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8/19/2019 Condition Monitoring Framework
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Smart Asset Management
Stephen McArthur s.mcarthur@eee.strath.ac.uk
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Drivers
Key requirements:
– State and health of assets
– Real-time rating
– Prognostics
Condition monitoring is increasing:
– In terms of new sensors and sensor technology – In terms of more condition monitoring systems
– In terms of deployment, both on-line & offline
Improved engineering support is necessary:
– In terms of managing and interpreting data
– In terms of corroborating evidence from different sensors and monitoringsystems
– Provision of decision support
Tap changer
•Temperature
•Vibration
Conservator tank
Cooling radiators
Fans
•Load current
Main tank (3 phases)
•Temperature
•Vibration
•Acoustic
•Internal UHF probe
Cooling circuit
•Dissolved gas
•Temperature(external and internal)
•Moisture
Oil pump motor
•Temperature
•Load current
•Vibration
Environment
•Solar radiation
•Wind speed and direction
•Atmospheric pressure
•Relative humidity
•Precipitation
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Smart Asset Management
New sensor technology, artificial intelligence and advanced software
techniques to embed intelligence within plant and equipment,integrated with:
Knowledge/models of physical behaviour
Knowledge/ models of degradation
Materials knowledge
Statistical models of asset performance
Self-learning monitoring and diagnostic systems:
Adapt to new plant and equipment Can diagnose defects in the absence of detailed experience of
applying the monitoring technologies
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Unified Model of Plant
Combine:
• Online monitoring data
• Codified expertise / knowledge
• Statistics-based health &
degradation models
• Physics based degradation
models
Using:
• AI based methods
• Statistical techniques
On-line Condition Monitoring
Asset Management Decision
Making Under Extreme
Uncertainty
Optimal Condition
Monitoring Policies
Continuous Learning of Asset
Behaviour and Degradation
“Physical” Models of Asset
Degradation
ASSET PROGNOSTICS
Optimal Outage Planning
ASSET MANAGEMENT
DECISION
METHODS
Strategic Management
of System Assets
“Statistical” Models of Asset
Degradation
ASSET MANAGEMENT PROGNOSTIC FRAMEWORK
Across multiple plant items
Reusable, generic, framework
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•Local intelligence
•Local data management
•Local intelligence
•Local data management
•Local intelligence
•Local data management
•Local intelligence
•Local data management
Substation D
Substation BSubstation C
Substation A
Link condition monitoring with utility asset
management systems – combine business
and technical information
Combine condition monitoring with real time
network control decisions
Unlocking the true value of CM
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EPSRC AMPerES Demonstrator
- Two sister transformers
- Manufacturer: GEC Witton
- 275/132kV, 180MVA
- One fine, one in poorer health
- Transfix on-line dissolved gas monitoring
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Tap changer
•Temperature
•Vibration
Conservator tank
Cooling radiators
Fans
•Load current
Main tank (3 phases)
•Temperature•Vibration
•Acoustic
•Internal UHF probe
Cooling circuit
•Dissolved gas
•Temperature(external andinternal)
•Moisture
Oil pump motor
•Temperature
•Load current
•Vibration
Environment
•Solar radiation
•Wind speed and direction•Atmospheric pressure
•Relative humidity
•Precipitation
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SGT1 sensorsOil cooling circuit
Gases (Transfix & Hydran), top oil temp., bottom
oil temp., bottom oil humidity
Main tankExternal temp. (6), vibration (4), acoustic
emission, oil pressure
Pumps (2) Temperature, vibration, load current
Fans (4) Load current
Environment Weather station, solar radiation
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SGT2 sensorsOil cooling circuit
Gases (Transfix & Hydran), top oil temp., bottom
oil temp., bottom oil humidity
Main tankExternal temp. (6), vibration (4), acoustic
emission, oil pressure
Pumps (2) Temperature, vibration, load current
Fans (4) Load current
Environment Weather station, solar radiation
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User requirements•Generic handling of data sources
•Learn per-item normal behaviour
•Periodic re-learning
•Conventional data interpretation
•Retain all data
•User interface is important
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How do we deliver a Smart Grid which “employs innovative products
and services together with in te ll igent moni tor ing , cont ro l ,
communicat ion , and self heal ing technologies” ?
Distribute intelligence and control:
Provide localised autonomy within the power system
Break down the complexity
Manage and interpret data locally
Arbitrate and co-operate globally
Implement automated data interpretation techniques
Automatically aggregate interpreted data into meaningful information
Provide “plug and play” architectures – flexible and extensible
Deliver tailored information to support various engineering functions
Control centres
Asset managers
Field support
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Extensible & Dynamic Architecture
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Anomaly Detection
- A class of machine learning techniques
-Potential for false alarms
-Therefore, Conditional Anomaly Detection
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Conditional Anomaly Detection
-Aims to reduce false alarms
-Classifies an anomaly if context doesn’t explain outliers
– Context is environmental weather parameters
– Only looks for anomalies in transformer data
– Uses statistical models of environment and
transformer parameters
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The need for wireless sensing
• A generic diagnostic condition monitoring architecture has been
created through AMPerES
• A number of novel sensors have also been developed through
AMPerES
• However, deployment of new sensors is challenging..
Widescale deployments can be underpinned by Wireless
Sensor Networks (WSNs) with in-built data processing anddiagnostics
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Partial discharge diagnostics:
The conventional approach
Integrate into a wireless CM sensor
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Sensor architecture overview
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Wireless sensor
technology
Knowledge-based
Diagnostics
++
Agent-based architecture
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How do we deliver a Smart Grid which “employs innovative products
and services together with in te ll igent moni tor ing , cont ro l ,
communicat ion , and self heal ing technologies” ?
Distribute intelligence and control: Provide localised autonomy within the power system
Break down the complexity
Manage and interpret data locally
Arbitrate and co-operate globally
Implement automated data interpretation techniques
Automatically aggregate interpreted data into meaningful information
Provide “plug and play” architectures – flexible and extensible
Deliver tailored information to support various engineering functions
Control centres
Asset managers
Field support
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