Webinaire : Comment optimiser la performance, la maintenance et la durée de vie des éoliennes ? | BEMAS-TWEED - 18 mars 2021

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.
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pbricout@clustertweed.be
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Info & inscriptions sur www.bemas.org
Mobilité CARL Flash: L’application mobile de demandes de
services et d’intervention pour tout public
Lors de ce web séminaire interactif, vous découvrirez CARL
Flash, l'application mobile de demandes de services tout public
qui permet de tracer, planifier les interventions et optimiser les
processus de maintenance.
A portée de tout public (agents de production, employés,
infirmiers, citoyens, passagers etc.), CARL Flash de créer du lien
entre ses utilisateurs et les services techniques et garantit la
qualité du service rendu.
Au travers d’une démonstration de l’application illustrée de
plusieurs expériences clients, vous pourrez apprécier l’étendue
fonctionnelle de CARL Flash et sa simplicité d’utilisation en
toute sécurité.
Jeudi 01/04/2021 à 15h00
• Xavier Foti, CARL
• Alexandre Grutering, CARL
Info & inscriptions sur www.clustertweed.be

Jeudi 18 mars 2021
Comment optimiser la performance, la maintenance et la durée de
vie des éoliennes?
Orateurs dans l’ordre des présentations:
- Nicolas Loix, Micromega
- Ivan De Visscher, WaPT
- Eric Delvaux, Schaeffler
- Olivier Dengis, I-care - Mathieu May,I-care - Philippe Mol, P4A

Dispositif de mesure de l’endommagement par fatigue et
extension de permis
Présentée par Dr. Ir. Nicolas Loix, General Manager
Micromega

•
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
➢


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•
•
•
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➢

•
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•
•
→
± 2g
0.1 – 10 Hz
fs = 25.6 Hz
≤ 50 𝜇𝑔/√Hz

•
•
•
•
+/- 15°
0.555 mA/°
0.0015°
0.0005°/√Hz

Modélisation opérationnelle et simulation numérique comme
outils à la prédiction et l’optimisation d’opération d’éoliennes
Présentée par Ivan De Visscher, Co-fondateur et General Manager
WaPT - Wake Prediction Technologies

Modélisation opérationnelle et simulation
numérique
Webinaire TWEED et BEMAS, 18 Mars 2021
Ivan De Visscher
Wake Prediction Technologies (WaPT)
Outils à la prédiction et l'optimisation d'opération d'éoliennes

© 2021 Wake Prediction Technologies - WaPT SPRL All Rights Reserved
Wind energy : Trends and related challenges
• Increased number of installed wind turbines
• Variability in wind turbine
– Size: From small on-shore to very large off-shore
– Design: HAWT, VAWT, Floating turbines
• On-shore:
– Installation in sites with increasingly complex
orography
• Complex wind evolution and thus prediction
• Small wind turbine more sensitive to local wind
behaviour
• Off-shore
– Larger wind turbines
– Increased number of farms
➔ Wind turbine interaction through wake effects

WaPT in a nutshell
➢ Spin-off company from Université catholique de Louvain
(UCLouvain)
➢ Benefiting from the UCLouvain tools and expertise developed since
the 90s
➢ Applying UCLouvain tools and expertise to industrial challenges
➢ Private limited company founded in 2013 by:
▪ Dr Ivan De Visscher (General Manager)
▪ Pr Philippe Chatelain (Part-time Expert and Scientific Adviser)
▪ Pr Grégoire Winckelmans (Part-time Expert and Scientific Adviser)
➢ Supported by the Walloon Incubator for engineering sciences (WSL)
Providing solutions to wake vortex-related issues
for aeronautics and wind energy applications

WaPT numerical simulation tools
Operational modeling
Advanced numerical
simulation
Aircraft
Wind energy WaRM, BEM, DMST
WAKE4D BigFlow, VPM
BigFlow, VPM4WIND

Fields of application
Challenge : Optimize
separations while
guaranteeing safety
0 1 2 3 4 5 6 NM
HEAVYfollowed by ..
.
MEDIUMfollowed by LIGH
T
HEAVY
followed by
MEDIUM followed
by LIGHT
Advanced numerical simulation
Aeronautics Wind Energy
High
accuracy
Real time
models
Operational modeling
• RECAT-EU recategorization
• TBS at London-Heathrow
• WIDAO at Paris-CDG
Challenges : Reduce costs
by reducing loads,
increasing power
production and lifetime in
wind farms
• LiDAR-enabled controler
• Global wind farm control
• New concepts: VAWT, etc.

