Research Huddle - Canadian Wind Energy Association … · Research . Huddle • University of...
Transcript of Research Huddle - Canadian Wind Energy Association … · Research . Huddle • University of...
January 30, 2019
Research Huddle
• University of Windsor• Concordia• University of Calgary• Nergica• WEICan
YR21 energy investment decision support system
IEA.TEM#93.12.13.2018.DTU.DENMARK
Rupp Carriveau, Lindsay Miller, David S-K Ting, Milad Rezamand, Jones Shen, Luke Norman
Matt Davison, Tim Newson, Hanping Hong
JJ Davis, Jordan Regnier
Scott Harper, Marianne Rogers
David Watkins, John Bridges
YR21 energy investment decision support system
motivation
How to make the most profitable investment decision for my wind farm?
YR1
YR5
YR10
YR15
YR25
YR20
INVESTMENT DECISION
DO NOTHING?
REPAIR?
REPLACE?EXPAND?
REMOVE?
issue
YR21 energy investment decision support system
overarching objective
objectiveTo deliver a system that educates stakeholders to make more informed, better decisions that ultimately improve energy business.
Investment
Valuation Model
[IVM]
Wind Resour
cePPA / Open
MarketStorag
e
Trans-
mission
Cost of
Capital
TaxesLand-owne
r
Environment
al Compliance
Curtail-
ment
Insurance
Maintenance
Social
License
YR21 energy investment decision support system
methodology
concept
RemainingUsefulLifeEstimation
PowerPrice
YR21 energy investment decision support system
methodology.actionplan.mantra
progressively determinant approachGet an answer today! Make the best assumptions you can with the resources you have at the time. Improve those assumptions as you go along.
move closer to your ultimate objective
better inform decisions in the mean time
Wind Turbine Fault Monitoring Research at Concordia University
Department of Mechanical, Industrial and Aerospace Engineering (MIAE)
January 2019By
Hamed Badihi, Ph.D.
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Future CMS• Run in real-time
• As self-directed as possible
• Detect and diagnose early signs of faults
• Fast estimation of RUL
• Minimum need for complicated data analysis
• Less sensors / more reliable
• Valuable information for control reconfiguration!
• …??
3
Faults in Sensors, Actuators and Components
Photo courtesy of The Lubrizol Corp. Photo from www.openpr.com
Research Theme
RevenueO&MLower O&M costs
Higher reliability and availability
Real-Time Fault Monitoring andFault-Tolerant Control
Condition-Based Maintenance
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Benchmark Models
Wind Turbine Properties Rating 5 MW Rotor Orientation, Configuration Upwind, 3 BladesControl Variable Speed, Variable PitchRotor, Hub Diameter 126 m, 3mHub Height 90 mCut-in, Rated, Cut-out Wind Speed 3 m/s, 11m/s, 25 m/sCut-in, Rated Rotor Speed 6.9 rpm, 12.1 rpmRotor Mass 110,000 kgOptimal Tip-Speed-Ratio 7.55Rated Generator Speed 1174 rpmRated Generator Torque 43,100 NmMaximum Generator Torque 47,400 Nm
Wind Turbine Benchmark Wind Farm Benchmark
SimWindFarmDeveloped by Aeolus Team
FASTDeveloped by U.S. NREL
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Innovative TechniquesFault-Tolerant Control (FTC)
Wind Turbine Control
Operational Control
FatigueLoad
Control
Wind Farm Control
Power Quality Control
Power Dispatch Control
Wind Farm(Wind Turbines)
Control commands
Real-Time Condition Monitoring
Fault Detection & Diagnosis
(FDD)
Lifetime Prognosis
(LTP)Control reconfiguration data
Regulated outputs
Sensor measurements
Health information
Supervisory control commands
Supervisory Control
Any fault(s) detected?
Controllable?
