Santhosh Wave Let Ppt

8/2/2019 Santhosh Wave Let Ppt

1/29

3/13/2012 1EEE Dept: 'VJIT' Hyderabad

Power Disturbance Classification

Using

Wavelet- Based Classifier

EEE(Electrical &Electronic Eng)

Presented by

G.Santhosh09915A0208


2/29


OUTLINE

Objectives

Introduction

FFT analysis

Wavelet transform

Simulink diagram and waveformsStatus Of Work


3/29


AIM OF THE PROJECT

This paper proposed a wavelet-based classifier for power

quality disturbance recognition.


4/29


Intorduction

Power system is defined as a network of one or moregenerating units, loads and power transmission lines

including the associated equipments connected to it.

The stability of a power system is its ability to developrestoring forces equal to or greater than the disturbing forces

to maintain the state of equilibrium.

Power system stability problem gets more pronounced incase of interconnection of large power networks.


5/29


Power quality has became a significant issue to both

costumer and utility companies since these equipment arevery sensitive in relation to input voltage disturbance results

in malfunction and damaging bringing huge losses .

Most common power quality problem is the currentharmonics which might damage and malfunction the sensitive

equipment most severe one is voltage sag which produces

equipment tripping and dropping out for industrial plant.


6/29


Power Quality Problems

1.Short term faults

2.Long term faults

Short term faults:-

I. Voltage sag

II. Voltage swell

III. Voltage fluctuation

IV. Voltage spike

I. Voltage unbalance

II. Harmonic distortion

III. Long interruptionsIV. noise

Long term faults:-


7/29


WHAT IS TRANSFORM?

Transform of a signal is just another form of representing thesignal. It does not change the information content present.

WHY TRANSFORM?

Mathematical transform are applied to signal to obtain further

information which is not present in raw signal

Three different transforms

FOURIER TRANSFORM

SHORT TIME FOURIER TRANSFORM

WAVELET TRANSFORM


8/29


Fast Fourier TransformThe FFT is a highly efficient procedure for computing the DFT of

a definite series and requires less number of computations then

that of direct evaluation of DFT.

We know that the Fourier transformof discrete transform x(k) isgiven by

Fourier Transform of a time domain signal gives frequency

domain representation.

FOURIER TRANSFORM:


9/29


LIMITATION OF FOURIER TRANSFORM:

When we are in time domain Fourier transform will not give informationregarding frequency and when we are in frequency domain it will not provideinformation regarding time.

0 0.5 1-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 250

50

100

150

Time

Magnit

ude

Magnit

ude

Frequency (Hz)

0 0.5 1-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 250

50

100

150

Time

Magnit

ude

Magnit

ude

Frequency (Hz)

Different in TimeDomain

Same in FrequencyDomain

At what time the frequencycomponents occur? FT can not tell!


10/29


Simulink diagram


11/29


LG fault voltage waveforms

Phase-a


12/29


Phase-b


13/29


Phase-c


14/29


Harmonic distortion


15/29



16/29


Wavelet Introduction

Earlier digital relays have proven very useful and efficientin power system steady state analysis. Fourier transforms

etc.

Due to the presence of non stationary signals the

performance of these techniques are limited.

Recent solution to this problem is WAVELET TRANSFORM

which was introduced by F.JIANG.


17/29


Demonstration of wave

A wave is an oscillating function of time or space an

is periodic

Wavelet A small wave

Means the window function is of finite length

Wave


18/29


Significance of wavelet transform

It has the capability of providing accurate transient information in both

time and frequency domain.

It has a special feature of variable time frequency localization which is

very different from windowed Fourier transform.

It is applied to decompose the current signal. The time and frequency

domain features of transient signals are extracted the spectral energies

of wavelet components are calculated and then employed to detect and

classify the faults.


19/29


For a signal or function X(t) its continuous wavelet transform (CWT)IS

Continuous wavelet transform

Where a = scaling parameter

b = translation parameter

Zi = mother wavelet function


20/29


For a signal or function X(k) its discrete wavelet transform

(DWT) is

Whereasterisk denotes a complex conjugate,

the parameters a and b in Eq.( 1) are replaced bydigitized parameters,

also k and m are scale and time-shift parameters.

discrete wavelet transform


21/29


Multi Resolution Analysis

Gives good time resolution and poor frequencyresolution at high frequencies and good frequency

resolution and poor time resolution at low frequencies

This helps as most natural signals have low frequencycontent spread over long duration and high frequency

content for short durations


22/29


Implementation of DWT using filter banks.

SUB BAND CODING


23/29


Ideal sine wave


24/29


For 10% at sag


25/29


For 90% at sag


26/29


For HARMONICS


27/29


CONCLUSION

Fourier transform provided information regarding

frequency.

So, wavelet transform is preferred over Fourier transform

and short time Fourier transform since it provided multi

resolution.

Since the time and frequency resolutions can be achieved together


28/29


References:-

1.M. Brent Hughes, John S. Chan. and Don 0. Koval." Disuibution Customer Power QualityExperience," EEE Trans. on Industry Applications. Vol. 29. No. 6. NavembedDecember 1993, pp. 1204-

1211. I. M. Brent Hughes, John S. Chan. and Don 0. Koval." Disuibution Customer Power QualityExperience," EEE Trans. on Industry Applications. Vol. 29. No. 6. NavembedDecember 1993, pp. 1204-

1211.

2. Ench W. Gunther and Harshad Mehta, '' A Suvey of DistributionSystem Power Quality - PreliminaryResults, IEEE Trans. an Power Delivery, Vol. IO, No.1, January 1995. pp. 322-329.

3. S. Santoso, E. J Powers, W. M Grady. P. Hofmnn, Power Quality Assessment via Wavelet Transform

Analysis, IEEE Trans. on Power Delivery Vol. I I. No. 2. Apr.1996, pp.924-930.

4. T.B. Littler, D.J. MOTTOW, Wavelrts for the Analysis and Compression of Power SystemDisturbances. IEEE Trans. an Power Delivery, Vol. 14. No. 2, April 1999, pp. 358-364.

5. 0. Poisson, P. Rioual and M. Meunier, New Signal Processing Tools Applied to Pawer Quality

analysis. IEEE Trans. an Power Delivery, Vol. 14,No. 2,April 1999, pp. 561-566.

6. Heydt, P.S. Fjeld, C.C. Liu. D. Pierce. L. Tu, and G Hensley. Applications of the Windowed FFT to

Electric Power Quality Assessment, IEEE Trans. on Power Delivery, Vol. 14. No. 4, October 1999. pp.

1411-1416.7. G. T. Heydt. A. W. Calli. Transient Power Quality Problem Analyzed Using Wavelets, IEEE Trans.

on Power Delivery, Vol. 12, No. 2, April 1997


29/29


THANK YOU

Documents

Santhosh Wave Let Ppt