Why Data Modeling Using the CIM is Important to Big Data

Analytics (14PESGM2447)

Margaret Goodrich Director of Systems Engineering

SISCO, Inc. margaret@sisconet.com

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.

margarat@sisconet.com

www.sisconet.com

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