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Working with AMI Data

Working with AMI Data. Eric Jung SouthEastern Illinois Electric Cooperative. Load Modeling. Hourly data advantages Accuracy! (less than 5% variance) Fast load allocation Hourly data disadvantages File verification & estimation difficult and time consuming

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Working with AMI Data

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  1. Working with AMI Data Eric Jung SouthEastern Illinois Electric Cooperative

  2. Load Modeling • Hourly data advantages • Accuracy! (less than 5% variance) • Fast load allocation • Hourly data disadvantages • File verification & estimation difficult and time consuming • Must mix with traditional load allocation if AMI not 100% deployed

  3. File Setup • Interval files • Desired interval Di • Desired interval -1 Di-1 • Desired interval +1 Di+1 • Desired interval, different day (similar load characteristics) • Desired interval, different day 2 • Repeat until all meters have a reading

  4. File Setup Continued • Outage file (fast ping) for desired day • CIS data • Multiplier • Billing rate (optional) • Reactive load • Matching field to outage table • Location number link to Premise ID

  5. Validation • Pulse count to KW multiplier: (≠ Kh) • Verify meter type, multiplier and module match (Kw * 1000) / Multiplier = Pulse count

  6. Estimation • If valid read from desired interval (Di) use it directly • If not use Di-1 or Di+1 • If all three invalid use same interval from different day • Repeat until valid reads for all meters

  7. Reading Modification • Sum total load by interval • Apply adjustment factor by percentage difference between intervals. • Ie. Di-1 total load is 5% < Di. Divide all Di-1 reads by 95% • Suggest applying adjustment factors by billing rate.

  8. Additional Load Info • Take phasing from Outage file on single phase meters • Three phase loads must come from another source: • CIS • Mapping • Reactive load must come from CIS

  9. Table links - Blue Data source - Green

  10. Load Application for 100% AMI • One load group • Set sources to swing • Set CF% to 100% • PF % only applies to those without KVAR • Apply load and save errors!

  11. Load Application for < 100% AMI • All AMI data in one load group • Settings for this load group will be as for 100% AMI • Remainder will be as traditional • Run load allocation and save errors!

  12. Phasing Correction • Match load file phasing with error file from load application • Use “re-phase elements in file” updateable utility to phase according to load file • Re-run load application and view errors • Errors will be connectivity errors

  13. Accuracy • Absolute: 3.3% (average deviation) • Individual phase variation >10A indicates phasing or loading errors. • Normal < 5 A error per phase at feeder level

  14. Two Feeder Examples • Johnston City NW (average feeder) • Shell East (very accurate)

  15. Lessons learned • Check large industrial loads • If load down during peak, consider adjusting to realistic level for analysis • Trust the Twacs phasing, but check for phase rolls in software • Scrutinize the pulse count multipliers! • There will be errors!

  16. Blink File Import • Setup blink file using AMI momentary outage data • Suggest weekly or monthly intervals • Use “apply reliability indexes” utility • Element name,saidi,saifi,caidi… • Element name,blink week 1,blink week 2…

  17. Blink Analysis • Set “color by custom” • Graphical indication of blinking line sections

  18. Blink Analysis Example

  19. Single Outage File Import • Similar to blink file import • Leave only location and on/off status in file • Convert outage status into 1 (on) or 0 (off) • Save as CSV and load as “reliability.txt” • Provides a snapshot of system status

  20. Multiple Outage File Import • Link several outage files together based on location • Create one master database with several on/off entries (maximum of 6) • i.e. Element name,2pm result, 4pm result… • Provides progress view of system restoration

  21. Outage Analysis • Single outage file: • Color by custom based on phase • Highlights line section outages • Multiple outage file • Color by custom based on status change

  22. Conclusions • AMI data can bring load model accuracy to the next level • Apply reliability indexes utility is an extremely flexible tool • AMI data is not likely to save time on load allocation

  23. Contact Info • Eric Jung • Engineering and Purchasing Manager • SouthEastern Illinois Electric Cooperative • ericjung@seiec.com

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