pantaz d. vadim

8/13/2019 Pantaz d. Vadim

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Electric Power Systems Research 95 (2013) 192199

Contents lists available at SciVerse ScienceDirect

Electric Power Systems Research

journal homepage: www.elsevier .com/ locate /epsr

A new classification for power quality events in distribution systems

Okan Ozgonenela,, Turgay Yalcin a, Irfan Guney b, Unal Kurt c

a Ondokuz MayisUniversity, Electrical& Electronic Eng. Depart., Kurupelit, Samsun, TurkeybAcibademUniversity, Dean of Engineering Faculty, GulsuyuMah. Fevzi Cakmak Cad. No. 1, Maltepe, Istanbul, TurkeycAmasyaUniversity, Technology Faculty, Electrical & Electronic Eng. Depart., Amasya, Turkey

a r t i c l e i n f o

Article history:

Received 19 January 2012

Received in revised form 22 July 2012Accepted 18 September 2012

Available online 13 October 2012

Keywords:

Power quality disturbances

Hilbert Huang Transform

Ensemble Empirical Mode Decomposition

Support Vector Machines

One-against-all method

a b s t r a c t

This paper presents the performance evaluation of support vector machine (SVM) with one against all

(OAA) and different classification methods for power quality monitoring. The first aim of this study is

to investigate EEMD (ensemble empirical mode decomposition) performance and to compare it with

classical EMD (empirical mode decomposition) for feature vector extraction and selection ofpower qual-

ity disturbances. Feature vectors are extracted from the sampled power signals with the Hilbert Huang

Transform (HHT) technique. HHT is a combination ofEEMD and Hilbert transform (HT). The outputs of

HHT are intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA).

Characteristic features are obtained from first IMFs, IF, and IA. The ten featuresi.e., the mean, standard

deviation, singular values, maxima and minimaofboth IF and IA are then calculated. These features are

normalized along with the inputs ofSVM and other classifiers.

Crown Copyright 2012 Published by Elsevier B.V. All rights reserved.

1. Introduction

Power monitoring has taken on an important role in power sys-tems. Power quality (PQ); or, more specifically, a power quality

disturbance, is generally defined as any change in power (voltage,

current, or frequency) that interferes with the normal operation

of electrical equipment. Power quality concerns various kinds of

electric disturbances such as voltage sag/swell, flicker, impulse,

harmonics, DC component of voltage, oscillatory transients, elec-

tromagnetic interference (EMI), and power system outage [1,2].

However, more generally, PQ disturbances can be classified as in

three groups. These are Frequency and amplitude variations and

transient phenomena. Amplitude variations cover voltage vari-

ations under normal operating conditions, excluding faults and

interruptions, short and long interruptions, voltage sags (dips)

and swells, and voltage fluctuations known as flicker. Transient

phenomena cover voltage unbalance and voltage/current wave-

form disturbances [3].

To analyze these disturbances, data areoften available as a form

of sampled time function that is represented by a time series of

amplitudes. When dealing with such data, the Fourier transform

(FT)-based approach is most often used. FT assumes periodicity of

a given signal and loses the time axis account; however, it is not

Corresponding author. Tel.: +90 362 4377204.E-mail addresses:[email protected](O. Ozgonenel),

[email protected](T. Yalcin), [email protected](I. Guney),

[email protected] (U. Kurt).

capable of providing time information about signal disturbances.

Short time Fourier transform (STFT) provides both time and fre-

quency information, but it suffers severely from the Heisenberguncertainty principle [4], causing it to undergo a trade-off

between time resolution and frequency resolution. This is one of

the reasons for the creation of the wavelet transform (or multires-

olution analysis in general), which can give good time resolution

for high-frequency events, and good frequency resolution for low-

frequencyevents, which is thetype of analysis best suitedfor many

real signals. Wavelet transform [5], which is a popular signal anal-

ysis method, offers continuous and discrete wavelet transforms

(CWT and DWT) [6] and wavelet packet transform (WPT) [7,8] for

the feature extraction of signals.

