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John Dalsgaard Sørensen Aalborg University, Denmark

• Introduction • AAU: Reliability and risk-based planning of O&M• UiS: Condition-based maintenance for offshore wind parks• UiA: Wind farm control including operation and

maintenance aspects• AAU: Wind farm control strategy• Concluding remarks

Summary and highlights from NORCOWE Maintenance

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Introduction

Minimize the Total Expected Life-Cycle Costs

Minimize Levelized Cost Of Energy (LCOE)

140 (Anholt) 103 (HR 3) 64 (Near-Shore) [€/MWh]

• Dependent on Reliability Level Initial Costs

• Dependent on O&M Strategy, Availability and ReliabilityOperation &

Maintenance Costs

• Dependent on ReliabilityFailure Costs

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Introduction

Failure rates and downtime (examples – onshore wind turbines):

ISET 2006

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Blades

Gearbox, …

Power electronics:

Introduction

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Tower & Substructures:

Foundation:

Introduction

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• Corrective (unplanned): exchange / repair of failed components

• Preventive (planned):– Time-tabled: inspections, and evt. repair after predefined

sheme– Condition-based: monitor condition of system and decide

next on evt. repair based on degree of deterioration• Risk-based methods based on Bayesian decision theory

Operation and maintenance of wind turbines

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Reliability modeling of wind turbines – exemplified by power converter systems as basis for O&M planning

PhD study: Erik Kostandyan, Aalborg University. 2009-2013

Objectives: • Develop reliability models for O&M planning

– Damage accumulation models for power electronics components – physics-of-failure models

– Reliability model(s) for selected electrical component: IBGT module

• to be integrated into O&M strategies for planning and development

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Reliability modeling of wind turbines – exemplified by power converter systems as basis for O&M planning

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Reliability modeling of IBGT module

Silicon

Copper

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Reliability modeling of wind turbines – exemplified by power converter systems as basis for O&M planning

• Reliability model of failure mode with crack growth in solderlayer developed, and using limit state equations using structuralreliability methods• Application for O&M strategy development• Application for DLC 2 reliability modeling considering faults

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Risk-and reliability-based planning of inspections and maintenance

• Reliability-based cost-optimal planning of inspections andmaintenance for fatigue critical welded steel details in tower andsubstructures

• Maximum annual probability of failure = 5 10-4

• Used as background and basis for reduction of partial safetyfactors in CDV IEC 61400-1 ed. 4:2016

• Used in probabilistic design for wind turbines

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Publications

• Kostandyan, E., & Sørensen, J. (2012). Structural reliability methods for wind power converter system component reliability assessment. 16th IFIP WG 7.5 Conference on Reliability and Optimization of Structural Systems, Yerevan, Armenia. pp. 135-142.

• Kostandyan, E., & Sørensen, J. (2012). Weibull parameters estimation based on physics of failure model. Industrial and Systems Engineering Research Conference (ISERC 2012), 62nd IIE Annual Conference & Expo2012, Orlando, Florida, USA. pp. 10.

• Kostandyan, E., & Sørensen, J. (2013). Reliability assessment of offshore wind turbines considering faults of electrical / mechanical components. 23rd International Offshore (Ocean) and Polar Engineering Conference (ISOPE 2013), Anchorage, Alaska, USA. pp. in press.

• Kostandyan, E. E., Lamberson, L. R., & Houshyar, A. (2010). Time to failure for a k parallel r-out-of-n system.International Journal of Modelling and Simulation, 30(4), 479-482.

• Kostandyan, E. E., & Ma, K. (2012). Reliability estimation with uncertainties consideration for high power IGBTs in 2.3 MW wind turbine converter system. Microelectronics Reliability, 52(9-10), 2403-2408.

• Kostandyan, E. E., & Sorensen, J. D. (2012). Physics of failure as a basis for solder elements reliability assessment in wind turbines. Reliability Engineering and System Safety, 108, 100-107.

• Kostandyan, E. E., & Sorensen, J. D. (2012). Reliability of wind turbine components — solder elements fatigue failure. Reliability and Maintainability Symposium (RAMS), 2012 Proceedings - Annual, pp. 1-7.

