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How to Generate Greater Value from Smart Meter DataBy managing and analyzing smart meter event data utilities can improve customer experience grid reliability operational efficiency and revenue assurance
bull Cognizant 20-20 Insights
Executive Summary Utilities have made significant investments in smart meter roll-out programs and are now looking for ways to get a return on this investment In addition to ROI regulators are pushing utilities to show how these investments are helping to improve operational efficiencies and deliver enhanced levels of customer service
Industry-led efforts such as Green Button1 are utilizing smart meter read data to provide customers with visibility into their energy usage data and consumption and billing patterns as well as tools for ldquowhat-ifrdquo scenarios However the other category of data generated by smart meters mdash meter events mdash is a relatively new concept for utilities and its true value is largely untapped Some utilities in North America are just at the early adoption stage of gaining insights from event data
Event information relayed from smart meters includes real-time device status power quality information and meter status information all of which provides a very powerful source of informa-tion to improve utilitiesrsquo core business processes Based on our experience with and observations of the changing nature of utilitiesrsquo industry
operations we believe that information captured from events can be used to derive useful insights to vastly improve customer experience grid reli-ability outage management and operational efficiency The challenge lies in managing the high volumes of event data and applying logical and predictive analytics to it such as filtration association correlation factor analysis and regression as these are relatively new concepts for most utilities
This white paper discusses the numerous logical and statistical techniques that utilities can utilize to tap the potential of events information It also illustrates how these techniques can be applied to improve the outage management process (outage detection verification and restoration) and enhance operational efficiency and field crew optimization
Meter Event Data Beyond Interval ReadsSmart meters are well known for their ability to provide meter read data at smaller intervals such as every 15 30 or 60 minutes as well as bi-directional communication and remote operating capabilities In addition to these features smart meters also generate hundreds of meter events
cognizant 20-20 insights | april 2012
An event is information that originates from the metersrsquo endpoints and can have several attributes including source and proxy information severity level and event category The source is normally the device that originates the event while the proxy is the device responsible for detecting and communicating the event Severity levels include emergency information error warning and clear The event category provides informa-tion regarding the process to which the event is related There are four basic event categories
bull Meter or device status events such as ldquopower restorerdquo and ldquolast gasprdquo
bull Power quality events such as voltage sag swell and highlow voltage alarms
bull Meter or device tamper flags such as reverse energy flow
bull Meter hardware information such as low battery alarms and battery critical alerts
Potential Business Areas for Events Data Insights
Some of the potential business areas where infor-mation from meter events can be used to derive useful business insights are
bull Customer experience Events like last gasp and power restore which can identify field outages and take proactive action even before the customer calls as well as alerts and notifi-cations to customers regarding power outages
bull Outage management Events to detect outages at the right device level and create proactive tickets as well as ldquopower restorerdquo to identify nested outages after large-scale outage restoration
bull Power quality Events like ldquovoltage sagrdquo and ldquovoltage swellrdquo in correlation with other device status information to proactively identify open neutrals and flickering lights
bull Revenue assurance Events like meter inversion and reverse energy flow along with meter reads to identify power theft and abnormal usagedemand patterns
bull Smart meter network operations and monitoring Events and meter ping commands to identify damageddefective meters access relays and other devices as well as hardware events to provide information regarding device hardware such as battery information firmware version etc
Deriving Business ValueBy now many utilities are broadly aware of the possible areas where they would like to leverage information from events However the real challenge lies in how to develop the processes and systems to continuously convert data into actionable information and then further refine the models based on the results
This challenge arises because of the nature of event data both status and exception Event data is a raw data stream and is also associated with high volumes because there are hundreds of events generated for normal operations as well as for changed conditions These events also need to be validated with other relevant information as they basically manifest the conditions of the network (meter or grid) and also some aspects of customer behavior
To manage the above needs we believe that utilities need to focus on two key dimensions
bull Systems to manage large volumes of events data both real-time and batch
bull Logical and statistical techniques that will help identify the right events and correlate with various conditions both event- and business-related and finally predict the outcomes
Key logical and statistical techniques that could be used include
bull Data filtering This refers to the analysis of events and intelligent filtration of redundant data based on predefined conditions from the event data stream This technique uses Boolean logic2 Based on our experience events like last gasp and power restore are relayed multiple times from the smart meters due to reliability considerations These kinds of events have the same event occurrence intervals but different event insertion times Hence in such cases duplicate traps could be filtered from processing using timing conditions