Wind turbine aerodymics:
a multiscale phenomenon

CFD tool: BigFlow solver
• Large Eddy Simulation (LES)
• Wall model
• Fourth order finite differences
• Velocity-pressure formulation
• Wind turbine accounted for through an Actuator Disk (AD) including
– Thrust and Torque effects and
– non-uniform forces repartition
• High fidelity turbine controllers: generator torque, pitch and yaw
• Initially developed at UCLouvain

Example of application:
Fatigue study in a wind farm
8
[m/s]
WT1 WT2 WT9
Fatigue equivalent loads
(yaw moment)
Flapping moment spectra

WaPT simulation tool: VPM4WIND
• Unsteady characterization of :
– Power production
– Blade loading
– Wake
• Accounts for :
– Wind turbine type
– Blade global aerodynamics
– Positionning (isolated or in a farm)
– Meteorological conditions (wind, turbulence)
• Use of hybrid numerical method combining
– Vortex Particle-Mesh (VPM) approach with
– Immersed lifting line technique
Efficient, accurate and scalable to massively parallel architecture
• Validated against state-of-the-art and experimental measurements
• Initially developped at UCLouvain

VPM4WIND
From the blade aerodynamics to the very far-wake

Example of VPM4WIND validation
• Simulated vs Full Scale LiDAR
– LiDAR 1 km downstream of
the rotor
– Transversal scans at hub
height
• Good agreement
– Jet decay captured
– Wake smearing in full scale
LiDAR due to averaging over
6° wind sector

Example of VPM4WIND verification
With a 5 to 12 fold time-to-solution advantage for VPM

VPM4WIND results
VAWT
Atmospheric Effects
Wake Meandering
Farm effects
Floating turbine

Example of application:
Pitch misalignement study
• VPM4WIND investigation of pitch misalignment on
– Power production
– Blade loading
– Controller behaviour
• Allows one-to-one comparison in same wind
conditions
• Study outcomes
– Identifications of pitch misalignement telltales
– Impact on AEP

Impact of Pitch misalignment on power
Higher/lower power with negative/positive pitch error
Negative pitch
on one blade
Positive pitch
on one blade

Mean force along the blade – No pitch error
1
7

Mean force along the blade – with pitch error
1
8
Increased
loading on
the blade
with pitch
error
Slight
decrease
for blades
without
error

Pitch error spectral signature
With pitch
asymmetry
Without pitch
asymmetry
Clear 1P and 2P signature due to pitch error

Operational modeling: POPE project
Operational model able to predict wind turbine performances
– Usable for
• Large wind turbine
• Small wind turbine
• Conventional rotor design
• Non-conventional rotor design
– Allowing
• Site assessment
• Local optimisation of power production
• Optimisation of rotor and controler development
• Use as input for predictive maintenance

Objectives and expected results
Development of an operational multi-physics modeling chain covering wind
ressource assessment down to wind turbine performance characterisation

Objectives and expected results
CFD results supporting operational model development

POPE outcome
• Prototype of a new operational modelling tool coupling
– Mesoscale wind prediction
– Microscale wind modelling module
– Wind turbine performance and loading model
• Uncertainty quantification of the whole modelling chain
• Example of application of the developed tool for small
wind turbine

Wind turbine performance and loading
operational modelling
HAWT - Horizontal Axis
Wind Turbine
VAWT - Vertical Axis
Wind Turbine
Blade Element Momentum (BEM) Double Multiple Streamtube (DMST)
Models accounting for
- Wind speed, shear,
turbulence
- Rotor controller
- Blade aerodynamics
- Blade deflection
- Dynamic stall
Outputs :
- Power, Torque
- Blade loading
- Deflection

Example of verification: DMST vs VPM4WIND
Same machine
• VAWT
• same blade aerodynamics
Same wind
• Various TI
• Various shear
Same control strategy
Comparison with VPM4WIND reference simulations

Example of verification: DMST vs VPM4WIND
DMST no controler
DMST – VPM4WIND
controler
Reference VPM4WIND
DMST reproduces both mean and variation of power signal

