm owners and other companies: as members of the End user panel”members of the „End user panel”

ReliaWind stepsReliaWind steps

•Identify Critical Failures and Components•Understand Failures and Their Mechanisms•Define the Logical Architecture of an Advanced WTG H lth M it i S tWTG Health Monitoring System•Demonstrate the Principles of the Project FindingsT i t d th Wi d E t •Train partners and other Wind Energy sector

stakeholdersDisseminate the achieved new knowledge through •Disseminate the achieved new knowledge through

Conferences, Workshops, Web Site and Media

System architectureSystem architecture

Planned (to-do) maintenance activities

Scheduler Management aspects

Possible maintenance activitiesPreventive

maintenance

Failure

Structure, ReliabilityStructure, ReliabilityStructure, ReliabilityStructure, Reliability Detected

Failure prognosis

Failure & probability

Statistical Analysis

Maintenance/

Reliability

ReliabilityModel

Mainte-nance


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel

Mainte-nanceMainte-nance

Detectedfailure and location

Failure (probability=1) Failure detection and prognosis

Failure effect mitigation

Maintenance/Event dataMaintenance/Event dataMaintenance/Event dataMaintenance/Event data

19/04/2010 Overview of the overall scope of Work Package 3 11

mitigation(control)

System architectureSystem architecture

Task 3.6: Planning for Maintenance Activities

Task 3.7: Support for Maintenance Decision Making

• developing maintenance decision making tools• algorithms will be tested in real conditions

Task 3.0: Overall Integration Assurance

• Guarantee overall consistency of monitoring architecture


Scheduler Management aspectsTask 3 5: Generation of Maintenance Activities

g

• planning maintenance activities• algorithms will be tested in real conditions

• Ensure the harmonious and mutually beneficial development of tasks


maintenance

Failure Task 3.4: Agents for Fault Prognosis

• provide a prognosis for the incipient failure

Task 3.5: Generation of Maintenance Activities

• generating maintenance activities to effect improvements to the availability of the wind turbine• algorithms will be tested in real conditions


Failure prognosis


Task 3.3: Agents for Failure Location

provide a prognosis for the incipient failure modes• the algorithms attempt to predict the residual life and reliability of the components• on the wind turbine and wind farm• algorithms will be tested in real conditions


Maintenance/

Reliability

ReliabilityModel

Mainte-nance


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel




Task 3.2: Agents for Failure Detection

• detection of specific incipient failure modes• determination and identification which turbine system has progressed from a nominal to a malfunction condition• each turbine system will be adapted to each of the turbine system’s aims

• locate which turbine system has progressed from a nominal to a malfunction condition• integrated with the knowledge developed in T3.1 and T3.2.• on the wind turbine and wind farm• algorithms will be tested in real conditions

WP2


Maintenance/Event dataMaintenance/Event dataMaintenance/Event dataMaintenance/Event data Task 3.1: Mitigation of failures by control action

• drive designers towards more ‘intelligent’control algorithms• investigating the extent to which such advanced control algorithms might be extended to address reliability issues

b th l d l d i t l ill

the turbine system s aims• algorithms will be tested in real conditions

g


mitigation(control)

• both closed loop and supervisory control will be considered• algorithms will be tested in real conditions

3 0: Overall Integration Assurance3.0: Overall Integration Assuranceanalysis Business Workflows

The Workflow describes the diagrams processing informations of the Reliability Monitoring

«information»Manual records of

«information»O ti l R t

«information»

«information»Failure Forcast

Failure Forcast GenerationEvent1

Manual records of Maintenance

Operational Report Work Orders

«information»SCADA Data

«information»Failure Modes

Mitigation of Failure byControl

Mitigation of Failures byMaintenance

Failure DetectionMonitoring Signals

«information»Component Status

«information»«information»Pre enti e Maintenance Acti ities

«information»Sheduling Constraint Add-Hoc Maintenances

Maintenance Scheduling

Preventive Maintenance Activities Sheduling Constraint

«information»Maintenance PlanEvent2 Maintenance Plan

Decision support

13

Manager

Wind Turbine Logical NodesWind Turbine Logical Nodes

14

Critical components failuresCritical components, failures

•Critical components were selected in WP1(Id tif C iti l F il d C t ) b d (Identify Critical Failures and Components) based on field data

I ifi d b WP2 (U d d F il d Th i • It was verified by WP2 (Understand Failures and Their Mechanisms) using reliability models and calculations

Th 6 t iti l t l t d•The 6 most critical components were selected• The 5 most critical failures were selected for all of th iti l tthe critical components

6 t iti l t 5 t iti l f il 6 most critical components x 5 most critical failures

30 i i l f il id ifi d i id WP3= 30 critical failures were identified inside WP3


T3 1: Mitigation of failures by control actionT3.1: Mitigation of failures by control action

Specific objectives achieved:•Description of potential fault tolerance control algorithms.g•Assessment of controller mitigation actions for each failure mode when applicableeach failure mode, when applicable•Advanced control algorithms have beeninvestigated to address reliability issuesinvestigated to address reliability issues•Possible controller mitigating actions have b d ib d A l i f th t ti l been described. An analysis of the potential use of these mitigating actions on the most critical f il h b f dfailures has been performed.


