power transformer simulation laboratory for proactive
TRANSCRIPT
© Copyr i gh t 2014 O SIs o f t , LLC .
Presented by
Power Transformer
Simulation
Laboratory for
Proactive
Maintenance II
Nicolas Di Gaetano
& Luc Vouligny
© Copyr i gh t 2014 O SIs o f t , LLC .
Agenda
• An overview of Hydro-Québec
• Context of the project
• Objectives of the project
• Transformer monitoring
• Conclusion & Future Work
© Copyr i gh t 2014 O SIs o f t , LLC .
Snapshot
• Hydro-Québec is among the largest power
generator in North America
– 98% renewable energy
• Hydro-Québec is among the largest power
transmission companies in North America
• Hydro-Québec is the largest electric utility in
Canada
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About Hydro-Québec
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About Hydro-Québec’s Research Institute
(IREQ)
> Largest electric utility research centre in North America
• 500 employees of which half are researchers
• $100M invested each year on 100 projets
• 1000 patents
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Hydro-Québec TransÉnergie
• Transmission assets: $17.6 B
• 33,630 km of power transmission
lines
– Including 11,422 km of 735 kV lines
• 514 transmission substations
• Annual investment: $1,3 B
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17 interconnections with
neighboring markets
(> 6,000 MW)
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Context of the project
• Network: reliable and available
• Data: high-quality, value-added and just-in-time
• Decisions: appropriate, timely
• Power transformers are critical assets of the network
– Average age: 33 years old
– Their life expectancy: 40-50 years
• Transition from systematic to proactive maintenance
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Objectives of the project
• Maximize asset life
• Reduce the Risk & Cost of unexpected failure
• Drive maintenance and inspection by asset
condition
• Awareness of its condition and performance
• To maximize system availability
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Transformer monitoring
Real-time monitoring
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2 3 41 5 6MEET Project
Sensors,
IEDs,
Transducers
Communication
Infrastructure
Data Server
(PI System)Computational Intelligence
Diagnostics & PrognosticsWeb Interface
Sustainability
& Maintenance
Action
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Transformer monitoring: From past to future
• Use remote monitoring for all growth and asset sustainment projects– More than 500 substations over a 15-year timeframe (2009–2025)
• Objective: 30 to 40 substations a year
– Monitoring of 240 strategic transformers by 2015
• Gas and moisture
• Temperature
• Bushing and tap changer monitors (future deployment) – OSIsoft PI System
• Automatic addition of points (PI APS, PI GenericCSV_APS connector)• Maximizes use of PI AF templates
• PI AF SDK
200820072006 2009
IT infrastructure
2010 2011 2025
Business
case
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PI Webparts: Displays
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PI Webparts: Displays (continued)
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PI Webparts: Displays (continued)
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PI Notifications: Alerts
Remote Maintenance Centres
Technical Support
Managers15
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Transformer monitoring
Bushing
Dissolved
Gas
Thermal
Performance
Tap Changer
Moisture
and Aging
Mechanical state
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2 3 41 5 6MEET Project
Sensors,
IEDs,
Transducers
Communication
Infrastructure
Data Server
PI SystemComputational Intelligence
Diagnostics & PrognosticsWeb Interface
Sustainability
& Maintenance
Action
© Copyr i gh t 2014 O SIs o f t , LLC .
Acquisition, Estimation & Detection
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Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Simulated
data
PI
Points
Real
data
PI to PI
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Estimation
data
Asset
maintenance
model
Detection
logic
JMS/SI
Input Conn.
© Copyr i gh t 2014 O SIs o f t , LLC .
Thermal prediction with physical models
• IEC 60076-7 International Standard
– Loading guide for oil-immersed power transformer
• IEEE Clause 7 Non-Linear
• IEEE Clause 7 Linear
• Swift
• Susa
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IEC 60076-7 / Exponential Equations
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– Load varies as a step function
– Used in the determination of heat
transfer parameters
– Increasing load (κ)
– Decreasing load (κ)
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IEC 60076-7 / Difference EquationsTemporal variables
> κ : Load factor
> θo: Top-Oil temperature
> θa : Ambient temperature
> Dt: Time interval
• 1 minute
Constants:
> k11 : Model constant
• 0,8
> τO : Avg. oil time constant
• 150
> Δ θor : Top oil rise constant
• 50
> R: Load loss ratio
• 8,4
> x: Exponential power of current vs top oil rise
• 0,8
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- Both the load and the ambient
temperature are time-varying
- Adapted for monitoring purposes
© Copyr i gh t 2014 O SIs o f t , LLC .