• Kostandyan, E. E., & Sørensen, J. D. (2011). Reliability assessment of solder joints in power electronic modules by crack damage model for wind turbine applications. Energies, 4(12), 2236-2248.

• Kostandyan, E. E., & Sorensen, J. D. (2013). Reliability assessment of IGBT modules modeled as systems with correlated components. Reliability and Maintainability Symposium (RAMS), 2013 Proceedings - Annual, pp. 1-6.

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Risk and Reliability based O&M Planning of Offshore Wind Farms

Industrial PhD study: Masoud Asgarpour, ECN / Vattenfall + Aalborg University. 2013-

Objectives: • Development of Bayesian and risk-based O&M techniques for

multiple component WT systems• Development of Reliability Matrix approach using CMS data• Updating the deterioration models used in the Reliability Matrix• Updating the model to include neighbor wind farms

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Reliability Matrix for critical failure modes

Deterioration per Month/Park/Turbine/Component, updated using available information

Date Park Turbine Component Calculated "D" Reported "D" Reporting Method

2015 Jan WHV A01 MDA‐Rotor 0.2371002015 Jan WHV A01 MDC‐Pitch 0.6183302015 Jan WHV A01 MDK‐Drivetrain 0.1176592015 Jan WHV A01 MDL‐Yaw 0.7198482015 Jan WHV A01 MKA‐Generator 0.3835192015 Feb WHV A01 MDA‐Rotor 0.2375742015 Feb WHV A01 MDC‐Pitch 0.6208032015 Feb WHV A01 MDK‐Drivetrain 0.117777 0.3 Inspection2015 Feb WHV A01 MDL‐Yaw 0.7227272015 Feb WHV A01 MKA‐Generator 0.3846702015 Mar WHV A01 MDA‐Rotor 0.2380492015 Mar WHV A01 MDC‐Pitch 0.6232872015 Mar WHV A01 MDK‐Drivetrain 0.3051002015 Mar WHV A01 MDL‐Yaw 0.7256182015 Mar WHV A01 MKA‐Generator 0.385824 0.2 CMS2015 Apr WHV A01 MDA‐Rotor 0.2385252015 Apr WHV A01 MDC‐Pitch 0.6257802015 Apr WHV A01 MDK‐Drivetrain 0.3054052015 Apr WHV A01 MDL‐Yaw 0.728521 1.0 Failure2015 Apr WHV A01 MKA‐Generator 0.2006002015 May WHV A01 MDA‐Rotor 0.2390022015 May WHV A01 MDC‐Pitch 0.6282832015 May WHV A01 MDK‐Drivetrain 0.3057112015 May WHV A01 MDL‐Yaw 0.0082702015 May WHV A01 MKA‐Generator 0.201202

Baysian updating

Baysian updating

Rotor deteriorationmodel

Baysian updating

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Maintenance Matrix based on Bayesian decision rules

Date Park Turbine Component Scheduled Service Condition‐based Inspection Condition‐based Repair Corrective Replacement

2015 Jan WHV A01 MDA‐Rotor2015 Jan WHV A01 MDC‐Pitch2015 Jan WHV A01 MDK‐Drivetrain2015 Jan WHV A01 MDL‐Yaw2015 Jan WHV A01 MKA‐Generator…2015 Jul WHV A01 MDA‐Rotor2015 Jul WHV A01 MDC‐Pitch2015 Jul WHV A01 MDK‐Drivetrain2015 Jul WHV A01 MDL‐Yaw2015 Jul WHV A01 MKA‐Generator…2016 Mar WHV A01 MDA‐Rotor2016 Mar WHV A01 MDC‐Pitch2016 Mar WHV A01 MDK‐Drivetrain2016 Mar WHV A01 MDL‐Yaw2016 Mar WHV A01 MKA‐Generator…2016 Jul WHV A01 MDA‐Rotor2016 Jul WHV A01 MDC‐Pitch2016 Jul WHV A01 MDK‐Drivetrain2016 Jul WHV A01 MDL‐Yaw2016 Jul WHV A01 MKA‐Generator…2017 Dec WHV A01 MDA‐Rotor2017 Jan WHV A01 MDC‐Pitch2017 Jan WHV A01 MDK‐Drivetrain2017 Jan WHV A01 MDL‐Yaw2017 Jan WHV A01 MKA‐Generator

