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.

2

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

4

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

5

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

6

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

20

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

21

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

cgodreau@nergica.com mgagnon@nergica.com

Our main financial partners

Wind Energy Institute of Canada Research Plan

22

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

23

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