epri smart distribution and pq 2012-06-06

How Important is GIS Data Quality to the Modern Grid?

Robert J. Sarfi, Michael K. Tao, J. Baker Lyon

Boreas Group

John J. Simmins

EPRI

2012 PQSD Conference – San Antonio

June 6, 2012

1

2© 2012 Electric Power Research Institute, Inc. All rights reserved.

EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition

Contents

1. Introduction

2. Asset Management

3. Data Quality

4. GIS Data

5. DMS / OMS Data

6. CMMS Data

7. Conclusion



1. Introduction

“Process automation is limited by our

incomplete and inaccurate

operational data.”

“We have minimal ability to accurately

and quickly measure our

business performance.”

“We react slowly to shifting work

volumes due to manual resource

allocation processes.”

“Process standardization is

limited by vertically integrated systems.”

“We execute simple business tasks with high skill and high

cost resources.”

“We react inconsistently to information

requests.”“We have costly and inconsistent

asset management processes.”



WorkMgt.

BI / Analytics

MobileGIS

2. Smart Grid Systems

Mobile Workforce

WorkOptimization

Mobile GIS

IVR

DMS / OMS

SCADA

DistributionAutomation

AMI

Demand Response

MaterialsMgt.

Maint.Mgt.

EngineeringAnalysis

GISMapping

GraphicDesign

Asset Management

Op

erat

ion

s

Man

agem

ent

CIS CRM

Customer Management

Customer

Empowerment

ExecutiveInformation

System

Central Databases

SCADA GIS MDMS CMMS



DMS

2. How Data Enables Workflows

NetworkAnalysis

WMS

Planning &Engineering

Distribution Automation

Schedule andDispatch

Work Order Drafting& Design

AMI(MDM)

Home Automation and Demand

Response

ServiceRestoration

OMS

CMMS

Maintenance &Construction

WirelessMobile

Enablement

AMIMDMS

GIS



2. Smart Distribution - Operational Operatives

• Reliability• Resource optimization• Demand / load optimization• Power quality optimization• Customer service• Equipment lifecycle cost of

ownership• Equipment criticality• Staff utilization/skill set• Capital expenditures• Operations and maintenance

expenditures.

6



3. Common Data Quality Issues

•Corrupted (time sensitive, inaccurate)

•Redundant data (unplanned)• Inability to correlate/cross-reference

•Gaps•Lack of knowledge of available data

•Access/security



3. Causes of Data Issues

Data Maintenance• Ambiguous definition of data ownership

and access rights• Poor data quality control processes /

practices• Deferred data update and maintenance.

Data Repositories

Initial Data Quality

• Poor quality source data• Incomplete data migration and

conversion from paper maps, asset ledgers and field data collection.



4. Spatial (GIS) Data Registry

Asset• Identification• Attributes• Connectivity• Location

Customer• Service Address• Service Facilities

Environmental• Landbase• Rights and

Access• Administrative

H1 – Asset History

N1 – System DataC1 – Customer Information

L1 – Landbase



4. GIS Data Issues

• Gaps

• Redundancies with other systems

• Workflows pertaining to new construction and maintenance

• Lack of currency with system “as-built

• Inaccuracies with the field

• Inaccurate or unavailable land-base

• Customer to transformer connectivity by phase is in doubt

• GIS model itself allows for “bad” data

• Data dependencies and the “ripple effect” of bad GIS data

• What is included in the GIS, level of detail, best practices



4. Automating Phase Identification

Correlating Voltage

AMI Voltage

Data

SCADA Voltage

Data

Customer Phase ID



4. Phase Identification Example



4. Benefit: Operational Efficiencies

• Improved safety due to more accurate facilities records

• Reduction in the overall cost of maintaining the GIS system as a whole

• Efficiencies in implementing and troubleshooting AMI communications issues

• Improved OMS and DMS benefit• Improved crew efficiencies due to

improved distribution system representation

• Improved load forecasting• More accurate system planning• Reduced work order cycle times.



5. DMS / OMS Data

Facility• Identification• Type• Connectivity• Location

Customer• Service Address• Service Facilities

Environmental• Landbase• Rights and

Access• Administrative

T1 – Technical

N1 – Network SystemC1 – Customer Information

L1 – Landbase

Administrative• Operations• Procedures• Safety

A1 – Administrative



5. DMS/OMS Data

Attributes TypeNetwork facility characteristics Non Real TimeAs designed network facility connectivity Non Real TimeAs operated network facility connectivity Real TimeLine switch status Real TimeLine protection devices (circuit switchers, reclosers, etc)

Real Time

Substation circuit breaker status Real TimeNetwork status Real TimeName and service information Real Time and Non Real TimeOutage call information Real TimeAutomated call information Real TimeService point (Smart Meter) status Real TimeLandbase data Non Real TimeOperating standards and policies Non Real Time



6. CMMS Data

• Work orders• Preventive maintenance (PM)• Asset management• Inventory control• Safety

CMMS packages can produce status reports and documents

giving details or summaries of maintenance activities.

The more sophisticated the package, the more analytics are available.



7. EPRI GIS Data Quality Survey – Phase 1

Preliminary Findings

• Thirteen utilities participated in the survey.

• Outage management and engineering analysis are the most common uses of GIS data.

• Integration and dependencies vary widely.

• No correlation between integration of the GIS and data quality.

• User are generally confident in the data.• Utilities are doing a better job at

‘completeness’ than ‘accuracy’ of data.• Benefits of ‘good’ data are seen, but

repercussions of ‘bad’ data are not.



7. EPRI GIS Data Quality Survey – Phase 1

Survey Result Statistics• Thirteen utilities participated in the survey.

• 36% store all distribution data in GIS, but 66% make use of an asset management system.

• 66% have unique asset IDs, only 27% physically tag the asset in the field.

• 54% felt that data accuracy was 75-90% (64% user confidence in data).

• 63% felt that data completeness was 75-90% (72% user confidence in data).

• Only 9% of utilities have experienced a catastrophic problem due to data, but 56% have enjoyed a benefit of good data.

• While 91% have programs to improve data, only 54% have dedicated staff.

• 73% have automated quality assurance.

• 91% have not seen quality deterioration over time.



7. Conclusions

•Data is critical to T&D success•Leverage incremental success•Approach to data management will continue to evolve

•Three things critical to success:

1. Data

2. Data

3. Data



Questions?

We’d really like your help with a data quality survey:

http://www.surveymonkey.com/s/EPRI_GIS_Data_Quality_Project_1

Please complete the survey by Thursday, May 15.

http://www.surveymonkey.com/s/EPRI_GIS_Data_Quality_Project_1



Contacts

Robert J. Sarfi, Mike K. Tao, and J. Baker Lyon - Boreas Group LLC, Denver, CO 80209 (e-mail: [email protected], [email protected], [email protected] )

John J. Simmins - Electric Power Research Institute, Knoxville, TN 37932 USA (e-mail: [email protected] ).

21



Together…Shaping the Future of Electricity

epri smart distribution and pq 2012-06-06

Technology

gis data quality

conclusionepri power

bad data data dependencies

data update

ouroperational data

data quality4

field data collection

gis system