Power quality assessment via wavelet transform analysis

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IJREAS

Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

POWER QUALITY ANALYSIS VIA WAVELET TRANSFORM Megha Khatri * Harsha Vanjani **

ABSTRACT The dependence of modern life upon the continuous supply of electrical energy makes power quality of utmost importance in the power systems area. In this paper work, a new approach to detect, localize and investigate the feasibility of classifying various types of power quality disturbances is presented, wavelet transform analysis is done as well as the concept of mother wavelet is also explained. In quality of power, the current state of art is the use of Daubechies wavelets. Daubechies wavelets belong to a special class of mother wavelet and actually they are the most used for detection, localization and classification of disturbances. The key idea underlying the approach is to decompose the disturbance signal developed with the help of matlab 7.0.5 version simulink into other signals which represent a approximated version and a detailed version of the original signal by using the wavemenu toolbox. The signal under investigation is often corrupted by noises, especially the ones with overlapping high-frequency spectrum of the transient signals. The signal firstly separated and then analysed using different techniques step by step. The decomposition is performed using multi-resolution signal decomposition techniques. The demonstration is done with the distribution system to detect and localize disturbance with actual power line disturbances. In order to enhance the detection outcomes, utilization of wavelet transform coefficients of the analysed power line signals. The results of various other methods are compared and presented the best suitable method. The simulation results clearly demonstrate the superiority and effectiveness of the wavelet transform in both current and voltage signal noise reduction. Keywords— Power Quality, Fourier Transform, Wavelets, Multi Resolution Analysis, Filters.

* Assistant Professor, Ansal Institute of Technology, Gurgaon. ** Assistant Professor, Ansal Institute of Technology, Gurgaon. International Journal of Research in Engineering & Applied Sciences http://www.euroasiapub.org

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IJREAS

Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

INTRODUCTION In 1980s Power Quality become one of the prosodic buzzword. This is due to the fact that the electronic equipments and electronic based loads are used in bulk for distributive systems. These equipments and loads are sensitive to power quality disturbances such as voltage sag, voltage swell, transients, interruptions, harmonics, etc. Technically a disturbance is a phenomenon that may degrade the performance of a device, equipment or system. It may adversely affect living or inert matter. While in power quality, any deviation from the ideal voltage or current can be labeled as a disturbance or noise, which is unwanted electrical signal. Thus the goal of denoising is to maintain fundamental power frequency a nd normal voltage level without disturbing the distributive network. Classification of Power quality disturbances phenomena includes a significant number of types, which cover a broad frequency spectrum, starting from a few Hz (flicker) to a few MHz (trans ient phenomena). Power electronic devices control circuits, arcing equipments, and loads with solid-state rectifiers and switching power supplies cause noise in power system. A typical magnitude of noise is less than 1% of the voltage magnitude. Detection of power quality event is an important aspect before denoising the signal. Peak detection, RMS value, dq transformation of voltage and wavelet transformation are used for detection and decomposition of signal.

TRANSFORMATION Mathematical transformations are applied to signals to obtain information from the time domain raw signals. When time domain signals are potted it gives time amplitude representation of the signal. This representation is not always the best representation for most signal processing related applications. There are many signal transformation techniques like FT, STFT and FFT that gives frequency content of signal to be processed. Signals whose frequency contents do not change with time are called stationary signals whereas the signals whose frequency constantly changes in time are called chirp or non-stationary signals. Such signals can be analyzed using Multi resolution analysis (MRA) that is designed to give good time resolution and poor frequency resolution at high frequencies and good frequency resolution and poor time resolution at low frequencies. In MRA wavelet functions and scaling functions are used as building blocks to decompose and construct the signal at different resolution levels.

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IJREAS

Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

WAVELET TRANSFORMS AND MOTHER WINDOW CONCEPT Wavelet transformation has ability to analysis different power quality problems simultaneously in both time and frequency domains. The wavelet transform is useful in detecting disturbance features of various types of electric power quality disturbances because it is sensitive to signal irregularities. Wavelet analysis expands functions not in terms of trigonometric polynomials but in terms of wavelets, which are generated in the form of translation of a fixed function called mother wavelet. The continuous wavelet transform (CWT) or integral wavelet transform was developed as an alternative approach to the short time fourier transform (STFT) to overcome the resolution problem. The main difference between the STFT and the CWT is the fourier transform of windowed signals are not taken and therefore single peak will be seen corresponding to a sinusoid i.e. negative frequencies are not computed and width of the window is changed as the transform is computed for every single spectral component, which is probably the most significant characteristic of the wavelet transform. The CWT is defined as

W

f (b, a )

f (t )

b,a

(t ) dt

where as b,a

(t )

1 a

t

b a

,

a

0

As seen in the above equation, the transformed signal is a function of two variables, the translation (a) and scale (b) parameters, respectively. The term translation is related to the location of the window, as window is shifted through the signal. The parameter high scales correspond to a non-detailed global view (of the signal) and low scales corresponds to a detailed view. ψ (t) is the transforming function and it is called the mother wavelet . The mother wavelet is a prototype for generating the other window functions. We must have a window function whose radius increases in time (reduce in frequency) while resolving the low frequency contents, and decreases in time (increase in frequency) while resolving the high contents of a signal. This concept leads us to the development of the wavelet functions, unlike the window of STFT, in which

(0) 1 was a time window. Here

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IJREAS

Volume 2, Issue 2 (February 2012)

wavelet window

(0)

ISSN: 2249-3905

0 , which is a time- frequency window, whereas

( ) exhibit band

pass filter characteristics. For a general window function (t ) , we define its center t as t

and the radius

2

t (t ) dt

as 1/ 2

1

The function

2

1

(t t )

(t ) described above with finite

have a frequency window ( ) with center

2

2

(t ) dt

is called a time window. Similarly , we can and the radius

defined analogous to above

as 2

1

( ) d

2

Here

(t ) with finite

and

1/ 2

2

1

(

)

2

( ) d

is called a time- frequency window in STFT.