Many classification algorithms have been developed by

researchers for classification of power electrical disturbances. Inte-

grated Fourier linear combiner and fuzzy expert systems [9] were

used for the classification of transient disturbance waveforms

in power system. S-Transform and two dimensional timetime

(TT) transform [10] have been implemented for electrical fault

identification. In [10], transient disturbance waveforms in power

system were investigated by S-transform with together TT trans-

form methods. It is reported that ST with together TT transform

approaches was able to identify disturbed waveforms. An adaptive

neural network approach for the estimation of harmonic distort-

ions and power quality in power networks was also implemented

[11]. A hybrid system to automatically detect, locate, and classify

disturbances affecting power quality in an electrical power system

is also presented [12]. The least absolute value (LAV) state esti-

mation algorithm has been used to measure the flicker voltage

0378-7796/$ see front matter. Crown Copyright 2012 Published by Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.epsr.2012.09.007
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O. Ozgonenel et al./ Electric Power Systems Research95 (2013) 192199 193

Fig. 1. Basic stages of the classification process.

magnitude [13], while the Simulated Annealing (SA) optimiza-tion algorithm has been used for measuring the voltage flicker

magnitude, frequency and the harmonics contents of the volt-

age signal for power quality analysis [14]. An algorithm to detect

the fundamental frequency is proposed. It is based on the chirp-z

transform (CZT) spectral analysis and is able to observe all stan-

dards in force because of its accuracy and working characteristics

[15]. Wavelet multi-resolution decomposition, which combines

frequency domain with time-domain analysis for power disturb-

ance feature extraction, is also proposed [16]; moreover, a wavelet

norm entropy-based effective feature extraction method for power

quality disturbance classification problem has been studied by

[17]. Multi-wavelet based neural networks with learning vector

quantization networks are used for power quality disturbances

as a powerful classifier [18]. Fuzzy ARTMAP, back propagationalgorithm (BPA) and Radial Basis Function (RBF) networks, in

combination with S Transform, Wavelet transform, and Hilbert

Transform (HT) for classifying power faults, have also been used

[19].

On the contrary, unlike many of the aforementioned decompo-

sition methods, EMD is intuitiveand direct, with thebasicfunctions

based on and derived from the data. The assumptions for this

method are: (a) the signal has at least one pair of extrema; (b) the

characteristic time scale is defined by thetime between the succes-

sive extrema; and (c) if there are no extrema, and only inflection

points, then the signal can be differentiated to realize the extrema,

whose IMFs can be extracted. Integration may be employed for

reconstruction.The time between the successive extrema was used

byHuang et al. [20] as it allowed the decomposition of signals thatwere allpositive, allnegative, or both. This implied that thedatadid

not have to have a zero mean. This also allowed a finer resolution

of the oscillatory modes.

Thispaper presents a hybrid powerqualitymonitoring approach

based on EEMD and SVM. EEMD and basic statistical calcula-

tions of its outputs are used for feature extraction. Moreover,

SVM is used for classifying the analyzed power disturbances.

To show the performance of the proposed methods, different

well-known classifiers are tested. Among all the tested classifica-

tion algorithms, the suggested analytical procedures, followed by

SVM-OAA, give a reliable solution for classifying non-stationary

power disturbances. The proposed PQ monitoring approach is

easy to implement and presents reliable solution with high accu-

racy.

2. Materials andmethods

The suggested classification process is shown in Fig. 1.

As seen in Fig. 1, all major power quality issues such as voltage

sag/swell, flicker, harmonics, transients, DC offset, electromag-

netic interference (EMI), and interruptions have been investigated.

Training procedure has been achieved by using synthetically gen-

erated data. Moreover, the supply frequency and flicker frequency

are randomly chosen between 49.5Hz and 50.5Hz and between

8 Hz and 25Hz, respectively. Sampling frequency is set at 25.6kHz

in compliance with the real-time data. This yields a 32003 datamatrix for a three-phase system. The testing procedure was com-

pleted by using real-time data supplied by TUBITAK (The Scientific

and Technological Research Council of Turkey). The data collected

by the National Power Quality Monitors (NPQMC) during an eventbreaking normal operating conditions are transferred by means

of a network infrastructure which is formed in the NPQMC. The

data collected in the center are kept in databases and file systems

after processing them on time and location basis to consider their

distribution in the country.

