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

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

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

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

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

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