bull Association rules Algorithms or business rules to enable the discovery of relationships between events and other variables Inputs received from other systems such as work management systems (WMS) customer infor-mation systems (CIS) and supervisory control and data acquisition (SCADA) systems may be associated with event information to determine device-level issues before rolling out to the field crews Also events received from the smart
cognizant 20-20 insights 2
3cognizant 20-20 insights
meters can be logically segregated based on the inputs received from such systems
bull Point-of-detection algorithms These algo-rithms can help develop patterns of their occurrence which can help in taking proactive actions For instance time-wise and day-wise patterns for events can be developed Further filtration criteria can be applied to remove all patterns caused by electric communication or network issues and then the remaining patterns can used to explain occurrences of certain business outcomes such as outages power quality or device tampering
bull Data clustering This is an unsupervised model that uses data similarity to group the data points Similar categories of events can be clustered together with analysis performed to extract business value from the clusters of events For example we can identify clusters among all event types and then develop rela-tionships between outcomes and clusters of events Device status meter tamper and power quality events can be a cluster to determine issues such as open neutrals or flickering lights
bull Correlation This measures the association between two variables while assuming there is no causal relationship between the two We can develop a correlation among various events and other outcomes to determine future behavior For example correlation between event type and consumption fluctuation can help with revenue assurance
bull Factor analysis This allows variables to be grouped into common sub-groups in order to reduce the number of factors to be initially analyzed For example by performing factor analysis we can identify dominating factors that contribute to events or a set of events or an outcome
bull Regression This refers to the statistical rela-tionship between two random variables to predict the outcome Commonly used for fore-casting purposes regression examines the causal relationship between two variables An example is using regression to analyze the relationship between equipment conditions in the field such as a prediction of transformer failure based on the demand from meters associated with it
Usually more than one technique might be required to solve the problem For example to develop a relationship between device status and outage a combination of correlation factor
analysis and regression will be required to obtain the correct results
Improving Outage Management through Meter EventsSmart meter events such as last gasp and power restore that provide meter offon status can be used for improving outage management Being near-real-time these events have an advantage over outage information coming from customers and field staff Event information generated by smart meters is raw data with duplicate traps and high volume due to
bull Momentary outages and restoration-related events
bull Communication and network interface issue-related events
bull Events due to planned outages outages at the lateral feeder or transformer level customer disconnects etc
Hence it is practically not possible for outage management systems3 to process raw event data in the same way as they currently process inputs from SCADA systems customers and field staff Many utilities realized this when they integrated event information from head end systems (HES) directly into their outage management systems
In order to effectively use events data an event processing and analytics engine is required This engine needs to have the capabilities of logical filtration based on uniqueness of events momentary and existing outages and capabilities of association based on physical network hierar-chies It also needs to have pattern analysis or regression capabilities to predict the outages
A multistage event processing and analytics framework identifies confirmed cases of outages that can be passed to the outage management system for restoration (see Figure 1)
bull Stage 1 A set of conditions is used to filter duplicates from last-gasp events to identify unique cases of outage events Such events are then correlated with power-restore events to remove the cases of momentary outages (outages with a duration of less than 60 seconds)
Further inputs from other systems such as CIS and WMS are considered to segregate outage events that have occurred due to existing planned maintenance meter exchange or customer disconnect The remaining outage events are considered as realized events
4cognizant 20-20 insights
bull Stage 2 In this stage the meter-level realized events from Stage 1 are escalated to a higher level of device hierarchies (lateral feeder trans-former etc) and compared with other device inputs using association rules and conditions to identify an outage incident These cases of outage are considered to be probable cases that need to be tested further
bull Stage 3 During this stage the probable cases of outages from Stage 2 are verified using remote meter ping functionality and only confirmed outage incidents results are com-municated to the outage management system for further action
The event processing and analytics engine needs to be integrated into the utilities system landscape comprising the head end system CIS
meter data management (MDM) WMS distribu-tion automation and SCADA (see Figure 2) This will enable effective outage management and crew optimization by focusing on ldquorealrdquo outage events from smart meters
The benefits of this approach include
bull Early and accurate outage detection leading to improvement in power system reliability indices such as CAIDI SAIDI etc
bull Early detection of momentary pnd planned outages to help avoid costly field visits
bull Outage and restoration verification to avoid costly field crew movement
bull Improved intelligence due to inputs from appli-cations such as CIS WMS and SCADA
Event Processing and Analytics Framework
Figure 1
Stage 1 Stage 2 Stage 3
Event Filtration
Event Realization
Outage Escalation
Outage Comparison
Outage Verification