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Publications

• State of the art in O&M planning of offshore wind farms– Presented at European Safety & Reliability Conference (ESREL) - Poland– Published by Taylor & Francis Group, London, ISBN 978-1-138-02681-0 ©2015

• Framework of a risk and reliability based offshore wind O&M model– Annual Reliability and Maintainability Symposium (RAMS) - US– Published by IEEE 978-1-5090-0249-8/16/$31.00 ©2016

• Reliability matrix for O&M planning of offshore wind farms– Journal paper, expected submission 2016-Q4

• Maintenance matrix for O&M planning of offshore wind farms– Journal paper, expected submission 2017-Q1

• PhD thesis– Expected submission 2017-Q1

• Risk and reliability based O&M planning of offshore wind farms – A novel case study– Journal paper, expected submission 2017-Q2

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Risk and Reliability based O&M Planning of Offshore Wind Farms

PhD study: Mihai Florian, Aalborg University. 2013-

Objectives: • Degradation modeling for decision support in maintenance

planning• Development of risk-based inspection planning framework for

wind farms• Cost optimal O&M planning using Bayesian decision tree

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Initial cracking

Development Failure

• One dimensional fracture mechanics model• Loading based on turbulence mean wind speed and

turbulence intensity

Monte Carlo simulations

Deterioration modelling for WT blades

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Dynamic Bayesian Networks

• inspection at time t• update of uncertain parameters in damage model

Distribution of crack size before/after inspection

Inspection plan

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CTV HLVNumber 4 1Wave limit [m] 1.5 2

Wind limit [m/s] - 20Mobilisation time [days] - 30

Mobilisation cost [€] - 250000

Speed [knots] 20 11Day rate [€] 1000 100000

Activity Cost [€] Duration [h]Inspection 1000 6Repair 10000 24

Replacement 400000 80

Vessels

Cost model

Preventive strategy• Time/condition based• Risk/reliability based

Case study – NORCOWE Reference Wind Farm

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Time/condition based model• Time interval of inspection• Repair threshold

Optimal decision• 2 year interval• 0.4 [m] crack sizeTotal cost [€] Downtime [%]5.25 106 0.37

Optimal decision• 1% failure probability

Total cost [€] Downtime [%]4.55 106 0.27

11 inspection & 2.3 repairs/turbine 7.2 inspection & 1.9 repairs/turbine14% cost reduction

Case study – NORCOWE Reference Wind FarmRisk/reliability based

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Publications

• Wind turbine blade life-time assessment model for preventive planning of operation and maintenance - Journal of Marine Science and Engineering 2015

• Planning of operation & maintenance using risk and reliability based methods - Energy Procedia Journal 80 ( 2015 ) 357 – 364, 2015

• Risk-based planning of O&M for wind turbines using physics of failure models - European Conference of the Prognostics and Health Management Society 2016

• Case study for impact of D-strings on levelised cost of energy for offshore wind turbine blades – under revision for publication to the International Journal of Offshore and Polar Engineering

• Cost optimal risk-based inspection planning for offshore wind farms – to be submitted to DeepWind conference, Trondheim, 2017

• Risk-based inspection planning for offshore wind farms, a comparison with traditional maintenance – in progress

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Condition-based maintenance for offshore wind parks

PhD study: Ole-Erik Endrerud, University in Stavanger. 2013-

Objectives / Scientific Contributions:• Methodological contribution to hybrid simulation for modelling

of large scale industrial assets.• Increased understanding of the marine logistics system used for

O&M of offshore wind energy assets. (Work process optimization)

• A framework for simulation modelling of operation and maintenance. (Decision support)

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Publications

Conference articles• Decision support for operations and maintenance of

offshore wind farmIn Engineering Asset Management - Systems, Professional Practices and Certification. Springer 2015 ISBN 978-3-319-09506-6. s. 1125-1139

• Marine logistics decision support for operation and maintenance of offshore wind parks with a multi method simulation modelIn Proceedings of the 2014 Winter Simulation Conference. IEEE Press 2014 ISBN 978-1-4799-7486-3. s. 1712-1722

