Interpreting Reliability Data Yamille del Valle, Nigel Hampton, Josh - - PowerPoint PPT Presentation

1

Interpreting Reliability Data

Yamille del Valle, Nigel Hampton, Josh Perkel, Essay Wen Shu

Presentation prepared for the IEEE PES Distribution Reliability Group

Joint Technical Committee Meeting (JTCM) January 12-16, 2020 • Jacksonville, FL, USA

2

The information contained herein is, to our knowledge, accurate and reliable at the date of publication. Neither GTRC nor The Georgia Institute of Technology nor NEETRAC shall be responsible for any injury to or death of persons or damage to or destruction of property or for any other loss, damage or injury of any kind whatsoever resulting from the use of the project results and/or data. GTRC, GIT and NEETRAC disclaim any and all warranties, both express and implied, with respect to analysis or research or results contained in this report. It is the user's responsibility to conduct the necessary assessments in order to satisfy themselves as to the suitability of the products or recommendations for the user's particular purpose. No statement herein shall be construed as an endorsement of any product, process or provider.

Notice

3

Purpose of the Study

• SAIDI, SAIFI are internationally recognized indices used

to describe electric service reliability at distribution level

• Used to:

– Provide Context – Understand correlations – Improve performance – Estimate future performance / establish resource needs

• Public Data available (IEEE, EIA, Regulators, CEER,

Utility websites etc.)

4

Reliability Growth Model

7 6 5 4 3 2 1.5 1 1000 100

Cummulative Time (years) Cummulative IEEE SAIDI

Utility ID = U4

Cumulative Time (years) Cumulative IEEE SAIDI

7 6 5 4 3 2 1.5 1 1000 100

Cummulative Time (years) Cummulative IEEE SAIDI

Utility ID = U3

7 6 5 4 3 2 1.5 1 1000 100

Cummulative Time (years) Cummulative IEEE SAIDI

Utility ID = U25

5

Perform ance Evaluation

7 6 5 4 3 2 1

200 150 100 90 80 70 60 50 40 30 20

Cummulative Time (years) Cummulative IEEE SAIDI

Results include rows where 'ID_4'=301.

Utility ID = 30

7 6 5 4 3 2 1

200 150 100 90 80 70 60 50 40 30 20

Cummulative Time (years) Cummulative IEEE SAIDI

Results include rows where 'ID_4'=301.

Utility ID = 30

7 6 5 4 3 2 1

200 150 100 90 80 70 60 50 40 30 20

Cummulative Time (years) Cummulative IEEE SAIDI

Results include rows where 'ID_4'=301 Or 'ID_4'=302.

Utility ID = 30

Cumulative Time (years) Cumulative IEEE SAIDI

6

Perform ance Quantification

7 6 5 4 220 200 180 160 140 120 100 80

Cummulative Time (years) Cummulative IEEE SAIDI

Results include rows where 'ID_4'=301 Or 'ID_4'=302.

Utility ID = 30

Estimated SAIDI Actual SAIDI

Cumulative Time (years) Cumulative IEEE SAIDI

 = 8%

7

10 9 8 7 6 5 4 3 2 1.5 1

800 700 600 500 400 300 200 100

CumTime CumSAIDI w/o MED

Utility ID = U1

Trending / Prognosis

10 9 8 7 6 5 4 3 2 1.5 1

800 700 600 500 400 300 200 100

CumTime CumSAIDI w/o MED

6 8

Utility ID = U1

10 9 8 7 6 5 4 3 2 1.5 1

800 700 600 500 400 300 200 100

CumTime CumSAIDI w/o MED

6 8 428.35 608.38

Utility ID = U1

Cumulative Time (years) Cumulative IEEE SAIDI

Cumulative 2018 SAIDI

8

Perform ance by System

OH contribution to total SAIDI is higher than the contribution of UG However both systems are comparable in size

30 20 15

200 150 100 90 80 70 60 50 40 30

Cummulative Time (months) Cummulative SAIDI - 2013 to 2015

OH UG EEI_Origin_1

Results exclude rows where 'EEI_Origin_1'="Gen" Or 'CumTime_1'<12.

Cumulative IEEE SAIDI Cumulative Time (months)

9

Perform ance by Cause

35 30 25 20 15 10

20 15 10 9 8 7 6 5 4

Cummulative Time (months) Cummulative SAIDI - 2013 to 2015

BALLOON VEHICLE HIT Cause 1 System Level

Cumulative IEEE SAIDI Cumulative Time (months)

Vehicle hits have higher contribution to the SAIDI however balloon caused outages are increasing

10 10

Reliability Index

Compute average yearly SAIDI and SAIFI

7 6 5 4 3 2 1.5 1

900 800 700 600 500 400 300 200 150

Cumulative Time Cumulative SAIDI

7 6 5 4 3 2 1.5 1

10 9 8 7 6 5 4 3 2

Cumulative Time Cumulative SAIFI

Assess SAIDI and SAIFI evolution over time Determine second order performance change (tip-up or tip-down)

11 11

Project Status - Benchmarking

Multivariate Machine Learning Algorithm based on utility data, self adjust with the “information content” of the data Overall score combines all features Outlier rejection included Model builds with experience Influence of individual good / bad years is minimized

Green values are good, Amber needs to be monitored, Red is concern

Average SAIFI Average SAIDI SAIFI Trend SAIDI Trend SAIFI Tip‐up/down SAIDI Tip‐up/down Score

Utility Average SAIFI Average SAIDI SAIFI Trend SAIDI Trend SAIFI Tip‐up/down SAIDI Tip‐up/down Score Example

12 12

USA Visualization – IEEE Method

Algorithm (outlier rejection, level, trend) can also be used for visualization 800+ Utilities 25 PUDs 220+ Munis 400+ Coops 150+ IOUs

13 13

State Cooperatives Example - Median Data

Size of bubble relates to number of CoOps reporting Data Scrubbed Outliers Rejected 2103 to 2018

14 14

State Cooperatives Example - Trending

Data Scrubbed Outliers Rejected 2103 to 2018

15 15

State Cooperatives Example - Local Context

State 1 State 2 State 3 State 4 State 5

16 16

State Cooperatives Example - Benchmarking

Utility Average SAIFI Average SAIDI SAIFI Trend SAIDI Trend SAIFI Tip‐up/down SAIDI Tip‐up/down Score Example 1.51 122.42 0.87 0.76 0.23 0.27 4.879

Reliability Index

• Multivariate Machine Learning

algorithm creates a single reliability index for benchmarking purposes

• Index can be used to compare with
• ther state cooperatives
• Can be used with peers selected by
• ther criteria (not geographical

proximity)

• Can be used with any level of

granularity

State 1 State 2 State 3 State 4 State 5

17

For more information please contact: yamille.delvalle@neetrac.gatech.edu nigel.hampton@neetrac.gatech.edu