Considering positive frequencies, defining the center

and radius

on the positive

frequencies axis as 2

d

0

:

2

d 0

1/2 2

2

:

d

0 2

d 0

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Volume 2, Issue 2 (February 2012)

The definitions for t* and

remains the same with φ(t) replaced by ψ(t) for wavelets the 1 2

uncertainty principle gives If t* is a center and in the time window.

ISSN: 2249-3905

is the radius of ψ(t), then W f (b, a) contains the information of f(t) at

b

, at

a

b

a

Applying Parseval‟s identity, the

frequency window is represented as

W

1

f (b, a)

f (t )

a

a

t b dt a

f ( ) (a ) e

2

jb

d

The frequency window becomes 1 ( a

1 ), ( a

)

Time-frequency window product 2a

2 a

4

constant

From here we observe that the flexible nature of window in the wavelet transform, whereas in STFT, time-frequency window is fixed regardless of the frequency level.

IMPLEMENTATION OF WAVELET FAMILY MATLAB SIMULATION The system simulation is done using MATLAB Simpowersystem toolbox demo as shown in Fig. 1. The System consists of a simplified synchronous machine connected to the transmission network through a 13.8 kV/ 735 kV Wye-Delta transformer. A phase-to-ground fault is applied at the middle of line 2 and 3 phase fault in line 1. In order to apply the fault along the line, this line is simulated in two sections of 100 km. As soon as the fault is detected by the protection relays, an opening command is sent to open the circuit breakers. For the analysis of the system the measurement tools like RMS b lock, d-q transformation function and wavelet toolboxes have been used.

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Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

Fig. 1: 3 Phase fault introduced in Line 1 and PG Fault in Line 2 of Distribution System of 735 KV Trans mission Line.

Fig. 1.1: Event Detection Processing Block.

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Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

ANALYSIS USING GUI

Fig. 1.3: Analysis of 3 Phase and PG Fault introduced in Distribution System

Fig. 1.3.1 : Va Phase.

Fig. 1.3.2 : Vb Phase.

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Volume 2, Issue 2 (February 2012)

ISSN: 2249-3905

Fig. 1.3.3: Vc Phase.

Fig. 1.3.1.1: Analysis done on Phase Va by using db4 wavelet upto level 5.

CONCLUSION In wavelets time taken by them to remove the fault and make the system again working is in nano seconds while the time taken by RMS method and other methods for removing fault is in mille seconds. Further Wavelets can also be compared with neural networks even there they give better results than neural network method to solve the power quality problems. For the Analysis daubechies level 5th is used which gives better resolution. A great advantage of RMS method is its simplicity, speed of calculation and less requirement of memory, because RMS can be stored periodically instead of sample per sample However, its dependency of window length is considered as a disadvantage: one cycle window length will give better results in terms of profile smoothness than a half cycle window at the cost of a lower timeresolution. Moreover, RMS does not distinguish between fundamental frequency, harmonics or noise components; therefore accuracy will depend of the harmonics and noise content. The RMS voltage value is desirable when harmonics and/or flicker problem is the most outstanding issue. The fundamental voltage component characterization approach is based upon FFT/DFT and wavelet analysis of the voltage waveform. It is qualified to power quality International Journal of Research in Engineering & Applied Sciences http://www.euroasiapub.org

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ISSN: 2249-3905

disturbances featured in remarkable magnitude changing situations, such as sags, swells and interruptions.

The fundamental voltage component is proved as a more appropriate

magnitude characterization approach in most situations. Further by taking the individually each voltage of each phase the analysis is done on it and the results are compared as shown previously. Thus we can conclude from the above all that the wavelets used for analysing the signals gives better results than any other method.

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noisy

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SHI Yunhui And RUAN Qiuqi “ Continuous Wavelet Transforms” IEEE Trans. Info. Theory,0-7803-8406-7/04 C 2004

3.

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4.

Jan-olov Stromberg “Construction of wavelets” published by MGA tutorials September 08,2004

5.

Book on Signal Processing Of Power Quality Disturbances By Math H.J. Bollen and Irene Y.H Gu IEEE Press Series on power engineering Mohamed E. EL.-Hawary, Series Editor A John Wiley & Sons, Inc., Publication.

6. Oscar C. Montero-Hernández and Prasad N.Enjeti „A Fast Detecting Algorithm Suitable For Mitigating Of Numorous Power Quality Disturbances” Vol.41, NO.6, November/December 2005. 7.

N.S.D. Brito, B.A. Souza And F.A.C. Pires “ Daubechies wavelets in Quality of Electrical Power” 0-7803-5105-3C 1998 IEEE.

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of Distribution Power Quality Events with Fourier and Wavelet Transforms”, IEEE Trans. on Power Delivery, Vol. 15, No. 1, 2000. 14. L. Angrisani, P. Daponte, and M. D‟Apuzo,

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