Transcript Document
Data Analytics at American Electric Power Presentation to: SWEDE May 8, 2014 Tom Weaver, PE Business Analytics is the convergence of three key areas Business Opportunities Working with OpCos, define business opportunities or problems we are trying to solve in 3 areas ‐ Distribution ‐ Meter ‐ Consumer Technical Solutions AEP Business Solution Commercial Solutions Define the commercial relationships that are required to make this journey successful ‐ Build vs. Buy ‐ Collaboration with others Define the technical solutions that meet business needs for ‐ Data capture ‐ Data storage ‐ Complex processing ‐ Visualization Collaboration is vital as considerations are inter-connected What does Business Analytics Mean? Analytic Capability Answers the Questions Standard Reports What Degree of Intelligence (Competitive Advantage) When Ad Hoc Reports happened? did it happen? How many? often? Where? How Query Drilldown Or OLAP Where Alerts/Monitoring When How exactly is the problem? to I find the answers? What Statistical Analysis Why should I react? actions are needed now? is this happening? opportunities am I missing? What Forecasting What if the trends continue? How much is needed? When will it be needed? Predictive Modeling What How Analytic Capabilities Optimization will happen next? will it affect my business? How do we do things better? is the best decision for a complex problem? What Analytics framework today – Conceptual View Started simple OPERATIONALpending maturity of vendor solutions AMI (UIQ & LGCC) SAS MDM 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 Standard GUI for the integration of meter events and orders Standard MACSS (MCS& OPS) Operational Data Store DA System(PI) PowerOn CES Data PEV Data PeopleSoft GIS SOURCE DATA TERS SWAMI AMIGO Why is Data Analytics a Strategic Initiative for the Industry? Sense Power Plants Communicate Transmission Compute Substations Control Distribution Consumers Sensor and Communication Technology Leapfrogging Ability to Mine Data for High Value Applications for Electric Utilities © 2013 Electric Power Research Institute, Inc. All rights reserved. 5 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 © 2013 Electric Power Research Institute, Inc. All rights reserved. 6 Data Integration and Analytics Applied to a Storm Event and Recovery Day –(3) Storm Forecast Data Sets: Weather Forecasts Historical Damage Storm Protection Settings 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 Predictive Analytics Management Systems Assets and Inventory Field Crew Interfaces 10 Field Crew Support Customers Interfaces AMI, SCADA, GIS High Performance Computing Requirements Damage Assessments N+1 Data Sources © 2013 Electric Power Research Institute, Inc. All rights reserved. 101 011 Situational Analytics 7 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? 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. Ohio PSO I&M Issues 38 19 0 Population 5,776 1,718 473 0.66% 1.11% Voltage Magnitude Analysis – Transformer Rated Meters • Next Step: Create automated programs to analyze. Description of Issue Corrected Service Type in Meter Bad Cable Service Incorrect in MACSS Bad PT Blown Transformer Fuse Theft No Issues Total Feedback Number 8 2 2 2 1 1 1 17 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. FIRST: Clean Up AEP’s MACSS Data – Correlate Premises to Proper Transformer 2179 South 2S on 12S Service: 75% registration Texas Voltage Magnitude Monitoring Hi Volt/Failing Transformers – 111 found Oct. ’13 to Feb. ‘14 Utilities looking for . . . Grid Hardening Avoid the Outage Grid Resiliency Limit the Impact Grid Restoration Speed up Restoration Grid Utilization Improve grid efficiency Grid Health Optimize Utilization & Costs Improving grid reliability Used with permission from General Electric 15 Typical grid reliability objectives Total Grid Risk Management Proactive asset risk management across entire life cycle Focused maintenance – Proactive service & maintenance Reduced CapEx, OpEx – Reduction of capital expenses Enhanced Performance – Lower repair costs Manage asset risks Efficient & Optimized Operations – Enhance system reliability, availability & performance – Support optimized asset replacement – Optimize workforce productivity & safety • UsedUsed with permission from with permission from General Electric • General Electric 16 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 Questions? Tom Weaver – [email protected] 18