power signal rms shape recognition for feeder device identification using grammatical inference...
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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
March 30, 2004 3
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 4
Flow Structure: Pre-processing
Capture Datafiles
Filter DCcomponent
SignalSegmentation Extract Features Shape Recognition Display Output
KalmanParameters
InferenceEngineDictionary
March 30, 2004 5
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
March 30, 2004 7
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
March 30, 2004 8
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−= −
March 30, 2004 9
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
March 30, 2004 11
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
March 30, 2004 14
Flow Structure: Structure Inference
Capture Datafiles
Filter DCcomponent
SignalSegmentation Extract Features Shape Recognition Display Output
KalmanParameters
InferenceEngineDictionary
March 30, 2004 15
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
March 30, 2004 16
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
March 30, 2004 17
Inference Engine
INTERPRETATIONNONTERMINAL COMBINATIONS
SwellSASBS
‘V’ shapeSBAS
Inverted ‘V’SABS
Unknownall others
DipSBSAS
Step DownSBS
Step UpSAS
FlatS
March 30, 2004 18
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
March 30, 2004 19
RMS waveform recognition- Motor Start
• An example of a IU-shaped swell event.
March 30, 2004 20
RMS waveform recognition- U shaped swell
• An example of a IU-shaped swell event.
March 30, 2004 21
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
March 30, 2004 23
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
March 30, 2004 24
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
March 30, 2004 25
• 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
March 30, 2004 26
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
March 30, 2004 27
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
March 30, 2004 28
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
March 30, 2004 29
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
March 30, 2004 30
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
March 30, 2004 31
Discussion