Outage Confirmation
Event Processing Probable Outage Confirmed Outage
Smart Meter Event Processing Business Context Diagram
Figure 2
Field Work Execution
Outage Management
System
SCADA
Distribution Area Applications
Events Data
Real-Time Status Check
Smart Equipment Data
Field Force Automation
High-Quality Events Data
Real-Time Status Check
Planned Outage Data
Work Management
System
Head End System
Smart Meter
CustomerPremise Data
Customer Information SystemMeter Data
Management System
Smart Meter Event Processing Solution
Feeder Telemetry Data
5cognizant 20-20 insights
Cognizant Smart Meter Event Processing (SMEP) SolutionOur Utilities Practice has designed a smart meter event processing (SMEP) solution for improving the outage management process The SMEP solution is configurable to meet dynamic business requirements and is based on multistage processing and analytics
Our SMEP solution is designed to provide the functionality required to process huge volumes of real-time outage meter events data The following are the key features of the SMEP solution
bull Near-real-time processing of a high volume of meter event data
bull Business rules-based engine to configure the algorithms and rules to process the events
bull Dynamic and flexible control based on require-ments from other utility systems
bull Business process management to effectively route and manage eventsincidents
bull Integration with other utility applications for validation association and correlation
bull Visualization and dashboarding tools
In addition to the above features SMEP has been designed using the event-driven architecture (EDA) EDA helps orchestrate the generation detection and consumption of meter events as well as the responses evoked by them It helps effectively manage events and communica-tion with various application processes using messaging (see Figure 3)
Conclusion From Data to InsightsThe concept of leveraging meter events data to gain business insights is at an early stage To effectively convert raw data into meaningful insights utilities need to build state-of-the-art methods in logical and predictive reasoning with data management capabilities The theory of inte-grating and exploiting logical and statistical data relationships is quite new most utilities are still at an early stage of the maturity curve primarily reporting on and dashboarding the smart meter analytics they gather
Analytics need a combination of sound business and statistical capabilities which many utilities lack Statistical capabilities include knowledge of statistical methods statistical tools such as SAS and an ability to provide statistical inferences
Smart Meter Event Processing Solution
Figure 3
Event Preprocessing Probable Outage
Stage 1 Stage 2
Database
MeterEvents
Smart Meter Event Processing Solution
Stage 3
Hea
d E
nd
Sys
tem
En
terp
rise
Ser
vice
Bu
s
Confirmed OutageOutage
VerificationOutage
EscalationOutage
ComparisonEvent
RefinementEvent
Filtration
Outage management systemother applications
Visualization and Dashboarding Event Log Entry
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
An event is information that originates from the metersrsquo endpoints and can have several attributes including source and proxy information severity level and event category The source is normally the device that originates the event while the proxy is the device responsible for detecting and communicating the event Severity levels include emergency information error warning and clear The event category provides informa-tion regarding the process to which the event is related There are four basic event categories
bull Meter or device status events such as ldquopower restorerdquo and ldquolast gasprdquo
bull Power quality events such as voltage sag swell and highlow voltage alarms
bull Meter or device tamper flags such as reverse energy flow
bull Meter hardware information such as low battery alarms and battery critical alerts
Potential Business Areas for Events Data Insights
Some of the potential business areas where infor-mation from meter events can be used to derive useful business insights are
bull Customer experience Events like last gasp and power restore which can identify field outages and take proactive action even before the customer calls as well as alerts and notifi-cations to customers regarding power outages
bull Outage management Events to detect outages at the right device level and create proactive tickets as well as ldquopower restorerdquo to identify nested outages after large-scale outage restoration
bull Power quality Events like ldquovoltage sagrdquo and ldquovoltage swellrdquo in correlation with other device status information to proactively identify open neutrals and flickering lights
bull Revenue assurance Events like meter inversion and reverse energy flow along with meter reads to identify power theft and abnormal usagedemand patterns
bull Smart meter network operations and monitoring Events and meter ping commands to identify damageddefective meters access relays and other devices as well as hardware events to provide information regarding device hardware such as battery information firmware version etc
Deriving Business ValueBy now many utilities are broadly aware of the possible areas where they would like to leverage information from events However the real challenge lies in how to develop the processes and systems to continuously convert data into actionable information and then further refine the models based on the results
This challenge arises because of the nature of event data both status and exception Event data is a raw data stream and is also associated with high volumes because there are hundreds of events generated for normal operations as well as for changed conditions These events also need to be validated with other relevant information as they basically manifest the conditions of the network (meter or grid) and also some aspects of customer behavior
To manage the above needs we believe that utilities need to focus on two key dimensions
bull Systems to manage large volumes of events data both real-time and batch
bull Logical and statistical techniques that will help identify the right events and correlate with various conditions both event- and business-related and finally predict the outcomes
Key logical and statistical techniques that could be used include