Take-home messages
• Access to high-fidelity tools allows
– Accurate physics characterization
– Development and validation of operational tools
• Operational tools can be used
– For site assessment
– For controller design and optimisation
– For digital twin
– As input in maintenance tools
– In support to defect diagnosis and prediction
• Physics-based models
– Are more robust to « unknown » cases
– Do not require re-calibration
– But require accurate input data
– Can be combined with data-driven approaches e.g., through data assimilation

[WaPT] Wake Prediction Technologies
Contact
Ivan De Visscher
rue Louis de Geer, 6
1348, Louvain-la-Neuve
Belgium
ivan.devisscher@wapt.be
Thank you
www.wapt.be

Smart Ecosystem 4.0 : L’apport d’un fabricant de roulements à la
maintenance prédictive des roulements d’éolienne
Présentée par Eric Delvaux, Account Manager Industry
Schaeffler Group

Schaeffler Smart Ecosystem 4.0
L’apport d’un fabricant de roulements à la maintenance prédictive des roulements d’éolienne

Bemas/tweed : maintenance eolienne 2
Agenda
Bemas/Tweed : Maintenance éolienne
3/11/2021
Smart Ecosystem 4.0 :
L’approche Schaeffler de la maintenance prédictive des roulements d’éolienne
2
Jumeau numérique et intelligence artificielle :
l’estimation de la durée de vie résiduelle des roulements en éolien.
3
Lubrification et remise en état des roulements
4
Présentation Schaeffler : Pourquoi l’éolien est-il important pour nous
1

3
Agenda
Bemas
3/11/2021
2
3
4
1
Bemas/tweed : maintenance eolienne

Schaeffler en chiffres
11.03.2021
1 Before one-off effects | 2 As at June 30, 2020
1.1 m
Tonnes d‘acier traitées p.a.
Forte présence sur le
Marché avec plus de
11,800
clients
Approximativement
EUR 14.4 bn
ventes en 2019
Plus de
2,400
depôts de brevets en 2019
8.1%
marge EBIT en 20191
Environ
84 200
employes dans le monde2
Largement plus de
10,000
produits differents
75usines
20centres R&D
Plus de 170 sites dans 50 pays
4

Point de départ pour développer notre direction stratégique - Quatre mégatendances
Strategy “Mobility for tomorrow”
11.03.2021
Changement climatique
 Les émissions de gaz à effet de
serre entraînent le
réchauffement climatique,
l'industrie automobile en est un
principal responsable
• Accent mis sur la limitation du
changement climatique et sur la
préservation des ressources
naturelles ; l'accord de Paris est
une base mondiale commune
Urbanisation
• Importante croissance des grandes
villes, en 2025 environ 60% de la
population actuelle vivra dans les
villes
• De nouvelles solutions concernant
l'infrastructure et la mobilité sont
nécessaires
Globalisation
• Augmentation globale du
commerce dans le monde,
mobilité en tant que facteur de
croissance et de richesse
• Établissement de liens plus étroits
à travers les frontières ainsi qu'une
mobilité abordable sont des
facteurs importants
Digitalisation
• Augmentation de la connexion
numérique entre les machines et
les produits autant qu'entre les
compagnies, les fournisseurs et
les clients
• Transformation du modèle
d'affaire et des processus,
émergence des nouvelles
interfaces et des produits
intelligents
Environnement Societé Economie Technologie
5

Quatre domaines stratégiques dans la Mobilité de demain
Motorisations
écologiques
Mobilité
urbaine
Mobilité
interurbaine
Chaîne
énergétique
 Moteurs à combustion
interne optimisés
 Véhicules électriques
 Moteurs industriels
 2-roues
 Réseaux ferrés urbains
 Micromobiles
 Véhicules sur rails
 Transports aériens
 Véhicules non routiers
 Energie éolienne
 Energie solaire
 Energies
conventionnelles
6
11.03.2021

Automotive OEM
(Systemes)
Schaeffler Group
Trois divisions – Automotive OEM, Automotive Aftermarket et Industrie
Automotive Aftermarket
(Segments)
Industrie
(Domaines d'activité)
Systemes Moteurs Systemes Transmission
Systemes Châssis Systèmes Hybride et
Electique
Véhicules particuliers Véhicules utilistaires légers
Véhicules utilitaires lourds Tracteurs et engins
agricoles
Services
Eolien Iindustrie lourde
Aeronautique et spacial Ferroviaire
Agriculture Cycles et motocycles
Transmission Equipement de production
7
11.03.2021

