1

Signal Processing for Disturbance Identification in Power Systems

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

School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon Tyne NE1 8ST, UK E-mail: ghanim.putrus@unn.ac.uk

Acknowledgment: Dr Janaka WijayakulasooriyaDr Peter MinnsDr Chong NgMr Edward BentleyReyrolleReyrolle Protection (Siemens)Protection (Siemens)NaRECNaREC

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

2

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 Disturbance Identification in Power Systems

3

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 with

relatively high frequency components, which flow in the system, at relatively great distance from their point of origin.

– This “non-ideal condition” create problems for the power system, depending on the components that cause the distortion, their magnitude, frequency and duration.

4

Power Quality Disturbances

• Why now?– Modern electrical equipment are sensitive to PQ

disturbances e.g. microprocessor-based controllers, power electronic devices such as SMPS, variable speed drives, etc.

– Modern equipment (same equipment!) largely employ switching devices and hence have become the major source of degradation of PQ.

5

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 then analysed.

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 having integer multiple of the fundamental frequencyInterharmonics: Periodic waveforms which are not integer multiple of the fundamental frequency.

6

Power Quality Events• Transition 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

7

Power Quality

Disturbances

Disturbance Type TypicalDuration

Typical VoltageMagnitude

Nanosecond < 50 nsMicro second 50 ns~1 msImpulsiveMillisecond > 1 msLow freq. 0.3~50 ms 0 ~ 4 puTransientsMedium freq. 20 μs 0 ~ 8 puOscillatoryHigh freq. 5 μs 0 ~ 4 pu

Instantaneous 0.5~30 cycle 0.1~ 0.9 pu

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

Instantaneous 0.5~30 cycl. 1.1~1.8 pu

Momentary 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 < 0.1 puInterruption Temporary 3 s ~ 1 min. < 0.1 pu

Sustained Interruption > 1 min. 0.0 pu

Under Voltages > 1 min. 0.8~ 0.9 puLongDurationVariation 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 state

Noise Steady state 0 ~ 1%

Voltage Flicker Intermittent 0.1 ~ 7%

Power Frequency Variations < 10 s .95~1.05 pu

Based on IEEE Standards 1159

8

PQ Monitoring Equipment

Handheld Portable

Fixed PQ monitor/analyser Networked multipoint PQ monitors

9

PQ Monitoring Techniques• Time Domain

- Using filters or DSP techniques- Straightforward design, but inflexible, complex and response can be slow.-Does not provide much insight into the signal (e.g. frequency information 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 large DSP 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 to changes in the system. Need appropriate training.

10

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

An Ideal PQ Monitoring System would be able 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

11

Extracted Waveforms

Sampled Voltage and/or Current Waveforms

Captured Transition Events

EventClassification Transition Event Classification Steady-State Event Classification

Steady-State Disturbance Feature VectorTransition Disturbance Feature Vector

OscillatoryTransient

VoltageSag

ImpulsiveTransient

VoltageSwell

MomentarySupply

Interruption

Transition Disturbance Types

Power QualityReports

Storage Display

Over Voltage

SupplyInterruption

HarmonicDistortion

Under Voltage

Steady-State Disturbance Types

Feature Extraction Transition Feature Extractor Steady-State Feature Extractor

Captured Steady- State Events

Event Categorization

Disturbance Extraction

Block Diagram of the “Intelligent PQ Monitoring System”

12

Identify the presence of a disturbance and extracting its 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 WaveformVoltage Waveform

e(t)e(t)

rr

( )

dtTtVrtvjtVrtvC

wherer

C

TtVrCtVrCtvte

T

⋅⎥⎦⎤

⎢⎣⎡ −⋅⋅+⋅=

=

−⋅+⋅−=

∫0

)4

()()()(

, C

)4

()Im()(Re)()(

Disturbance Extraction

13

0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .8-2

0

2

Vol

tage

(pu

)

Vo lta g e W a ve fo rm

0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .8-2

0

2

Vol

tage

(pu

)

E x tra c te d No is e

0 0 .1 0 . 2 0 . 3 0 . 4 0 . 5 0 .6 0 . 7 0 .80 .5

1

1 .5

2

rms

(pu)

R M S Vo lta g e

Example

14

Steady State

Steady State

Transition State

Transition State

IntermediateSteady State

IntermediateSteady StateIntermediate

Transition StateIntermediate

Transition State

Captured EventCaptured Event

Captured EventCaptured Event

Captured EventCaptured Event

Event Categorization

State Model

15

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

0

5

(a) Sampled voltage waveform

Vs

(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

Ve

(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

STA

TE

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)

Vc

(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

2

(a) Sampled voltage waveform

Vs

(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

Ve

(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

STA

TE

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 disturbance during a capacitor switching

Identification of a voltage sag event generated by a remote fault on the

power network

16

Steady State Disturbance Feature Vector( 2 elements )

Transient Disturbance Feature Vector( 63 elements )