bull Data filtering This refers to the analysis of events and intelligent filtration of redundant data based on predefined conditions from the event data stream This technique uses Boolean logic2 Based on our experience events like last gasp and power restore are relayed multiple times from the smart meters due to reliability considerations These kinds of events have the same event occurrence intervals but different event insertion times Hence in such cases duplicate traps could be filtered from processing using timing conditions
bull Association rules Algorithms or business rules to enable the discovery of relationships between events and other variables Inputs received from other systems such as work management systems (WMS) customer infor-mation systems (CIS) and supervisory control and data acquisition (SCADA) systems may be associated with event information to determine device-level issues before rolling out to the field crews Also events received from the smart
cognizant 20-20 insights 2
3cognizant 20-20 insights
meters can be logically segregated based on the inputs received from such systems
bull Point-of-detection algorithms These algo-rithms can help develop patterns of their occurrence which can help in taking proactive actions For instance time-wise and day-wise patterns for events can be developed Further filtration criteria can be applied to remove all patterns caused by electric communication or network issues and then the remaining patterns can used to explain occurrences of certain business outcomes such as outages power quality or device tampering
bull Data clustering This is an unsupervised model that uses data similarity to group the data points Similar categories of events can be clustered together with analysis performed to extract business value from the clusters of events For example we can identify clusters among all event types and then develop rela-tionships between outcomes and clusters of events Device status meter tamper and power quality events can be a cluster to determine issues such as open neutrals or flickering lights
bull Correlation This measures the association between two variables while assuming there is no causal relationship between the two We can develop a correlation among various events and other outcomes to determine future behavior For example correlation between event type and consumption fluctuation can help with revenue assurance
bull Factor analysis This allows variables to be grouped into common sub-groups in order to reduce the number of factors to be initially analyzed For example by performing factor analysis we can identify dominating factors that contribute to events or a set of events or an outcome
bull Regression This refers to the statistical rela-tionship between two random variables to predict the outcome Commonly used for fore-casting purposes regression examines the causal relationship between two variables An example is using regression to analyze the relationship between equipment conditions in the field such as a prediction of transformer failure based on the demand from meters associated with it
Usually more than one technique might be required to solve the problem For example to develop a relationship between device status and outage a combination of correlation factor
analysis and regression will be required to obtain the correct results
Improving Outage Management through Meter EventsSmart meter events such as last gasp and power restore that provide meter offon status can be used for improving outage management Being near-real-time these events have an advantage over outage information coming from customers and field staff Event information generated by smart meters is raw data with duplicate traps and high volume due to
bull Momentary outages and restoration-related events
bull Communication and network interface issue-related events
bull Events due to planned outages outages at the lateral feeder or transformer level customer disconnects etc
Hence it is practically not possible for outage management systems3 to process raw event data in the same way as they currently process inputs from SCADA systems customers and field staff Many utilities realized this when they integrated event information from head end systems (HES) directly into their outage management systems
In order to effectively use events data an event processing and analytics engine is required This engine needs to have the capabilities of logical filtration based on uniqueness of events momentary and existing outages and capabilities of association based on physical network hierar-chies It also needs to have pattern analysis or regression capabilities to predict the outages
A multistage event processing and analytics framework identifies confirmed cases of outages that can be passed to the outage management system for restoration (see Figure 1)
bull Stage 1 A set of conditions is used to filter duplicates from last-gasp events to identify unique cases of outage events Such events are then correlated with power-restore events to remove the cases of momentary outages (outages with a duration of less than 60 seconds)
Further inputs from other systems such as CIS and WMS are considered to segregate outage events that have occurred due to existing planned maintenance meter exchange or customer disconnect The remaining outage events are considered as realized events
4cognizant 20-20 insights
bull Stage 2 In this stage the meter-level realized events from Stage 1 are escalated to a higher level of device hierarchies (lateral feeder trans-former etc) and compared with other device inputs using association rules and conditions to identify an outage incident These cases of outage are considered to be probable cases that need to be tested further
bull Stage 3 During this stage the probable cases of outages from Stage 2 are verified using remote meter ping functionality and only confirmed outage incidents results are com-municated to the outage management system for further action
The event processing and analytics engine needs to be integrated into the utilities system landscape comprising the head end system CIS
meter data management (MDM) WMS distribu-tion automation and SCADA (see Figure 2) This will enable effective outage management and crew optimization by focusing on ldquorealrdquo outage events from smart meters
The benefits of this approach include
bull Early and accurate outage detection leading to improvement in power system reliability indices such as CAIDI SAIDI etc