Health information
NO
YES
Continue operation
YES
NO
Shut down and idle
Continue operation based on the system’s
health status and using FTC
Sensor measurements
Faults
25% Reduction in ActuatorEffectiveness
50% Reduction in Actuator Effectiveness
Fault-Free OperationFaulty Operation : FTIPC - Fixed PI IPCFaulty Operation : CPC and Fixed PI IPC
Fault-Free OperationFaulty Operation : FTIPC - Adaptive PI IPCFaulty Operation : CPC and Adaptive PI IPC
290 300 310 320 330 340 3500
5
10
15
Time [s]
Pitc
h An
gle
[deg
]
290 300 310 320 330 340 3500
5
10
15
Time [s]
Pitc
h An
gle
[deg
]
25% Reduction in ActuatorEffectiveness
50% Reduction in Actuator Effectiveness
290 300 310 320 330 340 3500
5
10
15
Time [s]
Pitc
h A
ngle
[deg
]
290 300 310 320 330 340 3500
5
10
15
Time [s]
Pitc
h A
ngle
[deg
]
Example Results for FTC
Example Results for FDD
0 200 400 600 800 10000
0.2
0.4
Time [S]Powe
r Los
s [MW
] Estimated Fault Magnitude T1
0 200 400 600 800 10000
1.0
Time [S]Faul
t Ind
icato
r [-] Fault Indicator T1
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University of Calgary Work on Wind Turbine
Asset ManagementDavid Wood
Qiao SunEhsan Mollasalehi
Hadi Sanati
CanWEA, Jan 30, 2019
Upcoming Project: Generator current analysis for drivetrain fault detection
Condition Monitoring of Generator Bearings
Wavelet transform to decompose and RMS to calculate energy
Frequencies suggest outer race bearing fault which was found by cutting open the bearing
Found optimum times of day to take IR images
Most features and damage locations were found
A special “defect plate” was used with holes of known diameter and depth
Passive Thermography of Damaged Blade
Real time remote monitoring of blades’ dynamic and static condition Detect and track development of multiple failure modes – remotely from the ground
Vibrations (internal defects/damage, imbalance) Deformation (wind loads) Displacement (misalignment) Angular speed Transient effects Pitch angle dynamics Cracks Erosion Lightning Damage Delamination Icing thickness Pitch angle Other changes to aerodynamic profileNo shut down required
NOT A LIDAR
Laser Vibrometer
Test rig to develop laser scanning
© 2
018
Ner
gica
–Al
l rig
hts r
eser
ved
© 2
019
Ner
gica
–Al
l rig
hts r
eser
ved
Our Mission
Services Activity Sectors
Creating new opportunities for renewables
The natural progression of the TechnoCentre éolien
Our OrganisationCollege Centre for Technology TransferLocated in Gaspé, QC30 employees
Technology Development and AssessmentOperation and MaintenanceCold Climate SuitabilityCommercialization of InnovationsEvents OrganizationApplied Meteorology and Resource assessmentMicrogridsEnergy Storage and Grid Management
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Research Infrastructure
© 2019 Nergica – All rights reserved
Nergica owns and operates research infrastructures in a natural setting
4 MW windfarm (2 Senvion MM92 CCV)
16 kW solar plant
230 kW wind-solar-diesel-storage microgrid
Fully instrumented metmasts (2x126 m, 80 sensors)
Lidar
OSIsoft PI data archving system
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Research Projects - Wind
© 2019 Nergica – All rights reserved
A few examples
Performance evaluation of wind turbines in icing
Ice prediction model for wind farms
Impacts of climate change on wind energy potential
Use of drones for wind turbine inspections
More info: https://nergica.com/en/publications/
Contact usCharles Godreau, P.Eng., M. Eng. Matthew Wadham-Gagnon, P.Eng, M.Eng.
Project Manager, Research and Innovation Business Development Manager
[email protected] [email protected]
Our main financial partners
Wind Energy Institute of Canada Research Plan
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Major Themes
Goals
Resources
Outputs
Asset Management/Service Life Estimation
Identify trends in maintenance
issues
Wind R&D Park
Publications: peer reviewed papers, book chapters, magazine articles, technical reports,
handoutsOral and Poster Presentations
Increased collaborators
Increased funding
Skilled Technical
Staff
Identify causes for underperformance
and component wear
More than 30 years of data
300o exposure to the ocean, strong wind resource, highly corrosive environment, large
winter/summer temperature differences
Strong industry, utility, government, and academic ties
Develop tools to decide whether to maintain assets, expand operations,
or discontinue investment as turbines age and warranties end
Map structural
aging
Integration of Wind to the Electrical Grid
Determine how to increase wind energy in the electrical grid
Recognition as experts: Asked to review abstracts, proposals, publications; asked to represent Canada at international
meetings, invited talks, VPPP participation
Wind park and battery operation
improvementsOutcomes
Data Management
System
Highly Qualified Personnel
PI Data Historian
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INTERNET
DeWind Scada
IPC
CMaS CMaS CMaS CMaS CMaSBlade
conditioning
CMaSDatabase
Blade Conditioning
Database
Turbine Network
Kepware server
SEL RTAC 3530
SubstationSEL
devices
VTS HMI & ServerMECL
System
Substation
Campbell ScientificDATA PC
END USER
Windmatic Wesnet
PI Servers Office
Weather Station
Wind ParkMet. Tower
WEICan SiteMet. Tower
Meteorological Data
ION 7650
MECL &WEICan
ION 7650
CR1000 CR1000
ION 7650
MECL &WEICan
PI Data historian provides a central location to access all recorded data, for monitoring, analysis, and reporting