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Electric Power Systems Research 79 (2009) 569–575

Contents lists available at ScienceDirect

Electric Power Systems Research

journa l homepage: www.e lsev ier .com/ locate /epsr

ower quality disturbance classification utilizing S-transformnd binary feature matrix method

hai Nguyen, Yuan Liao ∗

epartment of Electrical and Computer Engineering, University of Kentucky, 453 F. Paul Anderson Tower, Lexington, KY 40506, USA

r t i c l e i n f o

rticle history:eceived 18 August 2007

a b s t r a c t

This paper presents new features and a novel decision-making system for automated classification ofpower quality disturbances. The most common types of disturbances including flickers, harmonics,

eceived in revised form 14 August 2008ccepted 18 August 2008vailable online 26 September 2008

eywords:inary feature matrix-transform

impulses, notches, outages, sags, swells, and switching transients are studied. Disturbances consistingof both sag and harmonic, or both swell and harmonic are also considered. It is assumed that the ana-lyzed waveforms are available in sampled form. The signal processing techniques utilized to extract thedistinctive features of the waveforms are Fourier and S-transform. A new method based on binary featurematrix is designed for making a decision regarding the disturbance type. Evaluation studies for verifyingthe accuracy of the method are presented.

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ower quality disturbance classification

. Introduction

With the increasing use of sensitive electronic devices suchs computers, electronic micro-controllers, variable speed drives,tc., the need for improved power quality (PQ) is also increas-ng, because poor PQ could cause failure or malfunction of certainevices. The study in [1] shows that, just in the U.S. alone, thealfunction of equipment due to voltage disturbances costs the

ndustry sector more than 20 billion dollars each year. To ensurehe quality of power and also help identify PQ problems, a sig-ificant number of power quality meters have been deployed inlectric power systems, resulting in a large number of disturbanceaveforms being captured. Manual analysis of PQ data is very

ime-consuming and also requires special expertise. An automatedystem for classifying the power quality disturbances will facilitatehe analysis process.

In recent years various approaches for automated classifica-ion of PQ disturbances have been proposed [2–10]. For featurextraction, the wavelet transform and S-transform have been madese of. The wavelet transform is capable of acquiring the timend frequency information simultaneously and therefore yield-

ng a time–frequency representation of the signal. The authors in3–6] have utilized the wavelet transform to extract the uniqueeatures for fast-changing signals such as switching transients andmpulses. However, accuracy of the features extracted by using the

∗ Corresponding author. Tel.: +1 859 257 6064; fax: +1 859 257 3092.E-mail address: yliao@engr.uky.edu (Y. Liao).

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© 2008 Elsevier B.V. All rights reserved.

avelet transform is rather susceptible to the level of noise [9,10]. Tomprove this condition another methodology that utilizes either thephase correction” of the wavelet transform or the variable windowhort-time Fourier transform known as S-transform was devel-ped. The S-transform is capable of obtaining reasonably accuratemplitude and phase spectrum of the analyzed signals even at theresence of high level of noise [9–11]. Refs. [2,9,10] have utilized-transform to extract features such as amplitude factor, frequencyactor, etc., from the PQ disturbance signals.

Refs. [2–10,12–16,19–25] have presented a variety of methodsor the decision-making. The neural network approaches have beenroposed in [3,6,14]. The neural network described in [3] consistsf an input layer (12 nodes), a hidden layer (8 nodes), and an outputayer (5 nodes). The authors in [12,13] have utilized the N-1 Sup-ort Vector Machines (SVMs) to classify the disturbance types ofQ; four SVMs classifiers are adopted to classify five different dis-urbances. Rule-based expert system and fuzzy logic have also beenmployed for decision-making step in the process of classifying PQisturbance types [7,8,19–25].

In this paper, we explore new methods for extracting moreistinctive and pertinent features for different types of PQ distur-ances and also investigate new techniques for decision-making.

We will take advantage of the signal processing techniques,uch as the windowed discrete Fourier transform (WDFT) and S-

ransform, to extract the unique features of the waveforms. A newinary feature matrix based method is designed for making a deci-ion regarding the disturbance type. The paper is organized asollows. The proposed feature extraction techniques are describedrst. Then the binary feature matrix based approach is illustrated.