EEMD is mainly a signal processing technique to extract dis-

tinctive features; namely, IMFs. Feature selection requires a series

of calculations based on statistics such as maxima, minima, sin-

gular value, standard deviation, and mean. Next, the singular

value decomposition (SVD) technique is used for calculating some

additional properties of the features singular values. SVD was cho-

sen simply because the data matrix is not square. It has been

observed that additional properties of the feature vectors increase

the performance of the classification algorithm. Finally, SVM witha one-against-all (OAA) approach is used for classification of PQ

events.

Sections 2.12.4 give the mathematical background of the pro-

posed feature extraction and selection algorithms.

2.1. EMD

EMD is a self-adaptive signal-processing method that has been

successfully applied in non-stationary signal processing. The EMD

method is based upon the local characteristic time scale of the sig-

nal and could decompose the complicated signal function into a

number of IMFs. IMFs reflect the intrinsic and real physical infor-

mation of analyzed the signals. Moreover, the superposition of IMF

components can reconstruct the analyzed signal (Eq. (1)). EMD


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194 O. Ozgonenel et al./ Electric Power Systems Research95 (2013) 192199

Fig. 2. A flowchart of EMD algorithm.

method is a sifting process; to decompose one IMF component

requires many sifting cycles. Thesifting process actually servestwo

purposes: to eliminate riding waves and to make the wave profiles

more symmetrical with respect to zero (Fig. 2).

The EMD algorithm used in this study comprises the following

steps:

(i) Identify all the extrema (maxima and minima) of the signal,x(t).

(ii) Generate the upper and lower envelope by the cubic spline

interpolation of the extrema points developed in step (i).

(iii) Calculate the mean function of the upper and lower envelope,

m(t).

(iv) Calculate the difference signald(t) =x(t)m(t).( v) If d(t) becomes a zero-mean process, then the iteration stop

and d(t) is an IMF1, named c1(t); otherwise, go to step (i) and

replacex(t) withd(t).

(vi) Calculate the residue signal r(t) =x(t) c1(t).(vii) Repeat the procedure from steps (i) to (vi) to obtain IMF2,

named c2(t). To obtaincn(t), continue steps (i)(vi) aftern iter-

ations. The process is stopped when the final residual signal

r(t) is obtained as a monotonic function.

At the end of the procedure, we have a residue r(t) and a collec-

tion ofn IMF, named from c1(t) to cn(t). Now, the original signal can

be represented as:

x(t) =n

i=1

ci(t) + r(t) (1)

Often, r(t) is regarded as cn+1(t) [20].

The sifting process should be applied with care, for carrying the

process to an extreme could make the resulting IMF a pure fre-

quency modulated signal of constant amplitude. To guarantee that

theIMF componentsmaintain enough physical senseof bothampli-

tude and frequency modulations, a criterion should be defined for

the sifting process to stop. This can be accomplished by limiting

size of the standard deviation (SD), it is similar to the Cauchy con-

vergence test, computed from the two consecutive sifting results

as

SD =T

t=0

di1(t) di(t)2d2

i1(t)

(2)

where di(t) is proto (candidate)-mode function.

A typical value for SD can be set between 0.2 and 0.3. As a com-

parison the two Fourier spectra, computed by shifting only five out

of 1024 points from the same data, can have an equivalent SD of

0.20.3calculated point-by-point. Therefore,SD valueof 0.20.3for

the sifting procedure is a very rigorous limitation for the difference

between siftings [20].

In Fig.2, Extrema


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Fig. 3. (a) Feature vectors (IMFs)for a voltage sagsignal with EMD. (b) Feature vectors (IMFs)for a voltage sagsignal with EEMD.

An equivalent expression gives the correlation coefficient as the

mean of the products of the standard scores. Based on a sample

of paired data (Xi, Yi), the sample Pearson correlation coefficient is

defined in Eq. (5).

R = 1n 1

ni=1

Xi X

X

Yi Y

Y

(5)

where (Xi X)/X, X and Xare the standard score, sample mean,and sample standard deviation ofX. Table 1 shows theR values for

EMD and EEMD.

Table 1 also proves that EEMD has better resolution for the ana-

lyzed PQ events.

