KONGSBERG - SINTEF · 2015. 2. 11. · Kongsberg Maritime Subsea – Geophysical, environmental...
Transcript of KONGSBERG - SINTEF · 2015. 2. 11. · Kongsberg Maritime Subsea – Geophysical, environmental...
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KONGSBERG “Optimized wind farm operation” EERA DeepWind 2015 Trondheim 6 Feb 2015 Oddbjørn Malmo General Manager Technology Kongsberg Maritime, Trondheim
Presentation overview
• This is KONGSBERG • Kongsberg Wind Farm Management System (WFMS)
– Performance Monitoring – Wind Farm Control – Production Forecasting – Condition Monitoring
• Contributions from the «Windsense» project • Cost savings potential
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At its core, KONGSBERG integrates advanced technologies into complete solutions
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Key core capabilities
• Integrating sensors and software
• Supporting human decision making, precision, safety, security
• Cybernetics, software, signal processing and system engineering
• Project and supplier management
Dynamic positioning and vessel automation
Real time drilling support
Advanced robots
Command and control systems
International high-tech solutions, from deep sea to outer space
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Advanced solutions and applications for the maritime, oil & gas, renewable wind, defence and space industry.
- Extreme Performance for Extreme Conditions -
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PROPULSION CONTROL
CARGO MONITORING & CONTROL
NAVIGATION
POWER MANAGEMENT
THRUSTER CONTROL
ALARM & MONITORING
PROCESS CONTROL
REMOTE DIAGNOSTICS
LOGGING & REPORTING
Kongsberg Maritime - merchant marine products
Maximizing marine performance by providing the Full Picture
Kongsberg Maritime ”The Full Picture” for Maritime and Offshore applications To be extended to Wind Power applications
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Cable plough – Dynamic positioning – Acoustic positioning
Turbine installation – Dynamic positioning – Marine automation
Wind Farm Management – Condition Monitoring – Wind Farm Control – Production Forecasting – Performance Monitoring
Fish and mammal survey – Acoustic impact assessment
Geophysical survey –AUV survey –Seabed mapping –Sub bottom profiling
Cable installation – Dynamic positioning – Marine automation
Foundation installation – Dynamic positioning – Marine automation – Underwater positioning – Underwater sensors – Acoustic impact monitoring – Sonar monitoring – Camera monitoring
ROV – Acoustic positioning – Inertial navigation – Camera monitoring
Traffic and Security systems – Surface surveillance – Sub-surface surveillance – Submarine warning – Vessel tracking systems
KONGSBERG Onshore/Offshore Wind
Kongsberg Maritime Subsea – Geophysical, environmental monitoring & inspection
Products: • Sonar’s and echo sounders, multi-beams
during inst., sonars, sub bottom profilers, 3D monitoring/inspections
• AUV – inspection survey and monitoring • C-node sensor network - CMS • Subsea cameras for inspection – ROV/AUV
mounted • Listening devices – subsea noise, sea
mammals
Consent Baseline studies
Planning
Inspection Environmental impact assessment
Corrosion Deformation Marine growth Scouring Cable tracing & pipe laying
Noise
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Presentation overview
• This is KONGSBERG • Kongsberg Wind Farm Management System (WFMS)
– Performance Monitoring – Wind Farm Control – Production Forecasting – Condition Monitoring
• Contributions from the «Windsense» project • Cost savings potential
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Kongsberg Wind Farm Management System Integrated decision support system for optimal performance and minimized downtime and operational costs
Modules: • Conditioning Monitoring with enhanced
analysis of turbine data • Production Forecasting through improved
weather analysing tools/ algorithms • Wind Farm Control with dynamic
production optimizer reducing wake and turbine loads
• Performance Monitoring; reporting, fault analyses, trending and benchmarking of turbines and wind farms
Production optimizer, load and wake control
Reduced down time and operational cost
Reduced imbalance Improved maintenance planning
Identify deviations Improved benchmarking
5-8% reduction in CoE
Performance Monitoring
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Power curve analysis
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Wind Farm Control
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INCREASED PRODUCTION BY DYNAMIC FARM OPTIMIZER • Individual set points for each
turbine based on: • Actual wind condition • Turbine state/condition
REDUCED TURBINE WEAR • Balancing of loads • Load mitigation • Reduced turbulence wear
Wind Farm Control – Production optimiser
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Total potential increased production 1 – 2 %
Production Forecasting
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IMPROVED PRODUCTION FORECASTING • Reduced imbalance cost • 48 hours predictions • Integration of WFC, CM and PF in
one system
IMPROVED MAINTENANCE FORECASTING • Predictions of maintenance weather
windows • Minimize production losses due to
maintenance stops
Production Forecasting
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Condition Monitoring
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Condition Monitoring – analysis tool
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2 – 3 months early warning
Inspect rear bearing Change grease €€€ Avoid unrepairable fault
Reduced down time and maintenance cost
The P-F curve combination of measurement techniques with different detection points
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20
40
60
80
100
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Tech
nica
l int
egrit
y [%
]
Time
Fault initiation
Catastrophic failure
Unrepairable
Acoustic emission Vibration
Temperature
Oil debris
Audible Earlier warning!