Industry 4.0 – un large champ d'application
Schaeffler Group
Machine outil 4.0
Concept pour la digitalisation
de la production
 Contrôle des processus de
la machine (vibrations,
forces, températures)
Voie ferrée 4.0
Contrôle numérique des roulements
de boîtes d'essieux, des moteurs de
traction et des boîtes de vitesse
 Maintenance préventive
 Diagnostique automatisé des
roulements
 Calcul de la durée de vie utile
restante du lubrifiant
Énergie éolienne 4.0
Contrôle numérique des éoliennes
roulements
restante des roulements
 Surveillance à distance
Transmission 4.0
Démonstration de surveillance en
ligne de systèmes de transmission
roulements
restante des roulements
8
11.03.2021

9
Agenda
Bemas
2
3
4
1
11.03.2021

How Schaeffler supports reliable operation and prevention of damages
Intelligent Sensor Concepts for reliable operation
2
Outlook
3
Keyfacts about wind turbines
1
11.03.2021

1 Keyfacts about wind turbines
No. 1: Turbine trends
Over the past 30 years:
• rotor diameters had been increased by factor 10.
• power ratings increased by factor 16-20.
"… We want less weight, less components and they should work reliable.“ (citation: Henk Lagerweij, neue energie 10/2012)
11.03.2021

No. 2: Influencing factors for energy output as well as for wind loads
turbine 1
turbine 2
turbine 3
turbine 4
turbine 5
turbine 6
turbine 7
turbine 8
turbine 9
Energy output as well as wind loads are turbine-specific for a certain installation site  high optimization potential by considering real conditions
Example of a local effect: park layout  wind condition of a certain turbine are directly influenced by neighbouring-turbines (e.g. WAKE-effects)
Question:
What is the turbine with
the maximum ernergy
output?
11.03.2021

No. 3: Bearings are part of the drivetrain system  exposed to many sorts of system influences
Example: WEC - one of the most critical bearing failure modes in industrial as well as automotive business
Description:
 WECs (White Etching Cracks) are network of cracks at
and in white etching phases, which can emerge in
rolling bearings, independent of the bearing type and
design.
 WEC rating life cannot be calculated using the classic
theory of rolling bearing rating life.
Knowledge state:
 Based on more than 15 years of fundamental research,
several impact parameters which lead into such failure
mode are identified and common sense in the
scientific research community.
 Main trigger:
 Humidity (lubrication, sealed lubrication system)
 Electrics (grounding systems)
Bearing failures are often triggered from „outside“, not always by bearing dimensioning or quality  system knowledge mandatory
11.03.2021

How Schaeffler supports reliable operation and prevention of damages
Keyfacts about wind turbines
1
Outlook
3
Intelligent Sensor Concepts for reliable operation
2
Overview
2.1
Field-Examples
2.2
11.03.2021

2 Intelligent Sensor Concepts for reliable operation
Augmenter la fiabilité des systems avec les capteurs
Les capteurs de roulement dans la chaine de transmission ont pour but :
•  assurer la fiabilité du système
Détection de probléme rapide et mesure correctives
•  d’optimiser les composants en considérant les conditions réelles
•  aide au processus de développement
Les capteurs sont utilisés également pour les prototypes
11.03.2021

ASTRAIOS : banc test
16
Test de rlt jusque 15T et 3,5m diamètre
Effort axial : 6000 kN
Effort radial : 4000 kN
Couple : 150 kNm
Plus de 200 capteurs
Mise en service 2011
11.03.2021

Sensorization of main bearing arrangements
Wind
Bearing
s -
oil circuit
Preload
Reduced preload may
result in downtime
Load conditions
Unawareness of harmful
operation conditions
Vibration / Temperature
/ Speed
Static number of maintenance
cycles increase MRO
costs and risk of failure
Water ingress
Decrease in lifetime
Lubrication
Decrease in lifetime
i
i
i
i
i i
i
i
Current flow
Risk of WEC failure on
bearings
i
11.03.2021

