March 30, 2004 1

Power Signal RMS Shape Recognition for Feeder Device Identification using

Grammatical Inference Technique

Praveen PankajakshanPower System Automation Lab

Department of Electrical EngineeringTexas A&M University

March 30, 2004 2

Hypothesis: Shape of the Root Mean Square (RMS) of the voltage and current signal is an important criteria in identifying feeder devices.

Objective: The aim of this project is to recognize RMS shapes of Power signals using Syntactic techniques.

Hypothesis and Project Objective

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The basic steps involved are:

• Step I: Pre-processing

• Step II: Segmentation and Feature Extraction

• Step III: Structure Description and Inference

Presentation Overview

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Flow Structure: Pre-processing

Capture Datafiles

Filter DCcomponent

SignalSegmentation Extract Features Shape Recognition Display Output

KalmanParameters

InferenceEngineDictionary

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The basic steps involved are:

• Step I: Pre-processing

• Step II: Segmentation and Feature Extraction

• Step III: Structure Description and Inference

Presentation Overview

March 30, 2004 6

Flow Structure: Segmentation and Feature Extraction

Capture Datafiles

Filter DCcomponent

SignalSegmentation Extract Features Shape Recognition Display Output

KalmanParameters

InferenceEngineDictionary

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Signal Segmentation: Representation model

• Mathematical model– State space representation– Estimated signal

• Parameters – = process state vector at step k– = state transition matrix – = state estimation error vector– = observation vector– = measurement error vector– = measurement vector

1k k k kx x wφ+ = +k k k kz H x v= +

kx

kφkw

kHkvkz

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Signal Segmentation: Kalman Filter

• Kalman Filter Equations– Gain Calculation:

– Update estimate:

– Compute error covariance and project ahead:

– Update the state vector:

– Residue calculation:

1( )T Tk k k k k k kK P H H P H R− − −= +

^ ^^

( )k k k k k kx x K z H x− −= + −

1T

k k k k kP P Qφ φ−+ = +

( )k k k kP I K H P−= −

^ ^

1k kkx xφ−

+ =^

k k k kr z H x−= −

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Flow Structure: Segmentation and Feature Extraction

Capture Datafiles

Filter DCcomponent

SignalSegmentation Extract Features Shape Recognition Display Output

KalmanParameters

InferenceEngineDictionary

March 30, 2004 10

Signal Segmentation and Feature Extraction

• Signal segmentation – Outliers in the residues correspond to regions in the signal with one or

more events.

• Outlier Detection• Features Extracted

– Number of events– Event Size– Event Location– Separation between multiple events– Number of Phases involved– Structure description

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The basic steps involved are:

• Step I: Pre-processing

• Step II: Segmentation and Feature Extraction

• Step III: Structure Description and Inference

Presentation Overview

March 30, 2004 12

Flow Structure: Structure Inference

Capture Datafiles

Filter DCcomponent

SignalSegmentation Extract Features Shape Recognition Display Output

KalmanParameters

InferenceEngineDictionary

March 30, 2004 13

RMS Structure Sampling

Very slowly decreasing (f)

Slowly decreasing (g)

Fast decreasing (h)

Very fast decreasing (i)

Flat (e)

Very slowly increasing (d)

Slowly increasing (c)

Fast increasing (b)

Very fast increasing (a)

REPRESENTATIONPATTERN

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Flow Structure: Structure Inference

Capture Datafiles

Filter DCcomponent

SignalSegmentation Extract Features Shape Recognition Display Output

KalmanParameters

InferenceEngineDictionary

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Grammar and Dictionary

WORDNONTERMINAL COMBINATIONS

[SABd, SABe, SABf]SABS

[Sa, Sb, Sc]SA

[SBAd, SBAe, SBAf]SBAS

[SASBd, SASBe, SASBf]SASBS

[SBSAd, SBSAe, SBSAf]SBSAS

[SBd, SBe, SBf]SBS

[SAd, SAe, SAf]SAS

[d, e, f, Sd, Se, Sf]S

[A B C]T

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Grammar and Dictionary

WORDNONTERMINAL COMBINATIONS

[Si, Sh, Sg]SB

[ ]U

[SAi, SAh, SAg]SAB

[SBa, SBb, SBc]SBA

[SBSa, SBSb, SBSc]SBSA

[SASi, SASh, SASg]SASB

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Inference Engine

INTERPRETATIONNONTERMINAL COMBINATIONS

SwellSASBS

‘V’ shapeSBAS

Inverted ‘V’SABS

Unknownall others

DipSBSAS

Step DownSBS

Step UpSAS

FlatS

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Feeder Device Characteristics

