presentation 1 : signal processing for disturbance identification in power systems

8/14/2019 Presentation 1 : Signal Processing for Disturbance Identification in Power Systems

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Signal Processing for DisturbanceIdentification in Power Systems

Dr Ghanim Putrus BSc, MSc, PhD, CEng, MIETReader in Electrical Power EngineeringDirector of Training Programmes for Industry

School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon Tyne NE1 8ST, UK E-mail: [email protected]

Acknowledgment: Dr JanakaWijayakulasooriyaDr Peter MinnsDr ChongNgMr Edward BentleyReyrolleReyrolle Protection (Siemens)Protection (Siemens)NaRECNaREC

EM Day, National Physical Laboratory, Middlesex, 29 th November, 2007


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Presentation Outline Power Quality (PQ)

Definition and introduction

Disturbances Classification PQ Monitoring Techniques Signal Processing for Disturbance Identification Intelligent PQ Monitoring System (IPQMS) Summary

Signal Processing for DisturbanceIdentification in Power Systems


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What is a Power Quality Disturbance? Deviation (steady-state or transient) of voltage or

current waveforms from a pure sinusoidal form of a

specified magnitude.

So what?

Signal distortion is normally associated withrelatively high frequency components, which flowin the system, at relatively great distance fromtheir point of origin.

This non-ideal conditioncreate problems for thepower system, depending on the components thatcause the distortion, their magnitude, frequency

and duration.


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Power Quality Disturbances

Why now? Modern electrical equipment are sensitive to PQ

disturbances e.g. microprocessor-basedcontrollers, power electronic devices such asSMPS, variable speed drives, etc.

Modern equipment (same equipment!) largelyemploy switching devices and hence havebecome the major source of degradation of PQ.


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Power Quality Events

Steady-State Events These are long term abnormalities in the voltage/current

waveform. Information are best presented as a trend of disturbance

level over a period of time (relatively long), and thenanalysed.

Voltage Flicker (Voltage Modulation)

0.05 0.06 0.07 0.08 0.09 0.1-3

-2

-1

0

1

2

3

Time, s

Current,

A

Harmonics: Periodic waveforms havinginteger multiple of the fundamentalfrequency

Interharmonics: Periodic waveforms

which are not integer multiple of thefundamental frequency.


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Power Quality Events

Transit ion Events These are sudden abnormalities of relatively short duration,

occurring in the voltage/current waveform. They are normally detected when the instantaneous value of

the voltage/current exceeds a certain threshold. These events occur between two steady-state events or

superimposed on a steady-state event.

Oscillatory transient

Change its polarity rapidlyFrequency 20Hz to 200 kHz

0 0 .0 5 0 .1 0 .1 5 0 .2-1

- 0 . 5

0

0 .5

1

1 .5

2

Impulsive Transient

Unidirectional polarity< 50ns to >1ms


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PowerQuality

Disturbances

Disturbance Type TypicalDuration T ical Volta eMagnitude

Nanosecond 1 msLow freq. 0.3~50 ms 0 ~4 pu TransientsMedium freq. 20 s 0 ~8 puOscillatoryHigh freq. 5 s 0 ~4 pu

Instantaneous 0.5~30 c cle 0.1~0.9 pu

Momentary 30 cycl.~3 s 0.1~0.9 puSag Temporary 3s ~1 min 0.1~0.9 pu

Instantaneous 0.5~30 cycl. 1.1~1.8 puMomentary 30 cycl.~3 s 1.1~1.4 puSwell

Temporary 3 s ~1 min. 1.1~1.2 pu

ShortDurationVariation

Momentary 0.5 cycl.~3 s 1 min. 0.8~0.9 pu

LongDurationVariation Over Voltages >1 min 1.1~1.2 pu

Magnitude Imbalance Steady stateVoltageImbalance Phase Imbalance Steady state

DC Offset Steady state 0 ~0.1%Harmonics Steady state 0 ~20%

Interharmonics Steady state 0 ~2%WaveformDistortions

Notching Steady stateNoise Steady state 0 ~1%

Voltage FlickerIntermittent 0.1 ~7%

Power Frequency Variations


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PQ Monitoring Equipment

Handheld Portable

Fixed PQ monitor/analyser Networked multipoint PQ monitors


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PQ Monitoring Techniques

Time Domain- Using filters or DSP techniques- Straightforward design, but inflexible, complex and response can beslow.-Does not provide much insight into the signal (e.g. frequencyinformation of the signal is not directly observable)

Time and/or Frequency Domain- Frequency analysis (e.g. FFT)- Time/frequency analysis (e.g. Wavelet transform)- Good extraction capability for PQ analysis, flexible, but require largeDSP computational power. FFT response is limited to one cycle of the

signal.