Value add - Monitoring of lubrication conditions and humidity level
 Humidity increase
 Increase of water content in lubricant
Online monitoring of lubrication
conditions and humiditiy level at bearing
housing in real turbine operation enables:
• Detection of defect sealing
 Oil tank of an operated turbine after re-
arrival in port:
Customer value add:
• Maintenance action only if required
• Lubricant exchange only if necessary
based on condition  possibility to plan
for exchange
• Avoidance of bearing damage, downtime
and exchange offshore
11.03.2021

2 Service Solutions
Schaeffler GreaseCheck system layout
Function:
• Optical measurement principle
• Measurement of light reflection and scattering.
• Three parameters can be detected using the optical measurement method:
 Water content, deterioration and grease temperature.
• The intelligent electronic evaluation system informs the user quickly and simply about the condition of the grease
5 mm
LED
Reference detector: LED
ageing
Measurement detector
Window of
sapphire glass
Flexible PCB
Housing
Grease
SENSOR HEAD
Penetration depth (into the
grease; 5-6 mm)
Front VIEW:
SIDE VIEW:
11.03.2021

Online monitoring electricity impact in
real turbine operation enables:
• Detection of passive earthing system in
the hub system
• Detection of passive earthing system at
generator side
Value add - Monitoring of electrical impact at main bearing
 Passivated earthing system leads to electric
potential on shaft
 Electr. potential leads to current
flow through main bearing
Customer value add:
• preventive and dedicated maintenance
action on demand instead of regular and
general maintenance  less costs in
maintenance
• avoidance of WEC damage at main
bearing  avoidance of nacelle change
offshore and unplanned downtime.
11.03.2021

Use of the load sense pin to measure the rolling element load
21
Measurement of
the deformation
+ Proven measuring
concept
Load Sense Pin
+ Coated strain gauge
+ no aging effect, as no
glue
Mounting
in the outer ring
+ Easy installation
+ Measurement
directly in the direction
of the force
Results of 5 rollovers
+ High signal quality
+ Load and speed analyzable
Load Sense Pin in Wind turbine application
11.03.2021

Q
rolling element force Q ~ LoadSense-Pin-signal
LoadSense-Pin
Outer ring contact:
F=?
Input: wind loads (5x), speed
Output2: bearing loads
Output1: rolling element force Q (~ LoadSense-Pin-signal, 8x)
machine learning
Value add - load monitoring: Working principal for an 1-row tapered roller bearing (TR1)
11.03.2021

Value add - load monitoring
 Dashboard example for load monitoring at Astraios test bearing arrangement:
Online load monitoring at main bearing in
• determination of load level and load
distribution at main bearing directly as
input for…
• …full usage of Schaefflers BearinX tool to
assess used life time, static safety, contact
pressures, or to evaluate impact of e.g.
storm events on individual turbine.
• determination of hub loads and moments.
11.03.2021

Value add - load monitoring
Deriving load
components at hub:
 Fx
 Fy
 Fz
 My
 Mz
Load measuring with Load Pin
1
3
Assessing structural
health of fundation
with load spectra at
hub position
4
2
Bearing and drive
train analytics
Online load monitoring at main bearing in
• determination of load level and load
distribution at main bearing directly as
input for…
• …full usage of Schaefflers BearinX tool to
assess used life time, static safety, contact
pressures, or to evaluate impact of e.g.
storm events on individual turbine.
• determination of hub loads and moments.
Customer value add:
• Understand the impact of the turbine
control on bearing and drive train load
• Develop life time optimized control
parameters
• Potentially extend input parameters for
control mechanism
• Structural health assessments
Mxy
n
Damage sum: Dactual = 0,7
Mxy actual vs Mxy expected
11.03.2021

3 Outlook
Wind 4.0
TCP/IP
interface to
additional
infromation sources
e.g. SCADA, Clouds of
wind turbine
operators, OEMs and
other suppliers,
weather information
(e.g. BLIDS)
extended
assessment of
measured values
e.g. ConditionAnalyzer
Displaying the
results
e.g. dashboard
(ideally on a real time
basis)
signal-processing
e.g. downsampling,
data classification
(means,
distributions,…)
first assessment of
measured values
e.g. generating of
alarmsin form of a
traffic light approach
communications-
interface
(„Edge-Device“)
signal pre-processing (= „PPM – Pre-Process Module “)
e.g. signal amplification/ -conversion, order analysis
e.g. LoadSense-Pin: transferring mV- into kN-signals
sensors
Scada
(or 3rd party device)
(„MPM – Main Process Module“)
OEM
Operator
Operator
11.03.2021