5 Systems Research 79 (2009) 569–575

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70 T. Nguyen, Y. Liao / Electric Power

he testing results for evaluating the new method are presentedext, followed by the conclusion.

. Proposed feature extraction method

Sections 2.1 and 2.2 briefly review the windowed discreteourier transform and discrete S-transform. Section 2.3 elucidateshe proposed features utilizing these techniques.

.1. Windowed discrete Fourier transform

The windowed discrete Fourier transform is adopted in our worknd is written as [17]

n[k] =N−1∑i=0

v[i+ (n− 1) × N]e−j(2�ki/N) (1)

here Vn[k] is a complex value denoting the discrete frequencyontent for the samples contained in the nth data window of thenalyzed signal; v is the analyzed signal; k = 0,1, . . ., N − 1; N is theumber of samples in one data window; n is the index of the dataindow; j is the imaginary unit.

.2. Discrete S-transform

The discrete S-transform is defined as [11]

[p

QT, iT

]=Q−1∑m=0

U[m+ pQT

]e−(2�2m2/p2) e(j2�mi/Q ), p /= 0 (2)

nd (2) becomes

1Q

Q−1∑m=0

v(m

QT

)for p = 0. (3)

here U is the Fourier transform of the analyzed signal; i = 0, 1, . . .,− 1; Q is total number of samples; T is the sampling time interval.

The multi-resolution S-transform output is a complex matrix, inhich the rows are the frequencies and the columns are the time

eries [18]; this gives precisely the time of a certain frequency. Eacholumn thus represents the local spectrum for that point in time,nd each row displays the S-transform amplitude with time varyingrom 0 to Q − 1 in the same frequency.

Let be the output matrix of the S-transform of the analyzedignal, and � be the output matrix of the S-transform of a normalignal. We have

i1i2 = S[i1QT, i2T

](4)

here i1i2 stands for the i1-th frequency of the time i2. � can bexpressed similarly.

.3. Proposed features

This section describes the extracted features based on theethods illustrated in Sections 2.1 and 2.2 for PQ disturbance clas-

ification.Tens of thousands of waveforms of common types of PQ distur-

ances including flickers, harmonics, impulses, notches, outages,ags, swells, switching transients, and combination of disturbancesuch as sag and harmonic, and swell and harmonic, have been sim-lated and studied [2,19]. The following six distinguishing featuresertinent to different types of PQ disturbances have been identi-

Oa0

a

Fig. 1. A transient signal and its features.

ed. The formulae for computing these features are presented asollows:

n =√

2abs(Vn[1])N

(5)

n = angle(Vn[1]) − angle(V1[1]) (6)

= STHD(THD1,THD2, . . . ,THDL) (7)

n = root(vsmiss) (8)

= std(max(abs( ))) (9)

= P1 +M1 − P2 −M2 (10)

here Vn is the fundamental frequency of the signal; �n is the phasengle shift; On is the oscillation number of the missing voltage ofach cycle; Z is the standard deviation of the maximum absolutealue of each column in the S-transform output matrix; STHD(•)eturns the total number of cycles in a waveform that have totalarmonic distortion (THD) greater than the threshold value, chosens 0.03 in our work; L is the total number of cycles of the analyzedignal;

miss[i] = v[i] − 2N

× abs(V1[1])

×cos{

angle(V1[1]) + 2�(i− 1)N

}(11)

smiss is defined as an array composed of vmiss[i], i = 0, 1, . . .,− 1; abs(•) yields the absolute value of the argument; std(•)

ields the standard deviation of the argument; angle(•) returnshe phase angle of the input argument; root(•) returns the num-er of zero-crossings of the argument; P1 is max(max(abs( )));1 is min(max(abs( t))); P2 is max(max(abs(�))); M2 isin(max(abs(�t))); t and �t represent the transposes of and; max(•) and min(•) return the maximum and minimum values of

he argument, respectively; For a matrix argument, the operations performed on each column, and the functions will return rowectors. For a vector argument, they will return a scalar value.

Fig. 1 illustrates a transient signal and it features. Note that thehase angle shift is in degrees. It can be seen that the missing volt-ge has a considerable number of oscillations during the transient.

ther main characteristics include little change in phase angle shiftnd in the fundamental voltage. Also, the values of ˇ, Z and H are.0095, 0.0026, and 1.0, respectively.

