IEC 61850 communication and CIM data base management applied ...
Why Data Modeling Using the CIM is Important to Big Data Analytics … · · 2015-09-21Why Data...
Transcript of Why Data Modeling Using the CIM is Important to Big Data Analytics … · · 2015-09-21Why Data...
Why Data Modeling Using the CIM is Important to Big Data
Analytics (14PESGM2447)
Margaret Goodrich Director of Systems Engineering
SISCO, Inc. [email protected]
Topics • Legacy use of data modeling for analytics
• The legacy process for accommodating multiple application models
• Impact of the legacy approach
• Understanding the use case
• The CIM based process for analytic data modeling
• Summary
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Legacy Approach for Analytic Data Modeling
• Each group looks at its own application needs and develops a data model that is optimized for its own use: – Only data needed for its
application is considered.
– New data model elements are added as needed based on needs of individual applications.
• The “Ad-Hoc” Approach
Data Store
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Ad Hoc Approach for Line Rating Analytic Line Rating Application
Control Area
Corridor
Line Segment 1
Line Segment 2
Ambient Temp
Wind Speed
Wind Direction
Current
A Line
LineTemp
Sag
B Line
LineTemp
Sag Line Rating App
Data Store
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Ad Hoc Approach for Remedial Action Schemes
Line Rating App
Remedial Action Application
Corridor
North-South Interconnect
Line Trip RAS
Generator Trip RAS
Airport Substation
Sydney Sub
West Dam Sub
East Wind Sub
Line Status
Current
Margin
Line Rating
RAS Arming
C-RAS App
Data Store
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Ad Hoc Approach for Disturbance Analysis
C-RAS App
Disturbance Monitor App
Control Area
Airport Sub
Sydney Sub
East Wind Sub
Battery
Breakers
Transformer
Voltage Level
138KV
69KV
West Dam Sub
DFR1
Bus Monitoring
Line Rating App
Disturbance Monitor
Data Store
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Ad Hoc Approach for CBM Analytics
C-RAS App
Line Rating App
Disturbance Monitor
Condition Based Maintenance
Circuit Breakers
Sydney Sub
69KV
138KV
Breaker Q1A1
Breaker Q1A2
Breaker Q1A3
Breaker Q2B1
Breaker Q2B2
Last Operate
NumOperations
Transformers
Sydney Sub
West Wind Sub
CBM
Data Store
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Impact of Ad Hoc Approach for Application Data Models
• Each Application has its own data model.
• Impact of cross-organizational integration and data sharing ignored.
• Questions:
– How many different models exist in a utility?
– How is data kept in synch?
C-RAS App
Line Rating App
Disturbance Monitor CBM
Next App?
How
Many?
Data Store
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How Does This “Ad-Hoc” Approach Happen?
• Misunderstanding the Enterprise Integration Needs
• Limiting Integration to the Use Case for a specific application or project
• Is this really the use case that should drive choices?
Outage
Management
Outage
Analysis
Application “A” System “A”
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What breaks the “Ad-Hoc” Approach? Change
• Addressing change becomes too difficult when each application uses its own incompatible data modeling:
– Business needs demand organizational changes and new levels of data sharing and integration.
– New technology must be addressed (e.g. renewables, DER, “deregulation”, etc.
• Result: Application rewrites, reintegration, project delays, barriers to data sharing.
• The “Bigger” the data, the more the negative impact will be of not using a consistent common data model.
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CIM Helps Manage Change
• The model driven process captures change and creates incremental updates
• The individual applications can be updated and kept synchronized with each other.
Existing Model Change: new, delete, modify
Modeling Tools and Processes
Model Store
Incremental Update
Incremental or Partial
CIM-XML File or
Updates from ESB
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Model-Driven Data Using CIM
• CIM is a Model that is flexible to accommodate: – Extensions for non-standard business needs
– Eliminate the complexity of unused models
• Profiles are created based on use cases to address specific needs
• Instances created to relate existing data to the CIM Profile schema
• Model can be used to configure analytics.
• Analytics use models to access data eliminating custom tag name dependency.
User Requirements
Extensions
CIM Model
Schema for
Data Templates
Profile
Use Cases
Instance File for
Application Data
Application
Data
MODEL and
Data Store
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Use Cases Rule!
• Use Cases defines the requirements needed to define data models, what data must be exchanged between which systems, how the data is used, who uses the data, why, what applications are needed, etc.
• Without a good understanding of the use cases an analytic design architecture cannot be developed.
• You don’t need to define ALL use cases up front. – Using a common model (CIM) as the starting point enables
all analytics to use common model constructs instead of creating new models for each new use case (analytic).
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Profile = Exchange Contract of CIM information
• Selection of which classes and attributes are of interest
• Selection of relationships (e.g. associations) are of interest (e.g. to create a “containership”).
• Add extensions
• Make optional attributes/associations mandatory Why? Because unique use cases have a different needs!
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Profiling is:
• needed to create a “contract” that represents what information is to be exchanged based on the requirements defined in the use case.
• typically a subset of the entire information model.
• used to generate messages as well as file definitions for analytic data modeling.
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The process of profiling: Defining information to be exchanged based on a Use Case
Step 1: Develop model Iteration
Step 2: Decide on profile
Proposed
Standard
Extensions
Step 3: Implementation: Create adapters /configuration
Messages Files Databases
XSD RDFS
RDFS or OWL
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CIM Data Models Deliver Flexibility
• Multiple uses cases can be addressed with one profile.
• Multiple profiles can be supported for use cases that can’t share a profile
• A disciplined modeling process with CIM provides models optimized for all applications
Use Case
CIM
Use Case Use Case
Profile Development IEC 61850
Profile 1 Profile 2
Other Models
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CIM Data Models
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CIM Is The Only Choice for the Model-Driven Utility
• Internationally accepted IEC 61970 and IEC 61968 standards.
• Developing your own comprehensive utility data model to replace CIM will take many decades of effort.
• How many experts can your utility hire to design this from scratch?
• CIM is specifically designed to be adapted to fit the needs of individual utility use cases: – Extensions – Profiles/subsets – Messages – Integration Patterns
• New applications can extend independently yet share the existing models where needs overlap without breaking existing applications and integration.
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SUMMARY • Power system data models provide standardized
context for data simplifying data management:
– Eliminating data source dependencies from analytics.
– Use of common semantic model between applications enables data sharing.
– Model management practical for large complex systems compared to tag management.
• CIM is an industry standard models that exists, has a defined process for adapting to individual needs and is ready to be used.
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Thank You
Questions/Discussion
Margaret Goodrich
Director of Systems Engineering
SISCO, Inc.
www.sisconet.com
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