27
Agenda
Bemas
2
3
4
1
11.03.2021

Digital Twin and Produkt Lifecycle
28
Production
Test rig: Astraios
Target: Field application
Development and validation
Operation and service
Digital Twin @ Schaeffler
11.03.2021

3 Digital Twin bei Schaeffler
Important features of the Digital Twin at Schaeffler
29
At the moment there is a rapid development of new digital services at the big cloud provider (e.g. Microsoft
Azure). We make use of it.
Integration of domain experts from specialist areas as co-creators of Digital Twin solution
The missing data from practice can be provided by simulation
Adaptation of Schaeffler simulation tools that greatly simplify the development of Digital Twin for
engineering engineers
…
11.03.2021

SIMPLA : NOTRE PLATEFORME DE SIMULATION DYNAMIQUE
le lien entre les outils de simulation et les produits Schaeffler
30
Process de simulation dynamique qui intègre les
connaissances Schaeffler a tous les niveaux
Toolchain @ Schaeffler
11.03.2021

Digital Twin Architectur
31
Test rig: Astraios
8x load sense pins
Simulation
Machine Learning
model
Application engineer
Consistent data flow from the test bench to
the online dashboard was built on the basis of
in-house developments by Schaeffler
11.03.2021

3 Digital Twin bei Schaeffler
Integration du savoir-faire Schaeffler dans l’infrastructure client
32
cloud to cloud communication
Video Hannovre 2018
Exemple intégration ZF
Cloud Schaeffler
Cloud client
11.03.2021

33
Agenda
Bemas
2
3
4
1
11.03.2021

Schaeffler Arcanol lubricants
Product Management Aftermarket Accessories
Arcanol in new
SCHAEFFLER
design since Q4/2020.
Portfolio: 18 premium bearing greases. 3 local standard greases.
2 auxiliary service products
Load400 & Load460 special application
- Main shaft SRB, CRB, TRB
- High loads with vibrations and shock
- False brinelling protection
- Low startup friction
- Other positions:
- Multitop for generators
- Load150 for slew ring
11.03.2021

Reconditioning Bearings
11.03.2021

Le condition monitoring et la détection rapide des problèmes aident au développement
du marché du reconditionnement
36
Recon Service
Condition Monitoring detects
bearing damage on early stage
Equipment is operating with
Condition monitoring Installation of reconditioned bearing
and start of operations
11.03.2021
1 Présentation Schaeffler : Pourquoi l’éolien est-il important pour nous

Advantages du Reconditionnement
Reductions in life cycle costs (LCC)
Increases in operating life
Savings in material and energy costs
Reductions in inventory costs
Short lead times
Feedback on the characteristics and frequencies of damage
Reconditioning according to Schaeffler quality standards
Reduction of the CO2 footprint
High flexibility
Independent from OEM
Ready-to-mount return shipment
37
11.03.2021

38
Reconditioning Levels 0 - 4
 Level I plus
 Polish components
 Disassembling
 Clean components
 Inspection
 Assessment of
components
 Measure components
 Assessment report
Level I
Requalifying
Level II
Refurbishment
Level III
Remanufacturing
Level IV
Remanufacturing
Grease/ preservation and packaging of rolling bearings
 Level II plus
 Regrind functional
surfaces
 Replace components
(e.g. rolling elements,
cage)
 Level III plus
 Replace rolling
bearing rings
 Coating
Level 0
Pre-qualificaton
In the field on site
(FSE or Distributor)
u Clean for selection
u Inspection / view to
possible using or service
case Level 1-4
u Assessment of
components
u Documentation with result
report
u Creating cost and delivery
offer
u Transport to next
responsible Schaeffler
location
11.03.2021
1 Présentation Schaeffler : Pourquoi l’éolien est-il important pour nous

MERCI pour votre attention
11.03.2021
39
39
39

PRODUCED BY
Philippe Mol
Technical Manager
P4A
Olivier DENGIS
4.0 Solutions Officer
I-care Group
Comment optimiser
la performance, la
maintenance et la
durée de vie des
éoliennes