A sag signal and its features are depicted in Fig. 2. Chief char-cteristics include large reduction in the fundamental voltage and

T. Nguyen, Y. Liao / Electric Power Syste

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ignificant phase angle shift. The values of ˇ, Z and H are −0.0156,.4641 and 0, respectively.

For classification purposes as illustrated in the next section,hese features are categorized into different classes according toheir values.ˇ is divided into three classes: ˇcl1 if ˇ < −0.00005; ˇcl2 if

0.00005 ≤ˇ≤ 0.00005; ˇcl3 if ˇ > 0.00005.For phase angle shift, we separate it into two classes: �cl1 if

ax(�cycles) < 5◦; �cl2 if max(�cycles) ≥ 5◦. Where, �cycles = �1, �2, . . .,L.

For oscillation number of missing voltage, we group it intowo classes: Ocl1 if max(Ocycles) ≤ 2; Ocl2 if max(Ocycles) ≥ 2. Where,cycles = [O1, O2, . . ., OL].

H is classified as: Hcl1 if H < 6; Hcl2 if H ≥ 6.For Z, we divide them into three classes: Zcl1 if Z < 0.0000001;

cl2 if 0.0000001 ≤ Z < 0.00001; Zcl3 if Z ≥ 0.00001.Lastly, for the fundamental voltage of the signal, we sep-

rate them into five classes: Vcl1 if max(Vcycles) > 1.1 p.u.; Vcl2f 0.1 ≤ min(Vcycles) < 0.9 p.u.; Vcl3 if min(Vcycles) < 0.1 p.u.; Vcl4 if.9 ≤ Vcycles ≤ 1.1 and Vsum is greater than or equal to a thresholdalue, say 4 in the proposed method; Vcl5 if Vcycles is within theroximity of 1 p.u. Where, Vcycles = [V1, V2, . . ., VL], and Vsum equalso the sum of the number of times that the difference betweenhe fundamental voltage of one cycle and the fundamental voltagef the next cycle has an absolute value that exceeds a thresholdalue.

The proposed feature extraction method is the combination ofoth windowed Fourier transform and S-transform. Thus, it has thedvantage of efficiently capturing the features of stationary sig-als. The S-transform would have the advantage over the windowedourier transform when it is applied to fast-changing signals. Thus,y combining the two methods, the proposed method has the abil-ty to classify a wide range of PQ disturbances. This may bringbout more benefits than if only one of the technique is utilized5,28,29].

. The proposed binary features matrices for PQisturbance classification

This section describes the proposed binary feature matrix sys-em for decision-making.

As described in Section 2, we have grouped each feature into dif-erent classes. For example, after analyzing a particular waveforme obtain the features such as ˇcl1, �cl2, Ocl1, Hcl1, Zcl3, and Vcl2.

vid

a

ms Research 79 (2009) 569–575 571

his set of features is a unique set of outputs that indicate the sagisturbance, which can be expressed as

f ˇcl1 and �cl2 and Ocl1 and Hcl1 and Zcl3 and Vcl2

then sag (12)

To be able to correctly classify the 10 disturbance types andormal waveforms, 27 rules have been designed as follows.