V-Swell, V-DipInrush/Reclose transient

IU-Dip, IU-SwellMotor Stop

IU-Swell, U-DipMotor Start

Step Up, Step downCapacitor Switching OFF

Step down, Step UpCapacitor Switching ON

RMS SHAPE (CURRENT, VOLTAGE)

FEEDER DEVICE AND OPERATION

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RMS waveform recognition- Motor Start

• An example of a IU-shaped swell event.

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RMS waveform recognition- U shaped swell

• An example of a IU-shaped swell event.

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RMS waveform recognition- U shaped dip

• An example of a U-shaped dip event.

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RMS waveform recognition example- Capacitor Switching ON

• Step up Event.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 57680

7690

7700

7710

7720

7730

7740

7750

Time (Seconds)

RMS Signal Phase ARMS Signal Phase BRMS Signal Phase C

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RMS waveform shapes-Step Up

• Residual signal output from the Kalman Filter after event detection

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

50

100

150

200

250

300

350

400

450

TIME (in secs)

RESID

UAL

SIG

NAL

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Extracted Feature Vectors-Step Up

• Features extracted from the signal:

fdadfStructure Description

[1 1 1]Phase Information (Exists-1, N/A-0)

[NaN NaN NaN]Event Separation

[1 1 1]Event Direction (Up-1, Down-0)

[0.48 0.4921 0.4809]Event Size (% change)

[159 159 160]/[2.65 2.65 2.67]Event Location (Cycles/Seconds)

[1 1 1]Number of Events

FEATURE VALUES FOR ALL THREE PHASES

FEATURE DESCRIPTION

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• Original RMS waveform is divided into 3 regions (pre-event, event, post-event) and 5 sub-regions.

• Pre-Event duration: 156 cycles, event duration: 5 cycles, post-event duration: 199 cycles.

• Reconstruction based on our extracted primitives:

Signal Reconstruction based on primitives-Step Up

f da

d f

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Grammatical Inference-Step Up

• Rules executed:– S f– S Sd– SA Sa – SAd SAS– SASf SAS

• Inference is complete when look up is complete• Engine interprets it as a Step Up event

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RMS waveform recognition- V shaped dip

• An example of a V-shaped dip event.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 57750

7800

7850

7900

7950

8000

8050

8100

Time (Seconds)

RMS Signal ARMS Signal BRMS Signal C

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RMS waveform recognition- V shaped swell

• An example of a V-shaped swell event.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5120

140

160

180

200

220

240

260

Time (Seconds)

RMS Signal ARMS Signal BRMS Signal C

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Extracted Feature Vectors-V Swell

• Features extracted from the signal:

dfbgdStructure Description

[0 0 1]Phase Information (Exists-1, N/A-0)

[NaN NaN 0.033]Event Separation

[NaN NaN; NaN NaN; 1 0]Event Direction (Up-1, Down-0)

[NaN NaN; NaN NaN;14.41 -3.60]Event Size (% change)

[NaN NaN; NaN NaN;149 151]Event Location (Cycles)

[0 0 2]Number of Events

FEATURE VALUES FOR ALL THREE PHASES

FEATURE DESCRIPTION

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CONCLUSION• Syntactic representation for RMS of a power signal provides

flexibility and includes uncertainty.• Testing the method is difficult and manual classification

requirement.• Prior knowledge is required.FUTURE WORK• Learning new shapes for recognizing emergent conditions

and unknown devices.• Combination of features to overcome difficulty in separability.

Conclusion and future work

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Discussion