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Data Analytics at American Electric Power

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Presentation on theme: "Data Analytics at American Electric Power"— Presentation transcript:

1 Data Analytics at American Electric Power
Presentation to: SWEDE May 8, 2014 Tom Weaver, PE

2 Business Analytics is the convergence of three key areas
Opportunities Working with OpCos, define business opportunities or problems we are trying to solve in 3 areas Distribution Meter Consumer Technical Solutions Define the technical solutions that meet business needs for Data capture Data storage Complex processing Visualization AEP Business Solution Commercial Solutions Define the commercial relationships that are required to make this journey successful Build vs. Buy Collaboration with others Collaboration is vital as considerations are inter-connected

3 Degree of Intelligence (Competitive Advantage) Analytic Capabilities
What does Business Analytics Mean? Standard Reports Ad Hoc Reports Query Drilldown Alerts Statistical Analysis Forecasting Predictive Modeling Optimization Degree of Intelligence (Competitive Advantage) Analytic Capabilities Analytic Capability Answers the Questions Standard Reports What happened? When did it happen? Ad Hoc Reports How many? How often? Where? Query Drilldown Or OLAP Where exactly is the problem? How to I find the answers? Alerts/Monitoring When should I react? What actions are needed now? Statistical Analysis Why is this happening? What opportunities am I missing? Forecasting What if the trends continue? How much is needed? When will it be needed? Predictive Modeling What will happen next? How will it affect my business? Optimization How do we do things better? What is the best decision for a complex problem?

4 Metering Analytics Needs
Analytics framework today Started simple pending maturity of vendor solutions Conceptual View OPERATIONAL Metering Analytics Needs Analytic Capability Availability of Data for Load Research and Development of Detection Reports (Hot Sockets, Etc) Standard Service Order Processing Process/System Monitoring GUI for the integration of meter events and orders AMI ( UIQ & LGCC ) SAS MDM MACSS ( MCS & OPS ) TERS Operational SWAMI Data Store DA System ( PI ) A A T D C E R AMIGO PowerOn U O S CES Data PEV Data PeopleSoft GIS

5 Why is Data Analytics a Strategic Initiative for the Industry?
Sense Communicate Compute Control Power Plants Transmission Substations Distribution Consumers Sensor and Communication Technology Leapfrogging Ability to Mine Data for High Value Applications for Electric Utilities 5

6 Distribution Modernization Demonstration on “Big Data” Data Management & Analytics to Support Operations, Planning and Asset Management Mission: Benchmark “State of the Industry” Demonstrate applications Collaborate with industry leaders Vision: Develop “best practices” Accelerate understanding Document cost benefit Potential Breakthroughs: Better visualizations, insights Emerging analytics capabilities Application of data Take advantage of new opportunities afforded by a sensor enabled grid

7 Data Integration and Analytics Applied to a Storm Event and Recovery
Day –(3) Storm Forecast Day (0) Storm Event Day (+3) Storm Recovery Leverage the New EPRI High Performance Computing System Define the right system for the application Evaluate fast pattern recognition for storm damage data Data Sets: Weather Forecasts Historical Damage Predictive Analytics Storm Protection Settings Management Systems Situational Analytics Assets and Inventory Field Crew Interfaces 1 0 1 Field Crew Support Customers Interfaces AMI, SCADA, GIS High Performance Computing Requirements Damage Assessments N+1 Data Sources

8 AMI Meter Temperature Monitoring
Monitoring 502,310 meters. 85% accurate, 520 Issues out of 612 Field Orders. Next Steps for on-going Improvements: Automate monitoring. Change cutoff per season for more accuracy. Optimize parameters?


10 Site Genie/Quality of Service Report
Use SAS to decode then analyze the vectors. Broken CT and PT on transformer rated meters, poor connections under billing of commercial customers. New customer validation of service, saved Ohio 208 site visits this year. Issues Population Ohio 38 5,776 0.66% PSO 19 1,718 1.11% I&M 473

11 Voltage Magnitude Analysis – Transformer Rated Meters
Next Step: Create automated programs to analyze. Description of Issue Number Corrected Service Type in Meter 8 Bad Cable 2 Service Incorrect in MACSS Bad PT Blown Transformer Fuse 1 Theft No Issues Total Feedback 17

12 FUTURE: Energy Diversion Detection – Monitor Load Profile
Analyze the Voltage and kWh of Load Profile Flag premises with high voltage drop but low kWh compared to neighbors. Program flagging premises documented on the wrong transformer.

13 FIRST: Clean Up AEP’s MACSS Data – Correlate Premises to Proper Transformer
2179 South

14 Texas Voltage Magnitude Monitoring
2S on 12S Service: 75% registration Hi Volt/Failing Transformers – 111 found Oct. ’13 to Feb. ‘14

15 Utilities looking for . . . Improving grid reliability Grid Hardening
Optimize Utilization & Costs Improve grid efficiency Speed up Restoration Limit the Impact Avoid the Outage Grid Hardening Grid Resiliency Grid Restoration Grid Utilization Grid Health Improving grid reliability Used with permission from General Electric

16 Typical grid reliability objectives
Total Grid Risk Management Proactive service & maintenance Reduction of capital expenses Lower repair costs Enhance system reliability, availability & performance Support optimized asset replacement Optimize workforce productivity & safety Used with permission from General Electric Focused maintenance Reduced CapEx, OpEx Enhanced Performance Manage asset risks Efficient & Optimized Operations Proactive asset risk management across entire life cycle Used with permission from General Electric 16

17 AEP Distribution Analytics
Currently planning Load analytics Vegetation management Convert sensor data to actionable steps Future Plans Automating reliability metrics Tying asset age and health to outage trends Storm damage prediction

18 Tom Weaver –
Questions? Tom Weaver –

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