Classification of Power Quality events using S-Transform by Visual Inspection -A Review

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 06 | December 2015 ISSN (online): 2349-784X

Classification of Power Quality Events using STransform by Visual Inspection -A Review Swati R. Satao P. G. Student Department of Electrical Engineering Shri Sant Gajanan Maharaj College of Engineering, Shegaon(M.S.)

Ravishankar S. Kankale Assistant Professor Department of Electrical Engineering Shri Sant Gajanan Maharaj College of Engineering, Shegaon(M.S.)

Abstract This paper presents an overview for feature extraction method using S transform from a disturbance waveform which is a new useful tool for classification of power quality problems in electrical power systems. According to researchers, the excellent timefrequency resolution characteristics of the S-transform can be effectively used for the analysis of power quality (PQ) disturbances under noisy condition and has the ability to detect the disturbance correctly. Features extracted using the S-transform can be applied to an artificial NN for automatic classification of the PQ disturbances. he simulation results shown states that the S-transform can effectively detect and classify different power quality disturbances by visual inspection also.

Keywords: Power quality, PSCAD/EMTDC simulation, Voltage sag, Voltage swell, Voltage interruption ________________________________________________________________________________________________________

I. INTRODUCTION Nowadays to maintain a good power quality is a big challenge to the utilities. The adverse effects of poor power quality are well discussed in many research papers. In general, poor power quality may result into increased power losses, abnormal and undesirable behavior of equipments, interference with nearby communication lines, and so. The widespread use of power electronic based systems has further put the burden on power system by generating harmonics in voltages and currents along with increased reactive current[1]-[2], [5]–[7]. Due to the wide spread use of power electronics in every place in the power industry, the power supplied to the customers are now distorted in either the voltage signal or current signal or both of them. This distortion has a great effect on the sensitive equipments and may cause interruption to such equipments that result in very expensive consequence.

In this review paper unique features that characterizes power quality events and methodology in order to extract them from recorded waveforms is shown. Power quality events are characterized by their maximum amplitudes, crest voltages, RMS, frequency, statistics of wavelet transform coefficients, instantaneous voltage drops, number of notches, energy,duration of transients, etc. These characteristics are different for each power quality event, thus they are unique in identifying the most important mathematical transforms in power engineering world.

II. S TRANSFORM A new tool for monitoring power quality problems is introduced by scientist R. G. Stockwell in 1996. Wavelet exhibits its notable capabilities for detection and localizationof the disturbances [13]–[14]. However, its capabilities are often significantly degraded in real practice under noisy environment.

On the other hand, S-Transform has the ability to detect the disturbance correctly in the presence of noise. This ability of STransform attracts the researchers for the detectionand classification of PQ disturbances. The S transform is useful in detecting and extracting disturbance features of various types of electric power quality disturbances. The ST is an invertible time–frequency spectral localization technique that combines elements of WT and STFT. The ST uses an analysis window whose width is decreasing with frequency providing a frequency-dependent resolution. The ST is a continuous wavelet transform with a phase correction. It produces a constant relative bandwidth analysis like wavelets, although maintains a direct link with Fourier spectrum.

The ST has an advantage in that it provides multiresolution analysis while retaining the absolute phase of each frequency. This has led to its application for detection and interpretation of non-stationary signals. Further, the ST provides frequency contours which clearly localize the signals at a higher noise level.

Advantages over Wavelet transform Major advantage over WT of ST is to avoid the requirement of testing various families of wavelets to identify the best one for a better classification. It is not required to choose a suitable mother wavelet for accurate results here. The superiority of the ST over wavelet is that it can classify four types of PQ disturbances by visualizing the time-frequency contour of ST. The output of the ST is an mxn matrix, whose rows pertain to frequency and columns indicate time. Each column thus represents the “local spectrum” for that point in time.To construct an effective classifier, it is essential to choose a suitable feature vector that

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Classification of Power Quality Events using S-Transform by Visual Inspection -A Review (IJSTE/ Volume 2 / Issue 06 / 016)

can indicate and recognize the main characteristics of signal. For this purpose, the statistical features based on ST, the reduction of data size as well indicating and recognizing the main characteristics of signal without losing its distinguishing characteristics is extracted. In addition, the effectiveness of features is increased by scatter plots.

Output results are then given to MATLAB program for S transform. The computation of the ST is efficiently implemented using the convolution theorem and FFT. The S-Transform of a discrete time series, is given by N−1

i2Đ&#x;mj n m+n S [jT, ] = ∑ P [ ] G(m, n)[e( N ) ] NT NT −(

2Đ&#x;2 đ?‘š2 đ?›˝2

(1)

m=0

)

�2 where G(m, n)=� is the Guassian function and where j, m =0,1,2,‌.N-1 N=total no. of samples, β=1/b, n≠0; n=1,2,3,4‌N-1.

A typical value of b varies from 0.333 to 5, which gives us different resolutions like for low frequencies high value of b is chosen and for high frequencies a low value of b is chosen for suitable resolutions.

The following steps are used for the computation of ST. 1) Denote, n/NT, m/NT, kT and jT as n,m,k, j respectively, for the evaluation of ST. 2) Compute the DFT of the signal p(K) using FFT software routine and shift spectrum P(m) to P (m+n).

3) Compute the Gaussian window function for the required frequency n.

4) Compute the inverse Fourier transform of the product of DFT and Gaussian window function to give the ST matrix.

III. SIMULATION AND RESULTS According to many researchers S transform is a tool which generates ST matrix in which every element is a complex value [8][11]. Plots drawn from S matrix using MATLAB tool gives accurate results by visual inspection also. Use of a classifier may be done here to detect other PQ events also. In this section we have considered three cases voltage sag, voltage swell and volage interruption based on computer generated MATLAB code .Frequency is normalized with respect to sampling frequency.

Fig. I(a) shows the normal voltage signal without any disturbanceas a function of time. Figs. II-IV (a) represents the plot of the sampling disturbance signal. Figs. II-IV (b) is called time–frequency contour. Fig.II (a) shows the signal with voltage sag. It can be seen from the time–frequency contour of fig.II(b) that ST has a magnitude reduction during the disturbance similar to that in case og voltage sag in time domain clearly detecting, localizing and classifying the disturbance. Similarly voltage swell fig III and voltage interruption fig IV can also be classified visually.

Fig. 1: Normal Voltage

Fig. 2: Voltage Sag

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Classification of Power Quality Events using S-Transform by Visual Inspection -A Review (IJSTE/ Volume 2 / Issue 06 / 016)

Fig. 3: Voltage Swell

Fig. 4: Voltage Interruption

IV. CONCLUSION It is clear from the above results that the proposed S transform is capable to classify the PQ disturbances by visual inspection also which reduces the time of further computation like use of best classifier. [11]-[12] The most important advantage of the proposed method is the reduction of data size as well indicating and recognizing the main characteristics of signal without losing its distinguishing characteristics.[9]-[11] Furthermore, it can reduce memory space, shorten preprocessing needs, the network size and increase computation speed for the classification of PQ disturbances. The analyses and the results presented from the research paper clearly reveal the potential capability of the proposed method in classifying the distorted PQ signals accurately.

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