Artificial Intelligence- Using Artificial Neural Network (ANN)- Good extraction capability, flexible, fast response, can adapt tochanges in the system. Need appropriate training.


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Capture and Extract disturbance waveformCapture and Extract disturbance waveform

Categorize disturbance (steadyCategorize disturbance (steady --state or transient)state or transient)

Extract disturbance features and Identify componentsExtract disturbance features and Identify components

Classify the disturbanceClassify the disturbance (according to IEEE standards 1159)(according to IEEE standards 1159)

An Ideal PQ Monitoring System would beable to:

Do a Trend analysisDo a Trend analysis

Do Contribution analysis and Locate the sourceDo Contribution analysis and Locate the source

PQ Report and Advice


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Extracted Waveforms

Sampled Voltage and/or Current Waveforms

Captured Transition Events

EventClassification

Transition Event Classification Steady-State Event Classification

Steady-State Disturbance Feature Vector Transition Disturbance Feature Vector

Oscillatory

Transient

VoltageSag

Impulsive

Transient

VoltageSwell

Momentary

Supply Interruption

Transition Disturbance Types

Power Quality Reports Storage Display

Over

Voltage

Supply

Interruption

Harmonic

Distortion

Under Voltage

Steady-State Disturbance Types

FeatureExtraction Transition Feature Extractor Steady-State Feature Extractor

Captured Steady- State Events

Event Categorization

Disturbance Extraction

Block Diagram of the Intelligent PQ Monitoring System


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Identify the presence of a disturbance and extractingits components

0 50 100 150 200 250 300 350 400-30

-20

-10

0

10

20

30

40

50

0 50 100 150 200 250 300 350 400-4

-3

-2

-1

0

1

2

3

4

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 30.95

0.96

0.97

0.98

0.99

1

1.01

Extracted DisturbanceExtracted Disturbance

RMS VariationRMS Variation

Voltage Waveform Voltage Waveform

e(t)e(t)

rr

( )

dt T

tVrtv jtVrtvC

where

r

C

TtVrCtVrC

tvte

T

+=

=

+=

0

)4

()()()(

,

C

)4

()Im()(Re)()(

Disturbance Extraction


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-2

0

2

V o

l t a g e

( p u

)

V oltage W aveform

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-2

0

2

V o

l t a g

e ( p u

)

E xtracted Noise

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80.5

1

1.5

2

r m s

( p u )

RM S V oltage

Example


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SteadyState

SteadyState

TransitionState

TransitionState

IntermediateSteady State

IntermediateSteady StateIntermediate

Transition StateIntermediate

Transition State

CapturedEventCapturedEvent



Event Categorization

State Model


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0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-5

0

(a) Sampled voltage waveform

V s

( p . u . )

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-5

0

5

(b) Extracted disturbance waveform

V e

( p . u . )

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.21

2

3

4

(c) State transition

S T A

T E

0.1 0.102 0.104 0.106 0.108 0.11 0.112 0.114 0.116 0.118

-2

0

2

(d) Captured PQ event waveform time(s)

V c

( p . u . )

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-2

0

(a) Sampled voltage waveform

V s

( p . u . )

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5-0.2

0

0.2

(b) Extracted disturbance waveform

V e

( p . u . )

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.451

2

3

4

(c) State transition

S T A

T E

0.17 0.18 0.19 0.2 0.21 0.22 0.23

-0.1

0

0.1

(d) Captured PQ event waveform time(s)

V c

( p . u . )

Example

Identification of a voltage disturbanceduring a capacitor switching

Identification of a voltage sag eventgenerated by a remote fault on the

power network


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Steady State Disturbance Feature Vector ( 2 elements )

Transient Disturbance Feature Vector ( 63 elements )

FeatureExtraction

Transient FeatureExtractor

( Using DWT )

Steady State FeatureExtractor

( Using FFT )

Captured Transient Event

Waveforms

Captured Steady-stateEvent

Waveforms

Feature Extraction


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Example: Oscillatory Transient


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0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.40

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Harmonic Distortion

Under VoltageOver Voltage

No disturbance

Example: Steady-state Feature Space


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Time Domain Harmonic Extraction

0 50 100 150 200 250 3000

0.2

0.4

0.6

0.8

1

| H

( j

) |

Hz

n=2

n= 4

n=6n=8

Frequency Response Time response

0 0.01 0.02 0.03 0.04 0.05 0.06 0.070

0.2

0.4

0.6

0.8

1

1.2

1.4

n=8

=6

t (sec)

A m p l

i t u d e F i l t e r c h a r a c

t e r i s t i c


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Frequency Domain Harmonics Extraction

M a g n i

t u d e

A m

pl i t u d e of t h e S t e p

F un c t i on

0

50

100

150

200

250

0

2

4

6

8

10

0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

t (sec)

StepFunction

Output of FT(Magnitude of the 7thorder harmonic voltage)

