1. Baselining PMU Data to Find Patterns and Anomalies
CIGRE US National Committee 2015 Grid of the Future Symposium
Brett amidan
Jim Follum
Kimberly Freeman
Jeff Dagle
Pacific Northwest National Laboratory
November 15, 2018

2. “Big Picture” Objective
Power grid related data (PMUs, State Estimators, Load, etc)
Analytical Tool that provides:
Real time analytics, monitoring the state of the grid
Capability to look at historical trends and events
Reliable predictions about the forthcoming state of the grid
November 15, 2018

3. Pre-Processing Steps Read raw PMU data
Develop and then use data quality filters to clean poor quality data
Frequency
Remove bad data
Information about the data we are processing – 60 Hz PMU data for 50+ PMUs from BPA. We currently have processed 27+months of data (>8 TB). We are able to read, clean, and analyze 1 minute of data in under 45 seconds.
1 day of 60 Hz PMU data (54 PMUs) = 26 GB
November 15, 2018

4. Feature Extraction (Data Signatures)
Regression fits through the data calculate estimates of value, slope, curvature (acceleration), and noise.
Can be calculated in the presence of missing or data quality flagged values.
Summaries of these features are used in the analyses.
November 15, 2018

5. Baselining Grid Behavior
Univariate Approach
Create a baseline of typical behavior for each individual variable
Determine abnormal behavior based on the baseline
Multivariate Approach
Create a baseline across many (hundreds or even thousands) of variables
Relationship between variables is considered when determining abnormal behavior
Static
Baselining Limits
November 15, 2018

6. Univariate Baselining Example
Date / Time Model – Time Series Based Model
Day of Week
Model
Hours 0-23
Predicted Phase Angle Pair
Value at Midnight
Dynamic
Baselining Limits
(Calculated Daily)
Phase Angle Difference
Actual Value
Initial Training Period
November 15, 2018

7. Multivariate Baselining
Baseline captures what normal behavior is expected to be
Group similar behavior
Time periods that group together indicate normal grid behavior
Variables that group together indicate highly correlated variables and may be candidates for feature reduction
Identify data that does not belong with the normal behavior
Time period contains data that is unusual (possible abnormal grid behavior)
Variable is unlike other variables, or something has happened to indicate a behavioral change in the variable
November 15, 2018

8. Creating a Baseline – Unsupervised Learning
Training Data:
Historical PMU Data
Baselining Learning Algorithm
Class 1
Real Time PMU Data
Model
Class 2
Class 3
Class 4
Class 5
November 15, 2018

9. Identifying Data Driven Atypical Events
Using multivariate statistical techniques to establish baselines of typical behavior, atypical moments in time can be discovered and the variables responsible can be identified.
This slide shows how the atypicality score on the left increases due to atypical behavior in the system. The plots on the right show 2 different phase angle differences that were atypical during this same time period.
November 15, 2018

10. Atypicality Detection Lightning Related Anomaly
Atypicality Score
Substation A
Substation B
November 15, 2018

11. Atypicality Detection Equipment Failure Related Anomaly
Atypicality Score
Other PMUs behaved similarly
November 15, 2018

12. Phase Angle Pairs Clustering
Unsupervised learning (clustering) used to determine which variables are most similar during Time Period A.
Proximity on tree indicates similarity
November 15, 2018

13. Phase Angle Pairs Clustering
Time Period B (two months later)
Phase Angle Pair #2 is no longer like Pair #1. Why?
November 15, 2018

14. Baselining Learning Algorithm
Supervised Learning
Training Data:
Historical PMU Data
Class 1
Weather
Baselining Learning Algorithm
Class 2
Normal
Class 3
Voltage Drop
Class 4
Surge
Class 5
Maintenance
Labels
Real Time PMU Data
Predictive Model
Weather
Normal
Voltage Drop
Surge
Maintenance
November 15, 2018

15. Understanding Precursors to Inform Prediction Models
Precursor Features (Signature)
Precursor activity
Inform Machine- Learning Model
Create Classification to identify future precursors
Known event
November 15, 2018

16. Future Step – Using Supervised Learning to Predict Current State
Possible Patterns
Likelihood
Event 1
0.75
Normal 4
0.15
Classification Based Prediction Model (Trained from Historical Data)
Precursor 3
0.05
Extract Signature
Event 7
0.04
NOTE: Only events and precursors with distinct data characteristics will be identifiable
November 15, 2018

17. Conclusions
Data driven anomalies can be identified using multivariate analyses techniques. Some of these anomalies correspond to actual events, but some do not.
Understanding precursors can inform prediction models, allowing for probability based predictions of the near-term future grid behavior.
November 15, 2018