bull Early detection of momentary pnd planned outages to help avoid costly field visits
bull Outage and restoration verification to avoid costly field crew movement
bull Improved intelligence due to inputs from appli-cations such as CIS WMS and SCADA
Event Processing and Analytics Framework
Figure 1
Stage 1 Stage 2 Stage 3
Event Filtration
Event Realization
Outage Escalation
Outage Comparison
Outage Verification
Outage Confirmation
Event Processing Probable Outage Confirmed Outage
Smart Meter Event Processing Business Context Diagram
Figure 2
Field Work Execution
Outage Management
System
SCADA
Distribution Area Applications
Events Data
Real-Time Status Check
Smart Equipment Data
Field Force Automation
High-Quality Events Data
Real-Time Status Check
Planned Outage Data
Work Management
System
Head End System
Smart Meter
CustomerPremise Data
Customer Information SystemMeter Data
Management System
Smart Meter Event Processing Solution
Feeder Telemetry Data
5cognizant 20-20 insights
Cognizant Smart Meter Event Processing (SMEP) SolutionOur Utilities Practice has designed a smart meter event processing (SMEP) solution for improving the outage management process The SMEP solution is configurable to meet dynamic business requirements and is based on multistage processing and analytics
Our SMEP solution is designed to provide the functionality required to process huge volumes of real-time outage meter events data The following are the key features of the SMEP solution
bull Near-real-time processing of a high volume of meter event data
bull Business rules-based engine to configure the algorithms and rules to process the events
bull Dynamic and flexible control based on require-ments from other utility systems
bull Business process management to effectively route and manage eventsincidents
bull Integration with other utility applications for validation association and correlation
bull Visualization and dashboarding tools
In addition to the above features SMEP has been designed using the event-driven architecture (EDA) EDA helps orchestrate the generation detection and consumption of meter events as well as the responses evoked by them It helps effectively manage events and communica-tion with various application processes using messaging (see Figure 3)
Conclusion From Data to InsightsThe concept of leveraging meter events data to gain business insights is at an early stage To effectively convert raw data into meaningful insights utilities need to build state-of-the-art methods in logical and predictive reasoning with data management capabilities The theory of inte-grating and exploiting logical and statistical data relationships is quite new most utilities are still at an early stage of the maturity curve primarily reporting on and dashboarding the smart meter analytics they gather
Analytics need a combination of sound business and statistical capabilities which many utilities lack Statistical capabilities include knowledge of statistical methods statistical tools such as SAS and an ability to provide statistical inferences
Smart Meter Event Processing Solution
Figure 3
Event Preprocessing Probable Outage
Stage 1 Stage 2
Database
MeterEvents
Smart Meter Event Processing Solution
Stage 3
Hea
d E
nd
Sys
tem
En
terp
rise
Ser
vice
Bu
s
Confirmed OutageOutage
VerificationOutage
EscalationOutage
ComparisonEvent
RefinementEvent
Filtration
Outage management systemother applications
Visualization and Dashboarding Event Log Entry
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
3cognizant 20-20 insights
meters can be logically segregated based on the inputs received from such systems
bull Point-of-detection algorithms These algo-rithms can help develop patterns of their occurrence which can help in taking proactive actions For instance time-wise and day-wise patterns for events can be developed Further filtration criteria can be applied to remove all patterns caused by electric communication or network issues and then the remaining patterns can used to explain occurrences of certain business outcomes such as outages power quality or device tampering
bull Data clustering This is an unsupervised model that uses data similarity to group the data points Similar categories of events can be clustered together with analysis performed to extract business value from the clusters of events For example we can identify clusters among all event types and then develop rela-tionships between outcomes and clusters of events Device status meter tamper and power quality events can be a cluster to determine issues such as open neutrals or flickering lights
bull Correlation This measures the association between two variables while assuming there is no causal relationship between the two We can develop a correlation among various events and other outcomes to determine future behavior For example correlation between event type and consumption fluctuation can help with revenue assurance
bull Factor analysis This allows variables to be grouped into common sub-groups in order to reduce the number of factors to be initially analyzed For example by performing factor analysis we can identify dominating factors that contribute to events or a set of events or an outcome
bull Regression This refers to the statistical rela-tionship between two random variables to predict the outcome Commonly used for fore-casting purposes regression examines the causal relationship between two variables An example is using regression to analyze the relationship between equipment conditions in the field such as a prediction of transformer failure based on the demand from meters associated with it
Usually more than one technique might be required to solve the problem For example to develop a relationship between device status and outage a combination of correlation factor
analysis and regression will be required to obtain the correct results
Improving Outage Management through Meter EventsSmart meter events such as last gasp and power restore that provide meter offon status can be used for improving outage management Being near-real-time these events have an advantage over outage information coming from customers and field staff Event information generated by smart meters is raw data with duplicate traps and high volume due to
bull Momentary outages and restoration-related events