FeatureExtraction

Transient FeatureExtractor

( Using DWT )

Steady State FeatureExtractor

( Using FFT )

CapturedTransientEventWaveforms

CapturedSteady-stateEventWaveforms

Feature Extraction

17

Example: Oscillatory Transient

18

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

19

Time Domain Harmonic Extraction

0 50 100 150 200 250 3000

0.2

0.4

0.6

0.8

1

| H(jω

)|

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

n=6

n=4n=2

t (sec)

Am

plitu

de

Filter characteristic

20

Frequency Domain Harmonics Extraction

Mag

nitu

deA

mplitude

oftheSt ep

Func tion

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

tjn dtetfT

nF ωωFourier Transform:

21

Fast Individual Harmonic Extraction (FIHE)

)()(23)( 1 tttabc mabcm βα ⋅⋅= −

mth order harmonic reconstruction

)(tabcα

dt

ntnM

ntnM

tnMmtmmtmtm

mtmmtmtm

Tt

Tt

t kn

n

n

n

m ∫ ∑+

∞

−=

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

+

−⋅

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

+−

+−

=6

12

0

0

0

000

000

0

0

)3

2sin(

)3

2sin(

)sin(

21

21

21

)3

2cos()3

2cos()cos(

)3

2sin()3

2sin()sin(

4)(

πω

πω

ωπωπωω

πωπωω

β

mth order Harmonic Extraction

n≠3 k

22

ω1

ω5

ω7

idc=ih5

C

A

B

ih7

ih5

ih1

ω7+ω5

ω1+ω5

ih7

ih1

1st

5th

7th

11th13th

100H

z

250 H

z

200H

z

50 250

350

550

650

Freq(Hz)

Freq(Hz)

5th

300H

z

07th

17th

600

13th

23th

900

300 H

z

300 H

z

1st

11th

300

Fast Individual Harmonic Extraction (FIHE)

23

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)

Am

plitu

de

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)

Am

plitu

de

0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

50

100

150

200

t (sec)

Am

pli t u

de0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

-200

-100

0

100

200

t (sec)A

mpl

itud e

β’m (t) of the FIHEStep function

Extracted harmonic component (abcm)Distorted signal with step function added

24

ndBy filter (2 order filter)

By FT

By FIHE

00.02

50

100

150

200

t (sec)

Am

p litu

de

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

mpl

i tude

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

25

Non-Recursive Technique using ANN

22 )()( nYnYV kkk++=

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛+=+

ks

kdk fnkVnY φωcos

⎟⎟⎠

⎞⎜⎜⎝

⎛+= k

skdk f

nkVnY φωsin)(

[ ]TnSnSMnSMnSX )()1(...)()1( −−+−=

( )WXfnY k ,)( =

( )WXfnY k ,)( ++ =

X

)(

2)()(

ioldi

newi

WWW

∂∂

−=εα

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

( ) ( )222 )()()()( nYnYnYnYwhere kdkkdk++ −+−=ε

Actual

Actual

Desired

Desired

26

Response to a step increase of 5th

harmonic component

2nd order filter

6th order filter

Kalman Filter

ANN

27

Performance Analysis

0.48 0.49 0.5 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.580.4

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

28

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

EAK

ER

RO

R

ERROR INFUNDAMENTALFREQUENCY

ERROR IN 3RDHARMONIC

ERROR IN 5THHARMONIC

ERROR IN 7THHARMONIC

29

-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

MA

X ER

RO

R % FUNDAMENTAL

3RD HARMONIC

5TH HARMONIC

7TH HARMONIC

Maximum Error in CWT Caused by Normal System Frequency Variations

30

Maximum Error in ANN Technique Caused by Normal System Frequency Variations

31

Correction for Error due to Normal System Frequency Variations

• The error could be avoided if the sampling period is adjusted digitally to make one wavelength of the adjusted fundamental signal equal to 0.02 s. Measurements with e.g. FFT will then give correct results as far as the effects of frequency deviation are concerned.

32

EventClassification

Transient EventClassifier

( SAANN-1 )

Steady State EventClassifier

( SAANN-2 )

Transition Event Feature Vector( 63 elements )

Steady State Event Feature Vector( 2 elements )

OscillatoryTransient

VoltageSag

ImpulsiveTransient

VoltageSwell

MomentarySupply

Interruption

Transient Event Classes

OverVoltage

SupplyInterruption

UnderVoltage

Steady State Event Classes

HarmonicDistortion

NormalCondition

Event Classification

33

The ‘Intelligent’ PQ Monitoring System (IPQMS)

34

Summary• Use of DSP techniques in PQ monitoring and

analysis results in powerful, accurate and small size equipment.

• DSP based equipment is capable of maintaining accuracy in the “non-ideal environment” of power systems.

• Including Artificial Intelligence in PQ equipment, will help in:–– Classifying and locating the source of distortion and its Classifying 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 time Provide methods to identify trends over a period of time and suggest possible solutions!and suggest possible solutions!