Agenda
 Identification et compréhension des problématiques de performance et
de maintenance
 Exploitation des données opérationnelles brutes à la recherche des
causes de perte de performance – cas pratiques
 Approche prédictive combinée pour diagnostiquer et prévoir les
défaillances machine – cas pratiques
 Chatbot au service des gestionnaires et maintenanciers de parcs éoliens

3 - Copyright © 2020 – Do not use without permission or proper licence from the author or rights holder
Wind Context
• European Union (EU) is aiming to boost the proportion of energy generated from
renewable sources
• In the energy sector, optimizing asset performance and deploying maintenance
teams efficiently can have a huge impact on output.
• As demand for wind energy grows, producers have the chance to make huge gains—
if they can tackle the issues that limit production levels. Apart from weather
conditions, which wind energy producers cannot control, the other main factors that
affect output are asset performance and availability

Identification et compréhension des problématiques de
performances et de maintenance

Industrie 4.0 : La feuille de route
COLLECT ACT
ASSESS IDENTIFY GENERATE ANALYZE VISUALIZE
a
APPS
Augmented Reality
Automated
Decision Making
a
CMMS
PROJECT MANAGEMENT & TRAINING (CONSULTANCY & COACHING)
CONTINUOUS IMPROVEMENT (RELIABILITY)

Problématiques de performance
Main performance degradations causes :
• Aerodynamical
o Yaw misalignment
o Blade Erosion or Pitch Calibration
• Electromechanical
o Generator degradation
o Power converter degradation
o Transformer degradation
o Controller (regulation issues)
The distinction between aerodynamic and electromechanical causes is made through the analysis of power
and torque curves

Problématiques de performance
Main performance degradation causes
“Effects of Yaw Error on Wind Turbine Running Characteristics
Based on the Equivalent Wind Speed Model”*
*Energies 2015, 8, 6286-6301; doi:10.3390/en8076286
Yaw Misalignment Pre-assessment

Problématiques de maintenance
Vertical Axis
Direct drive

Wind Turbines applications – Overview – Component identification
• Gearbox drive
V
²
²
V
²
²
V
²
²
V
²
²
V
²
²
V
²
²
V
²
²

Wind Turbines applications - Drive train Failure modes
Gearbox

Wind Turbines applications – Drive train Vibration frequencies
Gearbox description : most common :
PREDICTIVE : what are the technologies to address the failure
modes :
- Vibration : On line / Off line ?
- Oil / Grease Analysis
- Endoscopy

Exploitation des données opérationnelles brutes à la
recherche des causes de perte de performance – cas
pratiques

Introduction
Analyse et modélisation

Identification of the best wind turbine in the wind park and identification of performance
degradation based on estimated power curve
Cas 1 – Benchmark Performance

Estimated power curves
BEST
turbine
WORST
turbine
OEM
standard

Identification of Performance degradation using SCADA data
Aerodynamic vs Electro-mechanical degradations
Torque curve
power set point calculated from on
generator speed
Power curve
Deviation observed on the power curve and matching on the torque curve provide
decision of aerodynamic degradation.
1372
1213

Yawing system vs Blade roughness and/or miscalibration
Reference error distribution of
the best turbine
Error distribution of the
underperformed turbine
Mean error shifted toward the negative value and the deformation
of the error distribution shape.
Performance degradation is more probably due to blade roughness
and/or blade miscalibration.
Validation: blade inspection
Blade roughness identified
from the SCADA data has
been confirmed with blade
inspection
VS

Cas 2 – Désalignement nacelle

WTG WT 01 WT 02 WT03 Sum
Yaw misalignment 17 deg 24 deg 17 deg
Gain of Production
MWh
172 658 272 1103
Revenue 16 081 € 61 2015 € 25 365 € 102 662 €
Mean wind speed = 6.3 m/s
Context
• Electricity price, 93 €/MWh
• Wind condition, mean wind speed : 6.3 m/s
• Raw availability : 97 %
• Annual production : 4 612 MWh
Wind distribution during 2017
Illustration WTG02
Gain of production,
Sum 658 MWh
Cas 2 – Désalignement nacelle

Approche prédictive combinée pour diagnostiquer et
prévoir les défaillances machine – cas pratiques

