Melding Big Data and CIM for Bold Power Systems … Big Data and CIM for Bold Power Systems Insights...
Transcript of Melding Big Data and CIM for Bold Power Systems … Big Data and CIM for Bold Power Systems Insights...
Melding Big Data and CIM for
Bold Power Systems Insights
Dr. Siri Varadan, PE
UISOL, An Alstom Company
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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 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!