R1: if ˇcl2 and �cl1 and Ocl1 and Hcl1 and Zcl1 and Vcl5 thenNormalR2: if ˇcl1 and �cl2 and Ocl1 and Hcl1 and Zcl3 and Vcl2 then SagR3: if ˇcl1 and �cl1 and Ocl1 and Hcl1 and Zcl3 and Vcl2 then SagR4: if ˇcl3 and �cl2 and Ocl1 and Hcl1 and Zcl3 and Vcl1 then SwellR5: if ˇcl3 and �cl1 and Ocl1 and Hcl1 and Zcl3 and Vcl1 then SwellR6: if ˇcl1 and �cl2 and Ocl1 and Hcl1 and Zcl3 and Vcl3 thenOutageR7: if ˇcl1 and �cl1 and Ocl1 and Hcl1 and Zcl3 and Vcl3 thenOutageR8: ifˇcl2 and �cl1 and Ocl1 and Hcl2 and Zcl1 and Vcl5 then HarmonicR9: if ˇcl2 and �cl1 and Ocl1 and Hcl2 and Zcl2 and Vcl5 then NotchR10: if ˇcl2 and �cl1 and Ocl1 and Hcl2 and Zcl3 and Vcl5 then NotchR11: if ˇcl1 and �cl1 and Ocl1 and Hcl2 and Zcl2 and Vcl5 then NotchR12: ˇcl1 and �cl1 and Ocl1 and Hcl2 and Zcl3 and Vcl5 then NotchR13: if ˇcl3 and �cl1 and Ocl1 and Hcl2 and Zcl2 and Vcl5 then NotchR14: if ˇcl3 and �cl1 and Ocl1 and Hcl2 and Zcl3 and Vcl5 then NotchR15: if ˇcl2 and �cl1 and Ocl2 and Hcl1 and Zcl2 and Vcl5 then Tran-sientR16: if ˇcl2 and �cl1 and Ocl2 and Hcl1 and Zcl1 and Vcl5 then Tran-sientR17: ifˇcl3 and�cl1 and Ocl2 and Hcl1 and Zcl2 and Vcl5 then TransientR18: if ˇcl3 and �cl1 and Ocl2 and Hcl1 and Zcl1 and Vcl5 then Tran-sientR19: if ˇcl3 and �cl1 and Ocl2 and Hcl1 and Zcl3 and Vcl5 then Tran-sientR20: if ˇcl2 and �cl1 and Ocl2 and Hcl1 and Zcl3 and Vcl5 then Tran-sientR21: ifˇcl3 and �cl1 and Ocl1 and Hcl1 and Zcl3 and Vcl5 then ImpulseR22: ifˇcl3 and �cl1 and Ocl1 and Hcl1 and Zcl2 and Vcl5 then ImpulseR23: if ˇcl1 and �cl2 and Ocl1 and Hcl2 and Zcl3 and Vcl2 then Sagand HarmonicR24: ifˇcl1 and �cl1 and Ocl1 and Hcl2 and Zcl3 and Vcl2 then Sag andHarmonicR25: if ˇcl3 and �cl2 and Ocl1 and Hcl2 and Zcl3 and Vcl1 then Swelland HarmonicR26: if ˇcl3 and �cl1 and Ocl1 and Hcl2 and Zcl3 and Vcl1 then Swelland HarmonicR27: if V is Vcl4 then Flicker

More intuitively, the above 27 rules can also be shown as inable 1, where a value of 1 stands for TRUE, 0 for FALSE, DC foron’t Care. Also, a type of “sag and harmonic” indicates that a signal

ncludes two disturbances at the same time, i.e., sag and harmonic,nd likewise for swell and harmonic.

Given a waveform, we can first calculate its six features, decidehich class the features belong to, and then compare the obtained

eature classes with the designed rules. When a match is identified,he type of disturbance is determined accordingly. If the rule set is

ery large, then the matching process could be time-consuming. Tomprove the efficiency of rule matching, the following technique iseveloped.

Let us assign a different eight-bit binary number to each classs follows.

572 T. Nguyen, Y. Liao / Electric Power Systems Research 79 (2009) 569–575

Table 1Representation of the rule set

ˇcl1 ˇcl2 ˇcl3 �cl1 �cl2 Ocl1 Ocl2 Hcl1 Hcl2 Zcl1 Zcl2 Zcl3 Vcl1 Vcl2 Vcl3 Vcl4 Vcl5 Dis. type

0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 Normal1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 Sag1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 Sag0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 0 0 Swell0 0 1 1 0 1 0 1 0 0 0 1 1 0 0 0 0 Swell1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0 Outage1 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 Outage0 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1 Harmonic0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 Notch0 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 Notch1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 Notch1 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 Notch0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1 Notch0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 Notch0 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 Transient0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 1 Transient0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 Transient0 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 Transient0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 0 1 Transient0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 Transient0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 1 Impulse0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 1 Impulse1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 Sag and harmonic100D

