power quality analysis via wavelet transform - Euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905INTRODUCTIONIn 1980s Power Quality become one of the prosodic buzzword. This is due to the fact that theelectronic 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 aphenomenon that may degrade the performance of a device, equipment or system. It mayadversely affect living or inert matter. While in power quality, any deviation from the idealvoltage or current can be labeled as a disturbance or noise, which is unwanted electricalsignal. Thus the goal of denoising is to maintain fundamental power frequency and normalvoltage level without disturbing the distributive network. Classification of Power qualitydisturbances phenomena includes a significant number of types, which cover a broadfrequency spectrum, starting from a few Hz (flicker) to a few MHz (transient phenomena).Power electronic devices control circuits, arcing equipments, and loads with solid-staterectifiers and switching power supplies cause noise in power system. A typical magnitude ofnoise is less than 1% of the voltage magnitude.Detection of power quality event is an important aspect before denoising the signal. Peakdetection, RMS value, dq transformation of voltage and wavelet transformation are used fordetection and decomposition of signal.TRANSFORMATIONMathematical transformations are applied to signals to obtain information from the timedomain raw signals. When time domain signals are potted it gives time amplituderepresentation of the signal. This representation is not always the best representation for mostsignal processing related applications. There are many signal transformation techniques likeFT, STFT and FFT that gives frequency content of signal to be processed. Signals whosefrequency contents do not change with time are called stationary signals whereas the signalswhose frequency constantly changes in time are called chirp or non-stationary signals. Suchsignals can be analyzed using Multi resolution analysis (MRA) that is designed to give goodtime resolution and poor frequency resolution at high frequencies and good frequencyresolution and poor time resolution at low frequencies. In MRA wavelet functions and scalingfunctions are used as building blocks to decompose and construct the signal at differentresolution levels.International Journal of Research in Engineering & Applied Sciences 20http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905WAVELET TRANSFORMS AND MOTHER WINDOW CONCEPTWavelet transformation has ability to analysis different power quality problemssimultaneously in both time and frequency domains. The wavelet transform is useful indetecting disturbance features of various types of electric power quality disturbances becauseit is sensitive to signal irregularities.Wavelet analysis expands functions not in terms of trigonometric polynomials but in termsof wavelets, which are generated in the form of translation of a fixed function called motherwavelet. The continuous wavelet transform (CWT) or integral wavelet transform wasdeveloped as an alternative approach to the short time fourier transform (STFT) to overcomethe resolution problem. The main difference between the STFT and the CWT is the fouriertransform of windowed signals are not taken and therefore single peak will be seencorresponding to a sinusoid i.e. negative frequencies are not computed and width of thewindow is changed as the transform is computed for every single spectral component, whichis probably the most significant characteristic of the wavelet transform. The CWT is definedasW f ( b,a)f ( t)( t)dtb,awhere asb,a( t)1aAs seen in the above equation, the transformed signal is a function of two variables, thetranslation (a) and scale (b) parameters, respectively. The term translation is related to thelocation of the window, as window is shifted through the signal. The parameter high scalescorrespond to a non-detailed global view (of the signal) and low scales corresponds to adetailed view. ψ(t) is the transforming function and it is called the mother wavelet . Thetbamother wavelet is a prototype for generating the other window functions.We must have a window function whose radius increases in time (reduce in frequency) whileresolving