Unpredictable
Failures
Source : PwC – Beyond the hype - 2018
Big
Data
&
Statistics
Reliability
• Visual inspections: periodic physical inspections; conclusions based solely on inspector’s expertise
• Instrument inspections: periodic inspections; conclusions based on a combination of inspector’s expertise and instrument readouts
• Real-time condition monitoring: continuous real-time monitoring of assets, with alerts given based on pre-established rules & critical levels
• Predictive maintenance with big data analytics: continuous real-time monitoring of assets with alerts sent based on predictive techniques such as regression
analysis.
Level 1
Visual
inspections
Level 2
Instruments
inspections
Level 3
Real-time
condition
monitoring
Level 4
PdM 4.0
Predictive Maintenance maturity Level
21

Wind Turbines applications – PDM technologies
The variety of the technologies will make the
success of any PDM program in order to address
the right failure mode !!

Wind Turbines applications – Inspections

Wind Turbines applications –endoscope inspection

Wind Turbines applications – IR Thermography

Wind Turbines applications – Vibration
CASE 1 : High speed shaft bearing fault – Vibration analysis – Endoscope inspection (Annual inspection)
2.5 MW – 11 years old

Wind Turbines applications – Vibration + Endoscopie
CASE 1 : High speed shaft bearing fault – Vibration analysis – Endoscope inspection (Annual inspection)
2.5 MW – 11 years old
Actions needed :
- Replacement Up-tower of all bearings of the High-speed shaft of the gearbox within
3 months
- Check coupling alignment after repair
+ Cost limited action
- Unplanned repair + turbine stopped

Wind Turbines applications – Case studies
CASE 4 : Rotor bearing fault (main bearing) – Vibration analysis – Visual inspection – grease analysis
1.5 MW – 14 years old –
Grease analysis :
WT5 New bearing VS WT1 damaged

CASE 5 : Generator – Electrical measurement - offline
2.5 MW – 11 years old – (trouble shoot)
- Initial problem : Visible crack on the input pipe of the heat exchanger above the generator
-  potential Cooling leak on the exchanger above the generator
 Potential effect on the generator insulation  insulation test

CASE 5 : Generator – Electrical measurement - offline
2.5 MW – 11 years old – (trouble shoot)
15/1/2019 : first fault detection, rotor insulation weaked, stator
unharmed
A still acceptable DD rotor indicate a probable moisture effect and not
a definitive weak point
1/3/2019 : Quickly after water leak fix , rotor still very low, stator
weakened
26/4/2019 : After hours of running, machine dryed and all value back
to good condition

Chatbot au service des gestionnaires et
maintenanciers de parcs éoliens

Dashboard
 Display information about the assets through dashboards on a web platform
 Development of a chatbot
Give back information generated in messages and clear recommendations for the asset
manager

Dashboard
Deployment of a Chatbot allowing to
« understand the users intents.
And give back through messages the
information generated by the models.

I-care
The European leader in predictive industrial
maintenance
€42m
revenue in
2019
450 employees
15 countries

Performance for Assets
 Company created after > 8 years of R&D
 Founded in 2017 by 3 companies specialized in :
o Maintenance
o Reliability
o Inspection of Industrial assets
 Joined force with experts in Business intelligence and Data Science
TARGET : Providing a solution to customer needs in the field of Predictive
Maintenance and Performance Optimization on industrial assets and
processes
 Why ? to create value from heterogenous data sources
 How ? Through hybrid data modelling approaches and consequently to move from
equipment and process data to actionable insights
 Where ? Solutions are hosted in a scalable platform, cloud-based or on-premise, able to
combine all technologies of sensors and data formats.

PRODUCED BY
Philippe Mol
Technical Manager
P4A
Olivier DENGIS
4.0 Solutions Officer
I-care Group
Comment optimiser
la performance, la
maintenance et la
durée de vie des
éoliennes
Olivier.Dengis@icareweb.com
Philippe.Mol@P4A.be

Q & A
Comment optimiser la performance, la maintenance et la
durée de vie des éoliennes?

Merci!
Comment optimiser la performance, la maintenance et la durée
de vie des éoliennes?
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Webinaire : Comment optimiser la performance, la maintenance et la durée de vie des éoliennes ? | BEMAS-TWEED - 18 mars 2021

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