• New Vessel Concepts for Operations and Maintenance of Offshore Wind FarmsIn Proceedings of the Twenty-fifth (2015) International Ocean and Polar Engineering Conference Kona, Big Island, Hawaii, USA, June 21-26, 2015

Journal articles• Reference Cases for Verification of Operation and

Maintenance Simulation Models for Offshore Wind FarmsIn Wind Engineering, Volume 39, No. 1, 2015

• In progress: Efficiency of condition based maintenance in operation and maintenance of offshore wind farms

• In progress: Assessment of maintenance strategies using an agent-based and discrete event simulation model

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SR Bank Innovation Award 2015

Endrerud fikk innovasjonsprisSøk i rogalandsavis.no

• Ole-Erik Endrerud got the SR Bank Innovation Award for the establishment of Shoreline AS and commercialisation of O&M simulation technology for the offshore wind industry.

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Health Assessment of pitch and yaw systems in offshore wind turbines

PhD study: S.T. Kandukuri (Surya), University in Agder. 2014-

Objectives: • Develop health assessment techniques for electrical pitch & yaw

systems that are suitable for farm-level implementation– Diagnostics for pitch and yaw systems – Prognostics for pitch and yaw systems – Capturing effects of corrosion and fatigue

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Overview

Wind Turbine Systems 

knowledge

• Wind & wave simulations

• FAST analysis – loads on pitch system 

• Pitch system components, health assessment requirements

Laboratory Demonstration

• Efficacy of diagnostics algorithms in variable load/speed conditions

• Effects of ’exogenous’ faults on component level diagnostics

Real‐world O&M scenario

• Sate‐of‐the‐art

• CM exists but as ‘end products’ on specific components

• Offshore wind farms in nascent stages

• Pitch & yaw systems fail often

Health Assessment of Pitch & Yaw Systems

• Failure modes and effects 

• Most common failure modes 

• Diagnostics & prognostics algorithms

• Fault classification 

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Induction motor

4-stage planetary gearbox

Pinion gear

Focus: Wind Turbine Pitch/Yaw systems

Motor GearboxTypically, 3 phase Induction Motors, servo motors

Common faults

• Stator windings (30%)• Bearings (40%)• Imbalance, Shaft Eccentricity• Broken rotor bars (IM)• Loss of rotor magnetization (SM)

Diagnostic Methods: • Motor current signature analysis

(MCSA), Vibration, Temperature…

2-3 stage planetary gearbox

Common faults

• Scuffing of gear tooth • Teeth crack• Carrier plate cracks• Ring gear damage• Bent shafts• Corrosion effects

Diagnostic Methods• Vibration signature, Acoustic

emission, oil debris monitoring…

Motivation:About 45% of all installations have electrical pitch and almost all turbines have electrical yaw systems.

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Algorithms - Motor current signature analysis (MCSA)

Mechanical faults

Electrical faults

Variations in electric circuit/ airgap magnetic field

Manifest as periodic disturbances in supply current

Accomplishments: • Detailed modeling of induction motor faults based on modified winding

function theory (MWFTh) • Diagnostics based on Fourier spectrum analysis of motor currents

Next Step: • Time – frequency analysis of current signature for variable speeds and

torque analysis

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Laboratory DemonstrationDemonstrate feasibility of techniques for incipient fault detection in pitch/yaw drives using current and vibration signature, suitable for farm-level implementation.

Subsystem Component Faulty type

Pitch Drive

MotorBroken rotor bars

Stator winding faultWorn bearings

Gearbox

Planet gear faultSun gear fault

Carrier plate cracks

Seeded fault tests

Test motorABB brake motor 1.1kW 4-pole 3phase IM

Test gearbox 2 stage planetary gearbox (1:48)

Load motorABB motor 3kW 8-pole 3phase IM

Load gearbox BPH gearbox (1:27)