2.4. Hilbert-Huang TransformationMethod (HHT)

HHT represents the original signalX(t) into the time frequency

domain by combining the empirical mode decomposition with the

Hilbert transform. The advantage of using HHT for feature extrac-

tion procedure can be summarized in Table 2.

EMD can decompose signal into a finite number of n IMFs

(Cj, j = 1,2,3...,n), which extract the energy associated with variousintrinsic time scales and residual rn; this step is called the sifting

Table 1

Relation with first IMFs and current power signal.

Cross-Correlation EMD EEMD

IMFhealthy state 0.9762 1.0000

IMFvoltage sag 0.8819 0.9081

IMFvoltage swell 0.9300 0.9685

IMFvoltage flicker 0.9700 0.9950

IMFharmonics (3, 5, 7) 0.7855 0.7861

IMFtransient 0.5685 0.6104

IMFdc component 0.9244 0.9357

IMFEMI 0.5334 0.6429

IMFoutage 0.9313 0.9856


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Table 2

Comparison of frequently used mathematical methods for feature extraction [20,21].

Fourier STFT Wavelet HHT

Basis Non-adaptive Non-adaptive Non-adaptive Adaptive

Frequency Convolution: Global Convolution: Regional Convolution: Regional Differentiation: Local

Presentation Energy-frequency Energy-time-frequency Energy-time-frequency Energy-time-frequency

Non-linear No No No Yes

Non-stationary No No Yes Yes

Feature Extraction No Discrete: No Continuous: Yes Discrete: No Continuous: Yes Yes

Theoretical base Theory complete Theory complete Theory complete empirical

process described as in Eq. (6). IMFs, as a class of functions, satisfy

two definitions. In the whole data set, the number of extrema and

the number of zero-crossings must be either equal or differ at most

byone.At any point, the mean value of the envelope defined by the

local maxima and minima is zero.

TheHilbert transform is then applied to each IMF component Cj:

vj(t) =1

+

cj()

t d (6)

where cj(t) and vj(t) are real part and imaginary part of an analytic

signalzj(t):

zj(t) = cj(t)+jvj(t)zj(t) =Aj(t) exp(jwj(t))

(7)

With amplitude, (IA), andphase defined by the expressions:

IA(t) =

cj(t)2 + vj(t)2

j(t) = arctan

vj(t)

cj(t)

(8)Therefore, the instantaneous frequency (IF), was given by:

IF(t) = dj(t)dt

(9)

Thus, the original data can be expressed in the following form:

x(t) =n

j=1

Aj(t) exp

i

wj(t) dt

(10)

Figs. 4 and 5 show IA and IF curves for healthy and voltage sag

signals. Fig. 6 shows the IA values for all analyzed PQ events.

3. Singular valuedecomposition (SVD)

SVD is used for calculating singular values and increases the

accuracy of the classification algorithm. Singular values give more

Fig. 4. Instantaneous amplitude (IA) for healthy and voltage sag signal.

information for identification of the system. SVD is a part ofhierar-

chical classification on multiple feature groups in this study.

For any real mn matrixA, there exist orthogonal matrices

U= [u1, u2,...,um] Rmm,

V= [v1, v2,...,vm] Rnn,(11)

such that:

A = UVT (12)

where = diag(1, 2,...,min(m,n))) Rmn and 1 2 ...

min(m,n)0.

The iis the ith singular value ofA in non-increasing order andthe vectors uiand viare the ith left singular vector and the ith right

singular vector ofA for i=min(m,n), respectively [22]. The singular

values of matrix A are unique, while the singular vectors corre-

sponding to distinct singular values are uniquely determined up to

the sign.

Fig. 5. Instantaneous frequency (IF) for healthy and voltage sag signal.

Fig. 6. IA corresponding to first IMF for all disturbances.


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Fig. 7. Singular values for training data set.

Fig. 7 shows singular values for analyzed power signals. Fig. 7only shows 3 sets of data over 90 training data sets. In Fig. 7, SV1

represents singular values for the healthy signal, SV2 for voltage

sag, SV3 for voltage swell, SV4 for flicker, SV5 for harmonics, SV6

for transients, SV7 for DC component, SV8 for EMI and finally SV9

for the voltage interruption signal.