the low frequency contents, and decreases in time (increase in frequency) whileresolving the high contents of a signal. This concept leads us to the development of thewavelet functions, unlike the window of STFT, in which ( 0) 1was a time window. Here,a0International Journal of Research in Engineering & Applied Sciences 21http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905wavelet window ( 0) 0, which is a time-frequency window, whereas ( ) exhibit bandpass filter characteristics. For a general window function (t), we define its center t ast1t ( t)22dtand the radius as1( tt)2( t)2dt1/ 2The function (t)described above with finite is called a time window. Similarly , we canhave a frequency window ( ) with center and the radius defined analogous to aboveas1( )22d1()2()2d1/ 2Here (t)with finite and is called a time- frequency window in STFT.Considering positive frequencies, defining the center and radius on the positivefrequencies axis as:0022dd1/2:022d2d0International Journal of Research in Engineering & Applied Sciences 22http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905The definitions for t* anduncertainty principle givesremains the same with φ(t) replaced by ψ(t) for wavelets the12If t* is a center and is the radius of ψ(t), then W f ( b , a ) contains the information of f(t)in the time window.at b a , atb a Applying Parseval‟s identity, thefrequency window is represented asWf ( b,a)1af ( t)tbadta2f ()( a)ejbdThe frequency window becomes11( ), (aa)Time-frequency window product2a 2a4 constantFrom here we observe that the flexible nature of window in the wavelet transform, whereas inSTFT, time-frequency window is fixed regardless of the frequency level.IMPLEMENTATION OF WAVELET FAMILYMATLAB SIMULATIONThe system simulation is done using MATLAB Simpowersystem toolbox demo as shown inFig. 1. The System consists of a simplified synchronous machine connected to thetransmission network through a 13.8 kV/ 735 kV Wye-Delta transformer. A phase-to-groundfault is applied at the middle of line 2 and 3 phase fault in line 1. In order to apply the faultalong the line, this line is simulated in two sections of 100 km. As soon as the fault isdetected 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 block, d-q transformationfunction and wavelet toolboxes have been used.International Journal of Research in Engineering & Applied Sciences 23http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905Fig. 1: 3 Phase fault introduced in Line 1 and PG Fault in Line 2 of Distribution Systemof 735 KV Transmission Line.Fig. 1.1: Event Detection Processing Block.International Journal of Research in Engineering & Applied Sciences 24http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905ANALYSIS USING GUIFig. 1.3: Analysis of 3 Phase and PG Fault introduced in Distribution SystemFig. 1.3.1 : V a Phase.Fig. 1.3.2 : V b Phase.International Journal of Research in Engineering & Applied Sciences 25http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905Fig. 1.3.3: V c Phase.Fig. 1.3.1.1: Analysis done on Phase Va by using db4 wavelet upto level 5.CONCLUSIONIn wavelets time taken by them to remove the fault and make the system again working is innano seconds while the time taken by RMS method and other methods for removing fault isin mille seconds. Further Wavelets can also be compared with neural networks even therethey give better results than neural network method to solve the power quality problems. Forthe Analysis daubechies level 5 th is used which gives better resolution. A great advantage ofRMS method is its simplicity, speed of calculation and less requirement of memory, becauseRMS can be stored periodically instead of sample per sample However, its dependency ofwindow length is considered as a disadvantage: one cycle window length will give betterresults 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, harmonicsor noise components; therefore accuracy will depend of the harmonics and noise content. TheRMS voltage value is desirable when harmonics and/or flicker problem is the mostoutstanding issue. The fundamental voltage component characterization approach is basedupon FFT/DFT and wavelet analysis of the voltage waveform. It is qualified to power qualityInternational Journal of