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Publications• S.T. Kandukuri, A. Klausen, H.R. Karimi, K.G. Robbersmyr, A review of diagnostics and prognostics of low-speed machinery

towards wind turbine farm-level health management (2016). Renewable & Sustainable Energy Reviews• S.T. Kandukuri, K.G. Robbersmyr, H.R. Karimi, Towards farm-level health management of offshore wind farms for

maintenance improvements (2015). The International Journal of Advanced Manufacturing Technology• S.T. Kandukuri, V.K. Huynh, H.R. Karimi, K.G. Robbersmyr, Fault diagnostics for electrically operated pitch systems in

offshore wind turbines (2016). Torque 2016, IOP Conf. Series• A. Chougule, S.T. Kandukuri, H.G. Beyer, Assessment of synthetic winds through spectral modelling and validation using

FAST (2016), Torque 2016, IOP Conf. Series

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Wind-farm Control strategy

S. Christiansen, T. Jensen, T. Knudsen and T. Bak, Aalborg University:

Control problem: minimize WT fatigueCentral assumptions:

1. Fatigue is positively correlated with turbulence; 2. Sum of individual fatigues is a relevant fatigue measure.

Minimisation problem: – Sum (over WTs) of added turbulence as objective function;– Max/min WT power and WF power reference as constraints.

Two approaches have been examined:1. Static optimisation. (24h updates of WT references);2. Dynamic optimisation. (5 sec updates of WT references).

T N Jensen, T Knudsen and T Bak; Fatigue minimising power reference control of a de-rated wind-farm; Torque 2016

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Comparison of strategies(Relative to a fixed distribution strategy)

5,6 5,6 5,7

7,98,5 8,2

3D 5D 10D

%

Spacing in Rotor diameters

Reduction in sum of added turbulence

Static Dynamic

Simulation: 3 turbines in a row. The sum of the added turbulence is reduced in both cases

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Comparison of strategies(Relative to a fixed distribution strategy)

Reduction in damage equivalent loads (DEL)

5,8

3,6

1,4

4,6

2,3

-3,5

3D 5D 10D

%

Spacing in Rotor diameters

Tower bending moment DEL

Static Dynamic

Reducing added turbulence translates to reduced DEL on the tower

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Floating wind turbines

• Estimating wave and wind disturbances• Optimal control and estimation – use pitch system, as if turbine was

bottom fixed• Allows loads to be lowered, with acceptable loads on pitch system.

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11

22

11

22

36

18

-121

15

1

54

1

20

-105

17

0

51

3

7

-95

13

-4

62

1

-150 -100 -50 0 50 100

ELEC. POWER (STD)

BLADE PITCH RATE (ABS)

TOWER FORE-AFT (STD)

TOWER SIDE-SIDE (STD)

PLATFORM PITCH (STD)

PLATFORM ROLL (STD)

% compared to baseline controller (NREL)

Perpendicular wind-wave forces: statisticalanalysis of relative controller performance (standard deviations

and abs values)

10 sec 5 sec 2 sec

Floating wind turbines - results

Peak wave period:

Baselinebetter

Optimal controlbetter

Less loads on platform and tower

More pitch

But this is more in line with typical

bottom fixed turbine operation

Christiansen, S., Bak, T., & Knudsen, T. (2013). Damping Wind and Wave Loads on a Floating Wind Turbine. Energies, 4097-4116.

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International cooperation …

• LEX - Torsional Stiffening of Wind Turbine Blades – Mitigating leading edge damages (Danish EUDP)

• RATZ (Root Area and Transition Zone) - Reduction O&M cost of WT blades (Danish EUDP)

• LEANWIND - Logistic Efficiencies And Naval architecture for Wind Installations with Novel Developments (EC – FP7)

• MANTIS – Cyber Physical System based Proactive Collaborative Maintenance (EC - H2020)

• IEA-WIND Task 33 - Reliability data – for O&M optimization of wind turbines

• IEC 61400-1 ed. 4 (CDV 2016) Wind turbines – Design requirements

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Concluding remarks

• Next steps:– Implementation of risk-based OM strategies– Better understanding of degradation mechanisms – and reliable estimates

of RUL (Remaining Useful Life) – Improved use of data (big-data) for OM planning– More application of

• integrated WT and WF control & OM planning• information from condition monitoring systems & structural health

monitoring (SHM) – accounting for uncertainties / reliability