4. Support vectormachines (SVMs)

SVMs are a class of learning machines that find optimal hyper-

planes among different classes of input data or training data in a

high dimensional feature space F, and new test data can be clas-

sified using the individual hyper-planes. The optimal hyper-plane,

found during a trainingphase, makes the smallest number of train-

ing errors [23]. Fig. 8 illustrates an optimal hyper-plane for twoclasses of training data.

Let {xi,yi}, i=1,2,. . .,Nbe N training data vectors xi with classlabel yi. Given an input vectorx, an SVM constructs a classifier of

the form:

f(x) = sign

Ni=1

aiyiK(xi, x)+ b

(13)

where {ai} are non-negative Lagrange multipliers, each of which

corresponds to a training data, b is a bias constant, and K(.,.)

is a kernel satisfying the conditions ofMercers theorem [24,25].

Fig. 8. Optimal hyperplane.

Frequently used kernel functions are the polynomial kernelK(xi, xj) = (xi.xj + 1)d and Gaussian Radial Basis Function (RBF),K(xi, xj) = e|xixj|

2/22 .

The problem of finding the optimal hyperplane is specified by

the following quadratic programming problem:

Minimizing:

W(a) = N

i=1

ai +1

2

Ni=1

Nj=1

aiajyiyjK(xi, xj) (14)

subject to:

Ni=1

aiyi= 0 (15)

0 ai C, i = 1, 2,...,N

The above quadratic programming problem can be solved with

traditional optimization techniques. The vectors for which ai > 0

after optimization are called support vectors.

4.1. One-against-allmethod

For a k-class problem, the one-against-all method constructs k

SVM models. The ith SVM is trained with all of the training exam-

plesinthe ith class with positive labelsand theothers with negative

labels. The final output of the one-against-all method is the class

that corresponds to the SVM with the highest output value. Given

a set of training examples D{(x1, y1), (x2, y2),..., (xN, yN)}, wherethe ith samplexi Rn andyj {1,...,k} is the class ofxjthe ith SVMsolves the following optimization problem [2628] (where c> 0 is

a regularization parameter for the tradeoff between model com-

plexity and training error, and imeasuresthe (absolute)difference

betweenf(x) = (wi)T(x)+ bi andyi.):

min1

2(wi)

Twi + c

N

i=1

ij (16)


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Table 3

Comparisons of the performances of the one-against-all (OAA) and the one-against-one (OAO) SVM classifiers.

Total numberof training/testingsegments Classification (SVM) One Against All (OAA)OneAgainst One (OAO)

90/25 HS VSG VSW VF VH VTR VDC EMI VI

HS 32 0 0 0 0 0 0 0 0

VSG 1 22 0 0 0 0 0 0 0

VSW 0 0 22 1 0 0 0 0 0

VF 0 0 0 32 0 0 0 0 0

VH 0 0 0 0 33 0 0 0 0

VTR 0 0 0 0 0 43 0 1 0

VDC 0 0 0 0 0 0 33 0 0

EMI 0 0 0 0 0 1 0 32 0

VI 0 0 0 0 0 0 0 0 22

HS:healthy state,VSG: voltagesag,VSW:voltageswell,VF:voltageflicker, VH:voltage harmonics, VTR: voltagetransients, VDC:DCvoltage,EMI:electro magneticinterference.

Table 4

Number of measurement point [2931].

Number of measurement points

Voltage level Load type

Heavy Industry Industry + urban Urban only Total

NB NF NB NF NB NF NB NF

33 kV 8 16 17 28 14 16 39 60

154 kV 3 5 10 13 9 9 22 27

380 kV 1 1 4 7 0 0 5 8

Total 12 22 31 48 23 25 66 95

NB: numberof buses, NF: number of feeders.

subject to

(wi)T(xj) + bi 1 ij, yj= i

(wi)T(xj) + bi > 1 + ij, yj /= i

ji 0;j = 1,...,N

(17)

The decision function of the ithSVM is

f(x)=

(wi)T(x)+

bi (18)

A point x is in the class that corresponds to the largest value of

the decision functions: the class of

x = argmaxi=1,...,k(wi)T(x) + bi (19)A comparative study is carried out in the next section that com-

pares the performances of the OAO (one-against-one) and OAA

(one-against-all) decomposition methods in training the proposed

multiclass SVMs. The results of the application of the proposed

algorithm to the data set are presented in Table 3.

