Melding Big Data and CIM for

Bold Power Systems Insights

Dr. Siri Varadan, PE

UISOL, An Alstom Company

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Outline• Big Data

• CIM and Big Data

• Conclusions

• Use Cases

Big Data Definition

• Gartner defines Big data using the four “V” s.

− Velocity− Volume− Variety− Variability

• Big Data is a “Scalar” quantity…That is, it may be described completely by just a number

Big Data in Power Systems• AMI data

• PMU data

• Data from IEDs

• The Data could include:

− Voltages− Currents− Power flows− Status− Time series data− Temperatures− Pressures− Other (DGA etc.)

• Power system data is Scalar and more…it has location, topology, direction and context

• Data correlations for certain data sets in power systems are governed by physical laws

Big Data in Power SystemsThe Question

• Scalar data in itself provides insights…Non-

scalar data in itself provides insights…Can the

two be combined to gain additional, unique

insights using Data Analytics?

– Analytics is defined as the discovery and

communication of meaningful patterns in data

Big Data in Power SystemsThe Answer

• Is not “42”…

• “It depends”

– On what you are looking for, or the Lens

• Lens is a specific business function that helps

summarize data from a specific perspective

– On the availability of an appropriate Lens and its

granularity

Big Data in Power SystemsAn Example

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Lens

Big Data in Power SystemsKinds of Lenses

• Depends on Area of focus (G, T, D etc.)

• Driven by the business purpose

• Applicable to Real-time or Non-Real time data

– Detection of an “incipient” system separation

– Calculation of ATC/TTC

– Asset “Health” in real-time

– Failure rates of asset classes

– Theft detection

Big Data and CIMPremise

• Big Data when combined with non-scalar

attributes like topology, location within the

context of a power system model (as in CIM)

provides unique insights.

• Questions:

– What insights?

– How?

• Take AMI data and combine it with CIM data for a

feeder!

Use CasesDistribution Focus

Opportunities to Use AMI Data Description Additional Data Utilized*

Distribution Loss Analysis Identify trend of loading on

feeders, analyzing potential

breakdown of theft and line

losses (Where, How much)

and notify user.

Distribution SCADA or Pi

Historian, GIS/CIM feeder

connectivity, OMS or DMS

operational data, CIS data,

CMMS, Vendor Catalogs

Distribution Transformer

Monitoring and Health Indexing

Compute, trend and notify

user of excessive feeder and

transformer loadings over

time (Which one, How much,

How long)

Complete Feeder Reliability

Analysis

Track, trend and predict

feeder reliability using asset

health indicators for key

feeders

Use CasesDistribution Loss Analysis

• Aggregate AMI data for customers on a per transformer, then feeder basis (CIM model provides topological connectivity, CIS provides customer information) to get a near-real-time load profile at each distribution transformer

• Run a load flow using the topology model (from CIM) and aggregated loads to get line loss for the same time period

• The power flowing out at the feeder head (obtained from SCADA) over the same period should match with the sum of the aggregated loads and loss. If not, loss is detected!

Energy loss, not Power loss, is of interest!

Use CasesDistribution Loss Analysis

• Heat-Maps

showing feeders

with

“suspiciously”

high losses

• Depending on

granularity of

meter data, the

process can

become near-

real-time

Use CasesDistribution Transformer Monitoring and Health Indexing

• Define metrics for distribution transformer monitoring based on aggregated AMI data

– Hours in service

– # of overloads

– Time spent overloaded

• Create “Health” indexes for feeders based on individual metrics that comprise the feeder

• Create “Health” indexes for substations based on performance of connected feeders

AMI Data could be the basis for Transformer Load Monitoring

Use CasesDistribution Transformer Monitoring and Health Indexing

• Displays based on metrics defined earlier

• AMI data used to calculate metrics

• AMI data merged with CIM model data

Use CasesDistribution Transformer Monitoring and Health Indexing

• Data Visualization (using SpotFire®)

Use CasesComplete Feeder Reliability Analysis

• Define metrics for Feeder performance based

on AMI data

• Could essentially establish measures like CAIDI

and CAIFI on a per customer basis…These

measures will be a true reflection of what the

customer experienced!

AMI Data could be the basis for a new set of customer centric standards!

Use CasesComplete Feeder Reliability Analysis

• Heat-maps to show feeders that have poor performance

• Performance calculated on the basis of AMI data

Use CasesOther Bold Insights

• Situational Awareness– Real-time operations and control

– Outage Management

– Crew dispatch

• Asset Intelligence– Real-time health indexing

– Condition based asset de-rating

– Lifecycle management

• Customer satisfaction

• Workforce management

• Planning

Power systems offer infinite possibilities for Data Analytics!

Conclusions

• Big Data Analytics must have a business purpose. That is, the Lens must be business driven

• Depending on the business purpose, the appropriate Lens of adequate granularity may be developed

• The amount of business value is clearly dependent on the Lens…You get what you pay for!

• Big Data analytics gets more meaningful in the context of the CIM

• There is a lot of visual appeal to data when combined with GIS based maps for power systems

A picture is worth a thousand words!

ConclusionsBig Data, CIM and Other Systems – The Vision

Operational Data SourcesAsset Connectivity Data

(contextual)

MDM PI

AMI

SCADA

OMS

DMS

GIS

CIM XMLXML, UDF

Data Aggregation

(In Memory)

Advanced Visualization

(Spotfire)Event Detection

& Notification

TLM

ConclusionsBig Data, CIM and Beyond

• CIM is growing rapidly to include T&D

• CIM is growing to encompass asset data pertaining to laboratory tests (for DGA, Oil Analysis) for asset health indexing

• CIM is growing to include other aspects of power systems– Work Management

– Asset Management

– Maintenance Management

– Customer support

– Operations and Network Control

CIM will be the skeleton off which all Data will ultimately hang!

Thank you!

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