the power of a single integrated database - osisoft...the power of a single integrated database...
TRANSCRIPT
![Page 1: The Power of a Single Integrated Database - OSIsoft...The Power of a Single Integrated Database Gábor Bús 1 #PIWorld ©2019 OSIsoft, LLC 2 Other European countries Customers and](https://reader035.vdocuments.net/reader035/viewer/2022070807/5f05f19e7e708231d4158120/html5/thumbnails/1.jpg)
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The Power of a Single Integrated Database
Gábor Bús
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Other European countries
Customers and shareholders
European Power Transmission System
World Largest interconnected grid
42 TSOs covering 35 countries
About 40 thousand network elements
700 Mio customers
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European Power Transmission System
Solar
6.000
1.0001 week
Wind
12.000
2.0001 week
1.500 MW
2.000 MW
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Coordinated Capacity Calculation
• More time horizons
2 Applications
Adequacy Assessment
• 1 Time horizon
1 Application
Common grid Merging
• More time horizons
2 Applications
Coordinated Security Assessment
• More time horizons
2 Applications
Outage Planning
• More time horizons
3 Applications
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The main 5 Services
24/7 shift operation
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Challenge #1: Integrate Source Systems
Diverging tools and environments
Overlapping processes during the shifts
Uncomfortable access of tools via VPN
Dynamic IDs of network elements
`
Enormous amount of data to process
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Solution #1
Integration of relevant data to PI
Pinpoint the what and where needs attention
Standardise network element IDs
Implement intuitive PI Vision Dashboards
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Solution #1
Integration of relevant data to PI
• Define the use cases
• Carefully asses the necessary data points (e.g. versions)
• 33 UFL interfaces
• 700k tags
Pinpoint the what and where needs attention
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Solution #1
Standardise network element IDs
• The diverging Identifiers are mapped to a Unique TSCNET ID
• Reference point used for mapping: R (resistance), X (reactance)
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TSC UID
Before After
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Solution #1
Implement intuitive PI Vision Dashboards
• Analyses templates: 205 (62k Analyses)
• Notifications template: 13 (13k Notifications rules)
• 8 PI Vision & 2 Processbook template Displays
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Benefits #1
Save 5 to 10 Man hours / week
Clear indicators for data quality issue
Ability to historize the data
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Challenge #2: ML with clean data
Explore Machine Learning potentials
Maintain clean & complete data source
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Solution #2
Introduce regular, automatic data check
Restore the data
Pilot project for ML
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Solution #2
Restore the data
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Solution #2
Introduce regular, automatic data check
Monitor
• Regular PI point updates (notifications rules)
• Disk space and memory on the PI application machines
• Scheduled backups
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Solution #2
Pilot project for ML
• Aim: Detect possible overloads without forecast data from TSOs
• Input data: Calendar, Weather forecast, (weekly outage information)
• Model training: Historical congestion forecast results
• Result: Good approximation on country and Time Stamp resolution
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Benefits #2
Lessons learned: clean data is 80% of ML or data analyses
It is proved that ML can be potentially used for enhanced services
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CHALLENGES SOLUTION BENEFITS
▪ Diverging source systems and IDs
▪ Too much data for human processing
▪ Historize and save data in a secure manner
▪ Potential in ML?
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▪ Integrated source system to a single database based on pre-defined use cases
▪ Run automatic analyses
▪ Inform operational staff via visualization and notification
▪ Maintain database health
▪ Less time and effort in analyses and reporting
▪ Higher level of database maintenance
▪ Proven ML concept for future services
Single Integrated Database
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Thank you for your attention
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• Gabor Bus
• Lead Data Visualization and Analyses
• TSCNET Services GmbH
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