Diagonal elements represent the correctly classified PQ

disturbances while non-diagonal elements represent incorrect

classification results (Table 3).

5. Experimental results anddiscussions

To evaluate the performance of the proposedpower quality clas-

sification algorithm, a total of 115 PQ disturbance data were used.

The PQ signals in each class were divided into 90 training sets and

25 test sets. The polynomial kernel is used as the kernel function

and the parameter d is chosen as 2 in the SVM classifier. The exact

number of the training and testing segments for each PQ event

is shown in Table 2. Testing data were obtained from TUBITAK.

This is an ongoing project and is devoted to taking the nationwide

power quality figures of the Turkish Electricity Transmission Sys-

temthroughfield measurements carried outby application-specific

mobile monitoring equipment. In this project, PQ measurements

are being carried out according to IEC 610004-30, except for a

continuous seven-day sampling rate in the selected locations. In

the transmission system, most of the bus bars have more than one

incoming and outgoing feeder. Therefore, the number of measure-

ment points can be taken as the number of feeders for currents and

as the number of bus bars for voltages. The classification of these

points as heavy industry, urban + industrial, and urban sites are

given in Table 4, with respect to voltage level. It is noted that there

are more than 95 incoming and outgoing feeders at 33 measured

transformer substations [2931]. The real-time data are acquired

in 3 s, which corresponds to 150 periods.

The most accurate multi classifiers (OAA) for each PQ datasetare indicated by red font. As seen from Table 2, the OAA method

(Error = 0 %, Precision = 100%) is superior to the OAO method

(Error= 16%, Precision = 84%). Therefore, the OAA decomposition

methodis chosen for the multiclass SVM in this study.According to

the results of the test data, the effectiveness of the proposed SVM

algorithmis madesuitableby increasing the RBF kernel parameters

standard deviation (sigma0.1 to 1).The polynomial kernel is worse

than the RBF kernel due to the CPU time. It has been observed that

additional properties of the feature vectors such as singular values

increase the performance of classification algorithm. The perfor-

mance of SVM algorithm with OAA and OAO is calculated as 100%

and 84%, respectively. Nevertheless, the performance of SVM algo-

rithm decreases dramatically without using singular values of IF

and IA values and is calculated as 80% with OAA and 56% with OAO,respectively. Table 5 gives a comparison of the different classifiers

on the training and testing data.

Table 5

Comparison of performances of the different classifiers.

Classifier Error Accuracy Time (s)

SVM-Linear 0.4000 0.6000 2.136

SVM-Poly d= 2 0.0000 1.000 12.711

SVM-RBF sigma = 0.1 0.2800 0.7200 8.208

SVM-RBF sigma = 1 0.0000 1.000 10.686

Gauss Mixture Model 0.2555 0.7444 1.493

AdaBoost + Naive Bayes 0.0400 0.9600 3.529

Bagging + Naive Bayes 0.1600 0.8400 2.577


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6. Conclusion

In this paper, a different PQ monitoring approach based on

EEMD, HHT, andSVM wasproposed. EEMD wasused forsignal con-

ditioning technique and two distinctive features, IA and IF, were

obtained by HHT. Then a series of statistical calculations was per-

formed to form feature matrix. The algorithm is found capable of

classifying many PQ disturbances with a high accuracy.

SVM with radial basis (sigma= 1) kernel function was used for

the decision-making unit and presented high accuracy among the

other classifiers. The proposed algorithm was applied to two sets

of data, one for generated data on a computer and the other for

the data obtained from TUBITAK. Practical applications showed

that the proposed hybrid algorithm can be used in power quality

monitoring software.

The analysis and results presented in this work clearly show the

potentialcapability of the suggested hybrid algorithmin classifying

the distorted PQ waveforms.

Acknowledgement

The authors wouldlike to thankTUBITAKfor supplyingreal-time

data.

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