Condition Monitoring Framework

8/19/2019 Condition Monitoring Framework

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Smart Asset Management

Stephen McArthur [email protected]


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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







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

Documents

Condition Monitoring Framework