T3 2: Agents for Failure DetectionT3.2: Agents for Failure Detection

•Main achievements• An advanced unsupervised learning system has been set up to facilitate automatic extraction of data from SCADA d ill i f id ifi bl f l SCADA and an illustration of identifiable faults. • An unsupervised learning method for SCADA alarm clustering has been developed clustering has been developed. • supervised learning method has been developed to create SCADA signal models and generalise detection create SCADA signal models and generalise detection algorithms, giving examples on Generator & Gearbox.• Non-conform situation detection algorithms were • Non conform situation detection algorithms were developed and non-conform situations were identified


DatasourcesDatasources


Alarm clusteringAlarm clusteringNo Expression 1 Expression 2 Expression 3

1 TOP TOP

2 GROUND GROUND

3 CONNECTIONS UPS CONNECTION3 CONNECTIONS UPS CONNECTION

4 LIGHT LIGHTING

5 RELAY AND BASE RELAY BASE

6 BLADE REPAIR BLADE REPAIRING BLADES REPAIRING

7 ELEVATOR REPAIR ELEVATOR REPAIRING

8 VIBRATIONS SENSOR VIBRATIONS SENSORS

9 YAW SENSOR YAW SENSORS

COMMUNICATION FAILURE GROUND COMMUNICATION10 COMMUNICATION FAILURE GROUND

GROUND COMMUNICATION FAILURE

11 GEARBOX FILTER REPLACEMENT GEARBOX FILTERS REPLACEMENT

12 LOW BUS VOLTAGE LOW VOLTAGE IN BUS



13 COOLING MOTOR FROZEN FROZEN COOLING MOTOR

Failure clustersFailure clusters


Maintenence driven failure detectionMaintenence driven failure detection


Non-conform situation detectionNon-conform situation detectionNon‐conform situation detection ‐ estimation of the gearbox bearing

temperature by a neural network modell (Model validity: ambient temperature between 4 and 10 C)

90

100

110

120

130

90

100

110

7%]

Values_for_Model_INPUT_2 Values_for_Model_INPUT_1

Gearbox bearing temperature_MODEL_ESTIMATES Gearbox bearing temperature_MEASURED

Ambient temperature (for model vaildity) Error_%

50

60

70

80

90

50

60

70

80

(%) [lim

it: +/‐17

peratures

0

10

20

30

40

20

30

40

50

stim

ation error (

Temp

‐30

‐20

‐10

0

10

Mod

el es

Time ‐ a year


T3 3: Agents for Failure LocationT3.3: Agents for Failure Location

•SCADA signals can be classified into operational signals and functional signals• SCADA operational signals, such as wind speed, shaft speed and power produced• SCADA functional signals, such as bearing and winding

d l b i i il temperatures and lubrication oil temperatures, pressure and pitch angle

•Location dependent•Location dependent

FMECA SCADA i l i d d •FMECA – SCADA signal mapping was needed to develop failure location algorithms


T3 4: Agents for Fault PrognosisT3.4: Agents for Fault Prognosis

•Reliability related estimation• Estimation of the residual life time of a component• …by considering cummulative SCADA data, too

• This ensures having concrete, field, turbine and component relevant estimation

Can be updated continuously• Can be updated continuously

•Statistical model building is possible based on past SCADA datap


T3 5: Generation of Maintenance Activities

•Preparation of a template

T3.5: Generation of Maintenance Activities

• for describing maintenance activities and• their circumstances

•This gives also the related database contentThis gives also the related database content

It can be used to feed the scheduler with the •It can be used to feed the scheduler with the maintenance assignments


T3 6: Planning for Maintenance ActivitiesT3.6: Planning for Maintenance Activities


The scheduler and a resultThe scheduler and a result


T3 7: Support for Maintenance Decision MakingT3.7: Support for Maintenance Decision Making

•A set of decision support reports


Advanced wind turbine health monitoring from SCADA


Scheduler Management aspects


maintenance

Failure No Expression 1 Expression 2 Expression 3

1 TOP TOP

2 GROUND GROUND

No Expression 1 Expression 2 Expression 3

1 TOP TOP

2 GROUND GROUND


Failure prognosis


3 CONNECTIONS UPS CONNECTION

4 LIGHT LIGHTING






10 COMMUNICATION FAILURE GROUND





3 CONNECTIONS UPS CONNECTION

4 LIGHT LIGHTING






10 COMMUNICATION FAILURE GROUND





Non‐conform situation detection ‐ estimation of the gearbox bearing temperatureby a neural networkmodell

Non‐conform situation detection ‐ estimation of the gearbox bearing temperatureby a neural networkmodell


Maintenance/

Reliability

ReliabilityModel

Mainte-nance


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel


Maintenance/

Reliability

ReliabilityModel




‐30

‐20

‐10

0

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50

60

70

80

90

100

110

120

130

0

10

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70

80

90

100

110

Mod

el estim

ation error (%) [lim

it: +/‐17

%]

Temperatures

Time ‐ a year





‐30

‐20

‐10

0

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50

60

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110

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0

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90

100

110

Mod

el estim

ation error (%) [lim

it: +/‐17

%]

Temperatures

Time ‐ a year






Maintenance/Event dataMaintenance/Event dataMaintenance/Event dataMaintenance/Event data


mitigation(control)

ContactContact

Dr. VIHAROS Zsolt János

Head: Manufacturing and business processes Head: Manufacturing and business processes group

[email protected], www.sztaki.hu/~viharos

Engineering and Management Intelligence (EMI)g g g g ( )MTA SZTAKI


Advanced wind turbine health monitoring from … wind turbine health monitoring from SCADA...

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