Acquisition, Estimation & Detection
21
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Simulated
data
PI
Points
Real
data
PI to PI
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Estimation
data
Asset
maintenance
model
Detection
logic
Exponential
Equation
Difference
Equation
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Difference equation performance
Simulation
Estimation
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Real data
• 3 years of data
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Load from 0 to 1.35 pu
Ambient Temp. from -31°C to 33°C
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Performance with real data
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Top-Oil
Ambient
Estimated
Difference
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Performance over a year
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Results with physical models – 2009
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Results with physical models – 2011
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Results with clustering
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Neural networks
• Neural networks are nonlinear black-box
structures with “interesting” properties
– general architecture
– universal approximator
– non-sensitive to over-parametrization
– have learning algorithm to acquire knowledge from
their environment, using examples
– have recall algorithm to use the learned knowledge
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Basic Regressor Neural Networks
Current Inputs
(Static Networks)
Current Inputs
and Past Outputs
(Dynamic Networks)
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Matlab results for Neural Network
top-oil temperature prediction
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2011 data test (6 months)– Mean Absolute Error: 0.25%
– Mean Error : 0.1 °C
– Max Error : 1 °C
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Abnormal behaviour (data cleaning)
AmbT load Actual ANN
9.68 36 19.52 19.49
9.68 36 10.85 10.89
9.68 11739 146.90 15.81
9.68 12339 13.86 16.88
9.56 3 24.86 28.95
9.58 36 10.79 27.59
9.37 3 24.75 15.70
9.38 3 10.70 10.71
9.37 3 10.72 10.69
9.38 3 10.72 10.69
9.37 3 10.67 10.66
© Copyr i gh t 2014 O SIs o f t , LLC .
Neural Network Estimator
33
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Simulation
data
PI
Points
Real
data
PI to PI
Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter Microsoft StreamInsight
PI Server
Queries
TCP/SI Adapte
r
PI Input
Adapter
PI OutputAdapter
Estimation
data
Detection
logic
.dll
© Copyr i gh t 2014 O SIs o f t , LLC .
Neural Network prediction results with real data
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© Copyr i gh t 2014 O SIs o f t , LLC .
Future work
• Gradual implementation of models (2014-2018)– Thermal performance
– Dissolved gas
– Moisture and aging
– Tap changer and bushing
– Mechanical state
• Each model are developed with the following steps
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Specification Development Coding Testing Documentation Implementation
© Copyr i gh t 2014 O SIs o f t , LLC .
Proactive maintenance Advantages
• Transformers– Avoiding major failures
– Avoiding unavailability and captive power
• Equipment– Awareness about equipment condition as it
ages
• Sensors – Detection of malfunctioning equipment
– Improvements on many equipment settings
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© Copyr i gh t 2014 O SIs o f t , LLC .
- Transformers
– Avoiding major failures
- Equipment
– Awareness about equipment
condition as it ages
Solution Results and Benefits
Power Transformer Simulation Laboratory for
Proactive Maintenance
- Maximization of asset life
- Risk & Cost Reduction of
unexpected failure
- Maintenance and Inspection
driven by asset condition
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- Set up the infrastructure &
organization needed to ensure
real-time monitoring of
transformer condition
- Real-time data analysis with
predictive model
« Now that we have on-line monitoring possibilities
for maintenance purposes on power transformers,
our simulation laboratory will allow us to elaborate
and test many possibilities of proactive
maintenance models & technics before
implementing them in the field. »
Business Challenge
© Copyr i gh t 2014 O SIs o f t , LLC .
Nicolas Di Gaetano• [email protected]
• Fiability Engineer
• TransEnergie, Hydro-Québec
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Arnaud Zinflou• [email protected]
• Researcher
• IREQ, Hydro-Québec
Patrick Picher• [email protected]
• Researcher & project manager
• IREQ, Hydro-Québec
Luc Vouligny• [email protected]
• Researcher
• IREQ, Hydro-Québec
© Copyr i gh t 2014 O SIs o f t , LLC .
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