Research in Engineering & Applied Sciences 26http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905disturbances featured in remarkable magnitude changing situations, such as sags, swells andinterruptions.The fundamental voltage component is proved as a more appropriatemagnitude characterization approach in most situations. Further by taking the individuallyeach voltage of each phase the analysis is done on it and the results are compared as shownpreviously. Thus we can conclude from the above all that the wavelets used for analysing thesignals gives better results than any other method.REFERENCES1. H. Zhang P. Liu and O.P. Malik “Detection and classification of power disturbances in noisyconditions” IEEE Proc.-Gener. Transm. Distrib, vol.150.no.5, September 20032. SHI Yunhui And RUAN Qiuqi “ Continuous Wavelet Transforms” IEEE Trans. Info.Theory,0-7803-8406-7/04 C 20043. IEEE Standards 1159-1995,”IEEE recommended practice for monitoring electric powerquality”, November 19954. Jan-olov Stromberg “Construction of wavelets” published by MGA tutorials September08,20045. Book on Signal Processing Of Power Quality Disturbances By Math H.J. Bollen and IreneY.H Gu IEEE Press Series on power engineering Mohamed E. EL.-Hawary, Series Editor AJohn Wiley & Sons, Inc., Publication.6. Oscar C. Montero-Hernández and Prasad N.Enjeti „A Fast Detecting Algorithm Suitable ForMitigating Of Numorous Power Quality Disturbances” Vol.41, NO.6, November/December2005.7. N.S.D. Brito, B.A. Souza And F.A.C. Pires “ Daubechies wavelets in Quality of ElectricalPower” 0-7803-5105-3C 1998 IEEE.8. Tao LIN Mineo TSUJI Eiji YAMADA “Wavelet Approach To Power Quality Monitoring” 0-7803-7108-9/0 C 20019. G.T. Heydt and A.W. Galli, “Transient power quality problems analyzed using wavelets”,IEEE Trans. Power Delivery, vol. 12, no. 2, pp. 908-915, Apr. 1997.10. S. Santoso, W. M. Grady, E. J. Powers, J. Lamoree and S. C. Bhatt, “Characterization ofdistribution power quality events with fourier and wavelet transforms”, IEEE Trans. PowerDelivery, vol. 15, no. 1, pp. 247-254, Jan. 2000.11. L.Angrisani, P.Daponte, M.D‟Apuzzo And A.Testa “A Measurement Method Based On TheWavelet Transform For Power Quality Analysis” IEEE Transactions on Power Delivery, Vol.13, No. 4, October 199812. Dogan Gokhan ,Omer Nezih Gerek “ Power Quality Event Detection using Joint 2D-Wavelet Subspaces” IEEE Transactions On Instrum. And Measure. Vol. 14, No.4, 1999.13. Surya Santaso, W. Mack Grady, and Edward J. Powers, “CharacterizationInternational Journal of Research in Engineering & Applied Sciences 27http://www.euroasiapub.org

IJREAS Volume 2, Issue 2 (February 2012) ISSN: 2249-3905of Distribution Power Quality Events with Fourier and Wavelet Transforms”, IEEE Trans. onPower Delivery, Vol. 15, No. 1, 2000.14. L. Angrisani, P. Daponte, and M. D‟Apuzo, “Wavelet network-based detection andclassification of transients,” IEEE Trans. Instrum. and Measure., vol. 50, Oct. 2001.15. C.H. Lin and C.H. Wang, “Adaptive wavelet networks for power quality detection anddiscrimination in a power system”, IEEE Trans. Power Delivery, vol. 21, no. 3, pp. 1106-1113, July 2006.16. A. I. Megahed, A.M. Moussa and A.E. Bayoumy, “Usage of wavelet transform in theprotection of series compensated transmission lines”, IEEE Trans. Power Delivery, vol. 21,no. 3, pp. 1213-1221, July 2006.17. Rieder P., Gotze J. and Nossek J.A., “Multiwavelet Transforms Based on Several ScalingFunctions”, IEEE , pp.393-396,1994.18. O. Poisson, P. Rioual, and M. Meunier, “New signal processing tools applied to power qualityanalysis”, IEEE Transactions on Power Delivery, vol. 14, no. 2, April 1999.19. S.K Pandy and L.Satish “Multiresolution Signal Decomposition:A new tool for fault detectionin power transformers during impulse tests”, IEEE Transactions on powerdelivery,vol.13,no.4,1998.20. A.W.Galli, G.I.Heydt and P.F.Ribeiro,”Exploring the power of wavelet analysis ”IEEEcomputer Applications in power vol.9, no.4, October 199621. E. Styvaktakis, M.H.J. Bollen; and I.Y.H. Gu, “Expert system for classification and analysisof power system events,” IEEE Trans. On Power Delivery, vol. 17, no. 2,pp. 423-428, Apr.2002.International Journal of Research in Engineering & Applied Sciences 28http://www.euroasiapub.org