+

=0

0)(1)(0

0

Tt

t

t jn dtetf TnF

Fourier Transform:


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1

5

7

idc

=ih 5

C

A

B

ih7

ih5

ih1

7+ 5

1+ 5

ih7

ih1

1st

5th

7th

11 th13 th

1 0 0 H z

2 5 0 H z

2 0 0 H z

5 0 2 5 0

3 5 0

5 5 0

6 5 0

Freq(Hz)

Freq(Hz)

5th 3 0 0 H z

0

7th17 th

6 0 0

13 th23 th

9 0 0

3 0 0 H z

3 0 0 H z

1st11 th

3 0 0

Fast Individual Harmonic Extraction(FIHE)


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Evaluation of Response Time

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

2

4

6

8

10

t (sec)

A m p l

i t u d e

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1-400

-200

0

200

400

t(sec)

A m p l

i t u d e

0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

50

100

150

200

t (sec)

A m p l

i t u d e

0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

-200

-100

0

100

200

t (sec)

A m p l

i t u d e

m (t) of the FIHEStep function

Extracted harmonic component (abc m)Distorted signal with step function added


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ndBy filter (2 order filter)

By FT

By FIHE

00.02

50

100

150

200

t (sec)

A m p l i

t u d e

0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07

With low order filter

0

50

100

150

200

t (sec)

A m p l

i t u d e

0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 0.065 0.07

By filter (6 th order filter)

By FT

By FIHE

With higher order filter

Performance Analysis


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Non-Recursive Technique using ANN

22 )()( n Yn YV kkk++=

( )

+=+ k

skdk f

nkVn Y

cos

+= k

skdk f

nkVn Y

sin)(

[ ] TnSnSMnSMnSX )()1(...)()1( +=

( )WXf n Y k ,)( =

( )WXf n Y k ,)( ++ =

X

)(

2)()(

ioldi

newi

WWW

=

Least Mean Square (LMS) algorithm used for training the ANN

( ) ( )222 )()()()( n Yn Yn Yn Ywhere kdkkdk ++ +=

Actual

Actual

Desired

Desired


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Response to a step increase of 5 th

harmonic component

2nd order filter

6 th order filter

Kalman Filter

ANN


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Performance Analysis

0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

0.5

FIHEANN IHEFFTButterworth Filter

Response to a step increase of 5th harmonic component


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Maximum Error in FFT Caused by Normal System Frequency Variations

-5

0

5

10

15

20

25

30

35

40

47.5 48 48.5 49 49.5 50 50.5 51 51.5 52 52.5

FUNDAMENTAL FREQUENCY hZ

% P

E A K E R R O

R

ERROR INFUNDAMENTALFREQUENCY

ERROR IN 3RDHARMONIC

ERROR IN 5THHARMONIC

ERROR IN 7THHARMONIC


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-0.5

0

0.5

1

1.5

2

2.5

47.5 48 48.5 49 49.5 50 50.5 51 51.5 52 52.5

FREQUENCY Hz

M A X E R R O R

% FUNDAMENTAL

3RD HARMONIC

5TH HARMONIC

7TH HARMONIC

Maximum Error in CWT Caused by Normal System Frequency Variations


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Maximum Error in ANN Technique Caused by Normal System Frequency Variations


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Correction for Error due to Normal System Frequency Variations

The error could be avoided if the sampling period isadjusted digitally to make one wavelength of theadjusted fundamental signal equal to 0.02 s.

Measurements with e.g. FFT will then give correctresults as far as the effects of frequency deviation areconcerned.


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EventClassification

Transient EventClassifier

( SAANN-1 )

Steady State EventClassifier

( SAANN-2 )

Transition Event Feature Vector ( 63 elements )

Steady State Event Feature Vector ( 2 elements )

Oscillatory Transient

VoltageSag

ImpulsiveTransient

VoltageSwell

Momentary Supply

Interruption

Transient Event Classes

Over Voltage

Supply Interruption

Under Voltage

Steady State Event Classes

Harmonic Distortion

Normal Condition

Event Classification

The Intelligent PQ Monitoring System (IPQMS)


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The Intelligent PQ Monitoring System (IPQMS)


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Summary

Use of DSP techniques in PQ monitoring andanalysis results in powerful, accurate and small sizeequipment.

DSP based equipment is capable of maintainingaccuracy in the non-ideal environment of powersystems.

Including Artificial Intelligence in PQ equipment, willhelp in: Classifying and locating the source of distortion and itsClassifying and locating the source of distortion and its

contributioncontribution

Perform long term feature analysis of disturbance levelsPerform long term feature analysis of disturbance levels

Provide methods to identify trends over a period of timeProvide methods to identify trends over a period of timeand suggest possible solutions!and suggest possible solutions!

presentation 1 : signal processing for disturbance identification in power systems

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