@ less false alarms!
F
P
Presentation overview
• This is KONGSBERG • Kongsberg Wind Farm Management System (WFMS)
– Performance Monitoring – Wind Farm Control – Production Forecasting – Condition Monitoring Methods
• Contributions from the «Windsense» project • Cost savings potential
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Condition Monitoring - online and offline monitoring • Sensor input
– Existing (SCADA data) – Add on sensors
• Fault detection – Automated analyses – Comparison with baseline data – Early warning of fault development
• Diagnostics – Add on high frequency sensors – Adaptive diagnostics
• Prognostics – Time to service – Remaining Useful Lifetime – Level of service – Enable predictive maintenance
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Signal Processing - Detection and Prediction
Earlier detection of faults by improved analysis methods: Compensate for process variations and environmental conditions by
mathematical modeling • Artificial Neural Network (ANN) • Heat balance models
– bearing temperature – electrical converter temperature
Next step: • Improved prediction of remaining useful lifetime (RUL)
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Detection of Rear Bearing Fault using ANN
• ①: The first noticeable deviation from the model occurred primo October 2010.
• The turbine stopped due to overheating twice (at ③ and ④) and was restarted by the operator
• ⑤: The turbine was permanently stopped
With the chosen thresholds the method gives
• 3 months pre-warning • 10 days earlier alarm than with standard
fixed alarm limits
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
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(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
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5
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15Difference Between Actual and Estimated Temperature
(b)
Time (yyyy-mm-dd)
4
52 3
1
4
1.5
Parameter Selection
• Indicator: Temperature
• Parameters that may influence or relate to the indicator: – Rear bearing temperature (t-1) – Active power output (t) – Nacelle temperature (t) – Turbine speed (t) – Cooling fan status
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Rear Bearing
Model Input
Rear bearing temperature (t-1) Active power output (t) Nacelle temperature (t) Turbine speed (t)
Model Output
Rear Bearing Temperature (t)
Model based approach
The actual process/system
Model correction method,
(e.g. Kalman filter)
Model
- 1
2
ˆˆyy
ˆ( )g x
1
3
3
ˆˆˆ
xxx
[ ]uInputs Actual Measurements
1
2
yy
[ ]u
ˆ( )y y−
Estimated state variable, e.g. bearing friction, not measured
Estimation of «bearing friction» front and rear bearing using unscented Kalman filter
Estimated friction in both bearings follow eachother well
Rear bear. friction sometimes above
Rear bear. friction always above
3 months
Generator side heat sink temperature of frequency converter with introduced faults Measurements vs model
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Complex impedance model for heat balance
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Focus on early fault detection - Acoustic emission?
