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8/2/2019 Santhosh Wave Let Ppt
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3/13/2012 1EEE Dept: 'VJIT' Hyderabad
Power Disturbance Classification
Using
Wavelet- Based Classifier
EEE(Electrical &Electronic Eng)
Presented by
G.Santhosh09915A0208
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3/13/2012 2EEE Dept: 'VJIT' Hyderabad
OUTLINE
Objectives
Introduction
FFT analysis
Wavelet transform
Simulink diagram and waveformsStatus Of Work
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3/13/2012 3EEE Dept: 'VJIT' Hyderabad
AIM OF THE PROJECT
This paper proposed a wavelet-based classifier for power
quality disturbance recognition.
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3/13/2012 4EEE Dept: 'VJIT' Hyderabad
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.
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3/13/2012 5EEE Dept: 'VJIT' Hyderabad
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.
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3/13/2012 6EEE Dept: 'VJIT' Hyderabad
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:-
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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
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3/13/2012 8EEE Dept: 'VJIT' Hyderabad
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:
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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
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Time
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Time
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Frequency (Hz)
Different in TimeDomain
Same in FrequencyDomain
At what time the frequencycomponents occur? FT can not tell!
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Simulink diagram
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LG fault voltage waveforms
Phase-a
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Phase-b
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Phase-c
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Harmonic distortion
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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.
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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
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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.
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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
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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
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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
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Implementation of DWT using filter banks.
SUB BAND CODING
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Ideal sine wave
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For 10% at sag
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For 90% at sag
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For HARMONICS
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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
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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
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THANK YOU