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OSIsoft Users Conference 2013
Mathieu Viau April 16th, 2013
AMI Data Integration into PI Using the CIM
2 Groupe − Technologie, Hydro-Québec
Agenda
> About Hydro-Québec & IREQ > R&D IT Challenges and Strategic Vision > Project Context and Methodology > Results > Conclusion and Future Work
About Hydro-Québec > Hydro-Québec is the largest electric utility in Canada
• 30% of electricity generation – Installed capacity of 36,671 MW – 98% renewable energy (hydro & wind power)
> Hydro-Québec is the largest power generator in North America • 15 interconnections with neighboring markets (> 5,000 MW) • Active in the following markets : Ontario, Maritimes, New-England, New-York,
PJM and MISO > Hydro-Québec is among the largest power transmission companies in North
America • $17 B in transmission assets
> 4 M Customers • Residential (41%), Large-power corp. (31%), Commercial (25%) and Others
(3%)
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4 Groupe − Technologie, Hydro-Québec
Hydro-Quebec
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• 33 630 km of lines
• 514 substations
• 37G of power
• 98% hydroelectric
• 4M customers
• 111 205 km of lines
• 23 000 employees
• 12B$ revenue
• 18B$ total orders
Hydro-Quebec’s Research Institute (IREQ)
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> Largest electrical utility research center in North America
• 500 scientists, technicians, engineers & specialists
• $100 M in innovation projects • Over 150 research projects • More than 1000 patents
5
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R&D IT Challenges > Projects with all business units > Heterogeneous sources and types of data > Very large volume of data > Projects have small IT funding, short time
frame
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Are you expecting SOA or ESB to solve that?
Made by André Desrochers, [email protected]
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Strategic Vision - Overview
Semantic SDK
Semantic Bridge Legacy Bus
Semantic Database
Queries
Relational Database
Mapping & Federation
Semantic Application
Machine learning Pattern matching
Data mining
Knowledge engineering (ontologies, rules,
uncertainty, …)
Semantic Integration Bus Domain Ontology
Legacy Application
Legacy Application
ODAS
IEC CIM SPARQL
Analytics, Data Mining,
Visualisation, BI
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Strategic Vision - Architecture
Synchronize
Work in progress
• Common domain ontology (CIM) • PI AF populated with CIM models
Project Context
> Objectives • Give access to AMI data for:
– Theft Detection – Low and medium voltage network analysis – Validation of network model
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Project Context > Efforts
• 5 months (September 2012 to February 2013) – 2 months for understanding the MDMS
model and getting an Oracle dump – 1 month for mapping the MDMS model to
CIM – 2 months preparing and importing data
and model into PI
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12 Groupe − Technologie, Hydro-Québec
AMI to PI Process
PI Server MDMS
EnergyICT
Oracle DB
Mapping File
(D2R)
topology
Cassandra (noSQL)
PI SDK
Network Operations
AMI values
CIM Instance
(RDF)
PI AF
CIM15 Schema
File (OWL)
AF SDK Element
AF SDK ElementTemplate
A B
C
A, B, C detailed in next slides
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A - ETL Route – From MDMS to PI Channel
Register
MDMS to CIM names
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B - PI Server Backfilling
R&D PI Server
Transmission PI Server
Oracle DB
Files Files Data Files
PI to PI Cassandra (noSQL)
ETL PI SDK
• Heterogeneous Sources and Data • Not in real-time • 200 000 tags, will increase to 1 000 000+ • Cassandra to re-order the data, speed the backfilling
process and enable compression
• 1 billion values - Power transformer monitoring
• 100 billion values - AMI data - SCADA - State Estimator
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B - PI Server Backfilling
PI Server
Cassandra
Slow
Fast
50 times faster for backfilling and enables PI compression
Tag1 V1 Tag1 V2 Tag1 V3Tag2 V1 Tag2 V2 Tag2 V3Tag3 V1 Tag3 V2 Tag3 V3Tag4 V1 Tag4 V2 Tag4 V3Tag5 V1 Tag5 V2 Tag5 V3Tag6 V1 Tag6 V2 Tag6 V3Tag7 V1 Tag7 V2 Tag7 V3Tag8 V1 Tag8 V2 Tag8 V3Tag9 V1 Tag9 V2 Tag9 V3Tag10 V1 Tag10 V2 Tag10 V3
insert 1 insert 2 insert 3
insert 1 Tag1 V1 V2 V3
insert 2 Tag2 V1 V2 V3
insert 3 Tag3 V1 V2 V3
insert 4 Tag4 V1 V2 V3insert 5 Tag5 V1 V2 V3
insert 6 Tag6 V1 V2 V3
insert 7 Tag7 V1 V2 V3
insert 8 Tag8 V1 V2 V3
insert 9 Tag9 V1 V2 V3
insert 10 Tag10 V1 V2 V3
ETL
C - Model Mapping (MDMS to CIM)
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MDMS CIM15
C - Model Mapping (MDMS to CIM)
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Table MDMS Classe CIM ou interprétation IRD_TRANSFORMATEUR PowerTransformer EISMPTFOLDER UsagePoint EMPLACEMENT_ADR_���ELECTRIQUE_ANONYMISE
Association entre PowerTransformer et UsagePoint
EISRTU Meter DRUMETERASSET Association historisée entre UsagePoint et Meter EISSYSRTUTYPE Informations additionnelles sur le type et modèle de compteur
DRUTRANSFORMERMULTIPLIER Association historisée entre UsagePoint et ServiceMultiplier
EISCHANNEL IntervalBlock XCHANNELDATA IntervalReading EISRTUREGISTER IntervalBlock EISRTUREGISTERMAPPING Pour obtenir le code OBIS des registres, ce qui servira au mappage
vers le ReadingType CIM EISRTUREGISTERSPEC Association entre les tables EISRTUREGISTER et
EISRTUREGISTERMAPPING EISRTUREGISTERREADING IntervalReading
CIM AF Element Templates
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• 1261 Element Templates • All CIM15 classes and
Attributes • Hierarchy kept through
parent/child relationships
ProcessBook – AMI Data (CIM)
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Videos
> AF AMI
> Processbook AMI
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Video : Osisoft AF from transmission to distribution
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Synchronize
© Copyright 2013 OSIsoft, LLC.
• Efficient storage of massive loads of data
• CIM based access with model versioning
• Easy to access and visualisation
Solution Results and Benefits
AMI Data Integration Into PI using the CIM
Business Challenge • Theft Detection • Distribution state estimation • Low voltage network
analysis
• Import of AMI data into PI Server
• Use PI AF as a reference model using CIM
Integrating our AMI data into PI was fast and easy. Also with PI AF, we now have an integrated CIM view of our data with versioning. With all the OSIsoft tools available for accessing and visualizing the data, I’m sure we couldn’t be more efficient in our work! Mathieu Viau, IREQ
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Conclusion and Future Work > Conclusion
• PI server is fast and reliable • PI AF is an excellent model management tool • Don’t be afraid, use the CIM
> Future work • Increase the number of smart meters • Getting PI to support SPARQL queries
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Thank You
Mathieu Viau [email protected] Special thanks to Alexandre Bouffard for his contribution.