Presentation overview
• This is KONGSBERG • Kongsberg Wind Farm Management System (WFMS)
– Performance Monitoring – Wind Farm Control – Production Forecasting – Condition Monitoring
• Contributions from the «Windsense» project • Cost savings potential
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3 year project (2012-2014): Funded by RCN Contribute to a 30 % reduction in Cost of Energy from Offshore Wind
WINDSENSE Add-on instrumentation for Wind Turbines
Windsense Work packages and time schedule
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Work package Responsible 2012 2013 2014 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 WP1 GAP analysis MARINTEK
WP2 Functional requirement specification STATOIL
WP3 Evaluation of sensing methods & eq. HIST
WP4 Dev. data interpretation algorithms NTNU
WP5 Implementation in CM system KM
WP6 Laboratory testing KM
WP7 Field testing at pilot turbine(s) STATOIL
WP8 Development of prediction algorithms SINTEF ER
WP9 Implementation of CBM system MARINTEK
WP10 Analysis of cost saving potential KM
WP11 Administration & dissemination KM
Windsense Illustration of data acquisition and analysis
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Wind Turbines
Degradation Process
Feature Extraction
Fault Diagnosis
Fault PrognosisMaintenance Scheduling / Maintenance Optimization
Signal Pre-process
Denosing Time Domain
Time-Frequency Domain
Frequency Domain (FFT, DFT)
Wavelet Domain (WT, WPT)
Principal Component Analysis (PCA)
Compression
Extract Weak Signal
Filter
Amplification
Support Support Machine (SVM)
Data Mining (Decision Tree & Association rules)
Artificial Neural Network (SOM & SBP)
Statistical Maching
Auto-regressive Moving Averaging (ARMA)
Fuzzy Logic Prediction
ANN Prediction
Match Matrix Prediction
Ant Colony Optimization (ACO)
Particle Swarm Optimization (PSO)
Gentic Algorithms (GA)
Meta-Heuristic approaches
Bee Colony Algorithms (BCA)
Key Performance Indicator (KPI)
KPI Leading
KPI Logging
Maintenance Management
System
Onshore Wind Turbine
Offshore Wind Turbine
Collapsed Wind Turbine
Data Acquisition
Fiber Bragg Grating Sensors
Acoustic Emission(AE)Sensors
Ultrasonic Sensors + AE
Vibration sensors
Wireless Data Collection Networks
Findings and further work
GAP analysis • A general lack of high frequency data • Limited use of advanced signal analysis • Limited data for lifetime prediction • Need for improved blade monitoring • Need for improved monitoring of high voltage components
CM methods – to be evaluated with respect to • Early and secure detection • Low false alarm rate • Reliable diagnostics • Cost/benefit ratio
Member of NCEI / 34 / KM: 2011-09-02
Prediction capability in dependence of size of populations
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Prediction horizon at different models
The challenge Improved methods and models for lifetime estimation
Presentation overview
• This is KONGSBERG • Kongsberg Wind Farm Management System (WFMS)
– Performance Monitoring – Condition Monitoring – Wind Farm Control – Production Forecasting
• Contributions from the «Windsense» project • Cost savings potential
11/02/2015 WORLD CLASS - through people, technology and dedication. Page 36
How will improved CM systems contribute to a reduced Cost of Energy for Wind Power:
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• Replace manual inspections with remote on-line measurements and analysis
• Implement automated diagnostics tools • Limit the number of alarms to those that are significant (KPI’s)
• Reduce consequential damages • Enable delay of maintenance until proper
weather window occur • Reduce downtime by more efficient fault
identification and diagnostics • Improve maintenance planning by better
diagnostics and estimation of remaining lifetime
Condition Based Maintenance (CBM)
Methods and systems employed in the offshore and maritime industry provides
• An indication of degraded performance or technical condition in a plant
• Efficient drill down capability • Triggers further investigation with analysis and
diagnostics, either through CM system or by manual inspection
• Includes decision support for intervention planning
Goal A substantial reduction in maintenance cost and increased energy production for offshore wind turbines by • A significant reduction in number of unplanned service trips • A reduced number of stops and less downtime • Controlled running at reduced load when this is safe rather than full shut
down until maintenance can be performed
KM: 2011-09-02 / 38 / Member of NCEI
Output to maintenance system
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Tech
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egrit
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Fault initiation
Catastrophic failure
Unrepairable
First detection
Confirmation RUL Estimate
F
P
Pre-warning
Warning
Alarm
1 2
3
4
−Warnings and Alarms - Fault diagnostics – root cause analysis - Estimates of remaining useful lifetime (P-F) - Dynamic updating of technical condition as input to RCM risk assessment
Operation & Maintenance Costs (O&M) • Condition Monitoring (CM) • Performance Monitoring (PM) • Condition based Maintenance (CBM)
1) Source: Wind Energy OM Report 2011
Life Cycle Cost: Pro-Active vs. Reactive Maintenance (Roeper 2009 1))
Cum
ulat
ed c
ost
(rel
ativ
e un
its)
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O&M Strategies for Wind Turbines
• 36 % (incl. the 27%) used reactive maintenance to some degree
• 63% used preventive maintenance somewhere in their wind farm portfolio
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27 %
50 %
23 %
Reactive (run tofailure)Preventive (timebased)Predictive (condition-based)
The most common O&M strategies (farm owners) Source: 7th annual Wind Energy O&K Forum
• Comparision with Oil&Gas (Norwegian sector)
• 60 % of the maintenance is preventive
• Target is 80 %
Thank you for listening! www.kongsberg.com [email protected]