bull Communication and network interface issue-related events
bull Events due to planned outages outages at the lateral feeder or transformer level customer disconnects etc
Hence it is practically not possible for outage management systems3 to process raw event data in the same way as they currently process inputs from SCADA systems customers and field staff Many utilities realized this when they integrated event information from head end systems (HES) directly into their outage management systems
In order to effectively use events data an event processing and analytics engine is required This engine needs to have the capabilities of logical filtration based on uniqueness of events momentary and existing outages and capabilities of association based on physical network hierar-chies It also needs to have pattern analysis or regression capabilities to predict the outages
A multistage event processing and analytics framework identifies confirmed cases of outages that can be passed to the outage management system for restoration (see Figure 1)
bull Stage 1 A set of conditions is used to filter duplicates from last-gasp events to identify unique cases of outage events Such events are then correlated with power-restore events to remove the cases of momentary outages (outages with a duration of less than 60 seconds)
Further inputs from other systems such as CIS and WMS are considered to segregate outage events that have occurred due to existing planned maintenance meter exchange or customer disconnect The remaining outage events are considered as realized events
4cognizant 20-20 insights
bull Stage 2 In this stage the meter-level realized events from Stage 1 are escalated to a higher level of device hierarchies (lateral feeder trans-former etc) and compared with other device inputs using association rules and conditions to identify an outage incident These cases of outage are considered to be probable cases that need to be tested further
bull Stage 3 During this stage the probable cases of outages from Stage 2 are verified using remote meter ping functionality and only confirmed outage incidents results are com-municated to the outage management system for further action
The event processing and analytics engine needs to be integrated into the utilities system landscape comprising the head end system CIS
meter data management (MDM) WMS distribu-tion automation and SCADA (see Figure 2) This will enable effective outage management and crew optimization by focusing on ldquorealrdquo outage events from smart meters
The benefits of this approach include
bull Early and accurate outage detection leading to improvement in power system reliability indices such as CAIDI SAIDI etc
bull Early detection of momentary pnd planned outages to help avoid costly field visits
bull Outage and restoration verification to avoid costly field crew movement
bull Improved intelligence due to inputs from appli-cations such as CIS WMS and SCADA
Event Processing and Analytics Framework
Figure 1
Stage 1 Stage 2 Stage 3
Event Filtration
Event Realization
Outage Escalation
Outage Comparison
Outage Verification
Outage Confirmation
Event Processing Probable Outage Confirmed Outage
Smart Meter Event Processing Business Context Diagram
Figure 2
Field Work Execution
Outage Management
System
SCADA
Distribution Area Applications
Events Data
Real-Time Status Check
Smart Equipment Data
Field Force Automation
High-Quality Events Data
Real-Time Status Check
Planned Outage Data
Work Management
System
Head End System
Smart Meter
CustomerPremise Data
Customer Information SystemMeter Data
Management System
Smart Meter Event Processing Solution
Feeder Telemetry Data
5cognizant 20-20 insights
Cognizant Smart Meter Event Processing (SMEP) SolutionOur Utilities Practice has designed a smart meter event processing (SMEP) solution for improving the outage management process The SMEP solution is configurable to meet dynamic business requirements and is based on multistage processing and analytics
Our SMEP solution is designed to provide the functionality required to process huge volumes of real-time outage meter events data The following are the key features of the SMEP solution
bull Near-real-time processing of a high volume of meter event data
bull Business rules-based engine to configure the algorithms and rules to process the events
bull Dynamic and flexible control based on require-ments from other utility systems
bull Business process management to effectively route and manage eventsincidents
bull Integration with other utility applications for validation association and correlation
bull Visualization and dashboarding tools
In addition to the above features SMEP has been designed using the event-driven architecture (EDA) EDA helps orchestrate the generation detection and consumption of meter events as well as the responses evoked by them It helps effectively manage events and communica-tion with various application processes using messaging (see Figure 3)
Conclusion From Data to InsightsThe concept of leveraging meter events data to gain business insights is at an early stage To effectively convert raw data into meaningful insights utilities need to build state-of-the-art methods in logical and predictive reasoning with data management capabilities The theory of inte-grating and exploiting logical and statistical data relationships is quite new most utilities are still at an early stage of the maturity curve primarily reporting on and dashboarding the smart meter analytics they gather
Analytics need a combination of sound business and statistical capabilities which many utilities lack Statistical capabilities include knowledge of statistical methods statistical tools such as SAS and an ability to provide statistical inferences
Smart Meter Event Processing Solution
Figure 3
Event Preprocessing Probable Outage
Stage 1 Stage 2
Database
MeterEvents
Smart Meter Event Processing Solution
Stage 3
Hea
d E
nd
Sys
tem
En
terp
rise
Ser
vice
Bu
s
Confirmed OutageOutage
VerificationOutage
EscalationOutage
ComparisonEvent
RefinementEvent
Filtration
Outage management systemother applications
Visualization and Dashboarding Event Log Entry
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
4cognizant 20-20 insights
