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Baselining PMU Data to Find Patterns and Anomalies Baselining PMU Data to Find Patterns and Anomalies

Baselining PMU Data to Find Patterns and Anomalies - PowerPoint Presentation

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Baselining PMU Data to Find Patterns and Anomalies - PPT Presentation

Brett amidan Jim Follum Kimberly Freeman Jeff Dagle Pacific Northwest National Laboratory October 6 2015 1 CIGRE US National Committee 2015 Grid of the Future Symposium Big Picture Objective ID: 733037

october data class 2015 data october 2015 class time behavior grid pmu learning baselining gov angle variables amidan normal

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Slide1

Baselining PMU Data to Find Patterns and Anomalies

Brett amidanJim FollumKimberly FreemanJeff Dagle

Pacific Northwest National Laboratory

October 6, 2015

1

CIGRE US National Committee 2015 Grid of the Future SymposiumSlide2

“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

2

October 6, 2015Slide3

Pre-Processing StepsRead raw PMU data

Develop and then use data quality filters to clean poor quality data

October 6, 2015

b.amidan@pnnl.gov

3

Remove bad data

1 day of 60 Hz PMU data (54 PMUs) = 26 GB

FrequencySlide4

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.

October 6, 2015b.amidan@pnnl.gov

4Slide5

Baselining Grid BehaviorUnivariate ApproachCreate a baseline of typical behavior for each individual variableDetermine abnormal behavior based on the baseline

Multivariate ApproachCreate a baseline across many (hundreds or even thousands) of variablesRelationship between variables is considered when determining abnormal behaviorOctober 6, 2015

5

StaticBaselining LimitsSlide6

October 6, 20156

Univariate Baselining Example

Model

Predicted Phase Angle PairValue at MidnightHours 0-23Day of Week

Date / Time Model – Time Series Based ModelPhase Angle Difference

Initial Training Period

Actual Value

Dynamic

Baselining

Limits

(Calculated Daily)Slide7

Multivariate BaseliningBaseline captures what normal behavior is expected to beGroup

similar behavior Time periods that group together indicate normal grid behaviorVariables that group together indicate highly correlated variables and may be candidates for feature reductionIdentify 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 variableOctober 6, 20157Slide8

October 6, 20158Creating a Baseline –

Unsupervised Learning

Baselining Learning Algorithm

ModelTraining Data:Historical PMU Data

Real Time PMU Data

Class 1

Class 2

Class 3

Class 4

Class 5Slide9

October 6, 2015

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.Slide10

Atypicality DetectionLightning Related Anomaly

October 6, 2015

10

Atypicality Score

Substation A

Substation BSlide11

Atypicality DetectionEquipment Failure Related Anomaly

October 6, 2015

b.amidan@pnnl.gov

11

Atypicality Score

Other PMUs behaved similarlySlide12

Phase Angle Pairs Clustering

October 6, 2015

b.amidan@pnnl.gov

12

Proximity on tree indicates similarity

Unsupervised learning (clustering) used to determine which variables are most similar during

Time Period A

.Slide13

Phase Angle Pairs Clustering

October 6, 2015

b.amidan@pnnl.gov

13

Time Period B (two months later)

Phase Angle Pair #2 is no longer like Pair #1. Why?Slide14

October 6, 201514Supervised

Learning

Baselining Learning Algorithm

Predictive ModelTraining Data:Historical PMU Data

Real Time PMU Data

Class 1

Class 2

Class 3

Class 4

Class 5

Labels

Normal

Voltage Drop

Surge

Maintenance

Weather

Weather

Normal

Voltage Drop

Surge

MaintenanceSlide15

October 6, 201515

Understanding Precursors to Inform Prediction Models

Known event

Precursor activity

Precursor Features (Signature)Inform Machine- Learning Model

Create Classification to identify future precursorsSlide16

October 6, 201516

Future Step – Using Supervised Learning to Predict Current State

Extract Signature

Classification Based Prediction Model (Trained from Historical Data)

Event 1Normal 4Precursor 3

Event 7

0.75

0.15

0.05

0.04

Likelihood

Possible Patterns

NOTE: Only events and precursors with distinct data characteristics will be identifiable Slide17

ConclusionsData 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.

October 6, 2015

17