bull Stage 2 In this stage the meter-level realized events from Stage 1 are escalated to a higher level of device hierarchies (lateral feeder trans-former etc) and compared with other device inputs using association rules and conditions to identify an outage incident These cases of outage are considered to be probable cases that need to be tested further
bull Stage 3 During this stage the probable cases of outages from Stage 2 are verified using remote meter ping functionality and only confirmed outage incidents results are com-municated to the outage management system for further action
The event processing and analytics engine needs to be integrated into the utilities system landscape comprising the head end system CIS
meter data management (MDM) WMS distribu-tion automation and SCADA (see Figure 2) This will enable effective outage management and crew optimization by focusing on ldquorealrdquo outage events from smart meters
The benefits of this approach include
bull Early and accurate outage detection leading to improvement in power system reliability indices such as CAIDI SAIDI etc
bull Early detection of momentary pnd planned outages to help avoid costly field visits
bull Outage and restoration verification to avoid costly field crew movement
bull Improved intelligence due to inputs from appli-cations such as CIS WMS and SCADA
Event Processing and Analytics Framework
Figure 1
Stage 1 Stage 2 Stage 3
Event Filtration
Event Realization
Outage Escalation
Outage Comparison
Outage Verification
Outage Confirmation
Event Processing Probable Outage Confirmed Outage
Smart Meter Event Processing Business Context Diagram
Figure 2
Field Work Execution
Outage Management
System
SCADA
Distribution Area Applications
Events Data
Real-Time Status Check
Smart Equipment Data
Field Force Automation
High-Quality Events Data
Real-Time Status Check
Planned Outage Data
Work Management
System
Head End System
Smart Meter
CustomerPremise Data
Customer Information SystemMeter Data
Management System
Smart Meter Event Processing Solution
Feeder Telemetry Data
5cognizant 20-20 insights
Cognizant Smart Meter Event Processing (SMEP) SolutionOur Utilities Practice has designed a smart meter event processing (SMEP) solution for improving the outage management process The SMEP solution is configurable to meet dynamic business requirements and is based on multistage processing and analytics
Our SMEP solution is designed to provide the functionality required to process huge volumes of real-time outage meter events data The following are the key features of the SMEP solution
bull Near-real-time processing of a high volume of meter event data
bull Business rules-based engine to configure the algorithms and rules to process the events
bull Dynamic and flexible control based on require-ments from other utility systems
bull Business process management to effectively route and manage eventsincidents
bull Integration with other utility applications for validation association and correlation
bull Visualization and dashboarding tools
In addition to the above features SMEP has been designed using the event-driven architecture (EDA) EDA helps orchestrate the generation detection and consumption of meter events as well as the responses evoked by them It helps effectively manage events and communica-tion with various application processes using messaging (see Figure 3)
Conclusion From Data to InsightsThe concept of leveraging meter events data to gain business insights is at an early stage To effectively convert raw data into meaningful insights utilities need to build state-of-the-art methods in logical and predictive reasoning with data management capabilities The theory of inte-grating and exploiting logical and statistical data relationships is quite new most utilities are still at an early stage of the maturity curve primarily reporting on and dashboarding the smart meter analytics they gather
Analytics need a combination of sound business and statistical capabilities which many utilities lack Statistical capabilities include knowledge of statistical methods statistical tools such as SAS and an ability to provide statistical inferences
Smart Meter Event Processing Solution
Figure 3
Event Preprocessing Probable Outage
Stage 1 Stage 2
Database
MeterEvents
Smart Meter Event Processing Solution
Stage 3
Hea
d E
nd
Sys
tem
En
terp
rise
Ser
vice
Bu
s
Confirmed OutageOutage
VerificationOutage
EscalationOutage
ComparisonEvent
RefinementEvent
Filtration
Outage management systemother applications
Visualization and Dashboarding Event Log Entry
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
5cognizant 20-20 insights
Cognizant Smart Meter Event Processing (SMEP) SolutionOur Utilities Practice has designed a smart meter event processing (SMEP) solution for improving the outage management process The SMEP solution is configurable to meet dynamic business requirements and is based on multistage processing and analytics
Our SMEP solution is designed to provide the functionality required to process huge volumes of real-time outage meter events data The following are the key features of the SMEP solution
bull Near-real-time processing of a high volume of meter event data
bull Business rules-based engine to configure the algorithms and rules to process the events
bull Dynamic and flexible control based on require-ments from other utility systems
bull Business process management to effectively route and manage eventsincidents
bull Integration with other utility applications for validation association and correlation
bull Visualization and dashboarding tools
In addition to the above features SMEP has been designed using the event-driven architecture (EDA) EDA helps orchestrate the generation detection and consumption of meter events as well as the responses evoked by them It helps effectively manage events and communica-tion with various application processes using messaging (see Figure 3)
Conclusion From Data to InsightsThe concept of leveraging meter events data to gain business insights is at an early stage To effectively convert raw data into meaningful insights utilities need to build state-of-the-art methods in logical and predictive reasoning with data management capabilities The theory of inte-grating and exploiting logical and statistical data relationships is quite new most utilities are still at an early stage of the maturity curve primarily reporting on and dashboarding the smart meter analytics they gather
Analytics need a combination of sound business and statistical capabilities which many utilities lack Statistical capabilities include knowledge of statistical methods statistical tools such as SAS and an ability to provide statistical inferences
Smart Meter Event Processing Solution
Figure 3
Event Preprocessing Probable Outage
Stage 1 Stage 2
Database
MeterEvents
Smart Meter Event Processing Solution
Stage 3
Hea
d E
nd
Sys
tem
En
terp
rise
Ser
vice
Bu
s
Confirmed OutageOutage
VerificationOutage
EscalationOutage
ComparisonEvent
RefinementEvent
Filtration
Outage management systemother applications
Visualization and Dashboarding Event Log Entry
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
6cognizant 20-20 insights
ReferencesldquoElectric Power Industry Overview 2007rdquo US Energy Information Administration httpwwweiagovcneafelectricitypageprim2toc2html
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoLocation of Outage in Distribution System Based on Statistical Hypotheses Testingrdquo IEEE Transactions on Power Delivery Vol 11 No 1 January 1996 p 546
Deepal Rodrigo Anil Pahwa and John E Boyer ldquoSmart Grid Regional Demonstration Project Project Narrativerdquo DOE-FOA-0000036 August 2009
ldquoDeploy Smart Grid in Difficult and Varying Terrainrdquo Silverspring Networks httpwwwsilverspringnetcomservicesmesh-designhtml
Doug Micheel ldquoSmart Grid Implementation The PHI Storyrdquo Pepco Holdings Inc Presentation to the 2011 GreenGov Symposium Nov 2 2011
ldquo1-210 Single phase Meterrdquo GE Energy httpwwwgeindustrialcompublibrarycheckoutGEA13391TN R=Brochures|GEA13391|PDF
ldquo1-210+c SmartMeterrdquo SmartSynch httpsmartsynchcompdfi-210+c_smartmeter_epdf
Krishna Sridharan and Noel N Schulz ldquoOutage Management Through AMR Systems Using An Intelli gent Data Filterrdquo IEEE Transactions on Power Delivery Vol 16 No 4 October 2001 pp 669-675
Lise Getoor and Renee J Miller ldquoCollective Information Integration Using Logical and Statistical Methodsrdquo University of Pennsylvania
Peter Yeung and Michael Jung ldquoImproving Electric Reliability with Smart Metersrdquo Silverspring Networks 2012 httpwwwsilverspringnetcompdfswhitepapersSilverSpring-Whitepaper-Improving-Electric-Reliability-SmartMeterspdf
Yan Liu ldquoDistribution System Outage Information Processing Using Comprehensive Data and Intelligent Techniquesrdquo PhD dissertation Michigan Technological University 2001
Hence utilities need to have a two-pronged approach In the short to medium term utilities can build solutions largely on logical techniques where they have sufficient develop-ment experience and can leverage vendors and partners that provide statistical capabilities
For the longer term utilities need to take a holistic approach toward analytics keeping in mind the
needs of the enterprise and leveraging various sources of information (not limited to meter read or event data) based on the assessment of the current state of process and people skills They should consider various approaches including building analytics skills through a Center of Excellence for Analytics or developing collabora-tive models with vendors specializing in analytics
Footnotes1 Green Button is an industry-led effort in response to a White House call-to-action
httpwwwgreenbuttondataorggreenabouthtml
2 Boolean logic consists of three logical operators ldquoORrdquo ldquoANDrdquo and ldquoNOTrdquo httpbooleanlogicnet
3 Outage management systems develop alternate supply plans and create job orders for restoration
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom
About Cognizant
Cognizant (NASDAQ CTSH) is a leading provider of information technology consulting and business process out-sourcing services dedicated to helping the worldrsquos leading companies build stronger businesses Headquartered in Teaneck New Jersey (US) Cognizant combines a passion for client satisfaction technology innovation deep industry and business process expertise and a global collaborative workforce that embodies the future of work With over 50 delivery centers worldwide and approximately 137700 employees as of December 31 2011 Cognizant is a member of the NASDAQ-100 the SampP 500 the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world Visit us online at wwwcognizantcom or follow us on Twitter Cognizant
World Headquarters
500 Frank W Burr BlvdTeaneck NJ 07666 USAPhone +1 201 801 0233Fax +1 201 801 0243Toll Free +1 888 937 3277Email inquirycognizantcom
European Headquarters
1 Kingdom StreetPaddington CentralLondon W2 6BDPhone +44 (0) 20 7297 7600Fax +44 (0) 20 7121 0102Email infoukcognizantcom
India Operations Headquarters
5535 Old Mahabalipuram RoadOkkiyam Pettai ThoraipakkamChennai 600 096 IndiaPhone +91 (0) 44 4209 6000Fax +91 (0) 44 4209 6060Email inquiryindiacognizantcom
copy Copyright 2012 Cognizant All rights reserved No part of this document may be reproduced stored in a retrieval system transmitted in any form or by any means electronic mechanical photocopying recording or otherwise without the express written permission from Cognizant The information contained herein is subject to change without notice All other trademarks mentioned herein are the property of their respective owners
About the AuthorsDr Sanjay Gupta is Cognizantrsquos Director of Consulting within the Energy and Utilities Practice of Cognizant Business Consulting He has more than 20 years of global energy and utilities industry experience in consulting business development and business operations and has led and executed consulting engage-ments with several large global customers Sanjay is also responsible for developing industry solutions and services with a focus on smart gridsmart metering asset optimization analytics renewable energy and operations management Sanjay holds a doctorate degree in energy and power and a masterrsquos in engineering He can be reached at SanjayGuptacognizantcom
Ashish Mohan Tiwari is a Consultant within the Energy and Utilities Practice of Cognizant Business Consulting with six-plus years of experience providing consulting services in the implementation of IT systems for the utilities industry He has extensive experience in smart metering infrastructure smart grid data analytics solutions and enterprise asset management Ashish has worked on numerous transformation engagements in the areas of process consulting package evaluation and solution design for global utilities companies in regulated and de-regulated markets He can be reached at AshishMohanTiwaricognizantcom