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TF

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tf(ddt

P

S

ws

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0

St

P

S

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se

0 0 1 0 1 0 0 1 00 1 0 1 1 0 0 1 00 1 1 0 1 0 0 1 0

C DC DC DC DC DC DC DC DC DC

cl1 = 00000001 Zcl1 = 00100101cl2 = 00001001 Zcl2 = 00101011cl3 = 00010001 Zcl3 = 00110011cl1 = 00001101 Vcl1 = 01001001cl2 = 00001000 Vcl2 = 01100011cl1 = 00001011 Vcl3 = 01111001cl2 = 00010110 Vcl4 = 00001111cl1 = 00000011 Vcl5 = 10001010cl2 = 00011101

hen each rule can be represented by using the assigned binary.or instance, the rule expressed in (12) becomes

f [00000001 00001000 00001011 00000011

00110011 01100011], then sag (13)

Consequently, from the first 26 rules, we can build two matrices:he primary matrix (P), which contains the sets that are triggeredrequently for each disturbance type, and the secondary matrixS), which contains the sets that are triggered less frequently. Theimension of P and S are �× 6, and (26 −�) × 6, respectively, with �esignating the number of rules contained in the P. Then we obtainwo matrices as follows.

=

⎡⎢⎢⎣

P11 P12 P13 . . . P16P21 P22 P23 . . . P26

...P�1 P�2 P�3 . . . P�6

⎤⎥⎥⎦ (14)

=

⎡⎢⎢⎣

S11 S12 S13 . . . S16S21 S22 S23 . . . S26

...S(26−�)1 S(26−�)2 S(26−�)3 . . . S(26−�)6

⎤⎥⎥⎦ (15)

here each element in the two matrices is a binary number corre-ponding to the feature class in a specific rule.

Now let us examine (13) again. If we add the six binary numbersepresenting the six features in (13), we will acquire an output of

tmnnp

0 1 0 1 0 0 0 Sag and harmonic0 1 1 0 0 0 0 Swell and harmonic0 1 1 0 0 0 0 Swell and harmonicDC DC 0 0 0 1 0 Flicker

0101101, as shown below

0000001 + 00001000 + 00001011 + 00000011 + 00110011

+ 01100011 = 10101101 (16)

imilarly, summing each row in (14) and (15) results in the followingwo vectors

sum =

⎡⎢⎢⎢⎢⎣

P sum1P sum2P sum3

...P sum�

⎤⎥⎥⎥⎥⎦

(17)

sum =

⎡⎢⎢⎢⎢⎣

S sum1S sum2S sum3

...S sum(26 − �)

⎤⎥⎥⎥⎥⎦

(18)

Let us define a threshold value Bthreshold. Then, we rearrange thebove two vectors in a way so that all the values less than or equalo Bthreshold are put at the top part of the vectors and the values thatre larger than the threshold value are put at the bottom part of theectors.

The decision-making step proceeds as follows. After the fea-ure classes are derived from a waveform, they are added together.hen the sum is compared with the values in the P sum and S sum,nd the disturbance type is decided corresponding to the matchedalue.

Although similar to an expert system [19–25], the method pre-ented here is faster and more computationally efficient than anxpert system. An expert system needs to compare a set of values

o another set of values to determine whether certain conditions are

et, but our proposed method only needs to compare two binaryumbers. If the set of rules is much larger, then this would sig-ificantly speed up the decision-making step. An example will berovided later to further illustrate the advantage.

T. Nguyen, Y. Liao / Electric Power Systems Research 79 (2009) 569–575 573

extrac

acpmtAcirn

Bposvd

Fig. 3. Flowchart of feature

Fig. 3 summarizes the steps of the overall feature extractionnd decision-making process. The Data Format Conversion blockonverts the inputs from a specific recording device or simulationackage format into a common format comprehensible to the otherodules of the algorithm. The Fourier and S-transform Based Fea-

ure Extraction block calculates the proposed features. The Binary

ssignment block assigns a specific binary number to each featurelass. If the fundamental component V is Vcl4, then the disturbances a flicker. However, if Vcycles is not Vcl4, all the binary values rep-esenting the six features are added together yielding a binaryumber called Ob. If Ob is less than or equal to the threshold value

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tion and decision-making.

threshold, then the program will compare the value Ob to the topart of vector P sum. Otherwise Ob is compared to the bottom partf P sum. As we mentioned above, if the set of rules is large then thistep would save significant time for the decision-making step. If thealue matches one of the values in P sum, then it will determine theisturbance type according to the matched binary number. If there

s no match between Ob and P sum, it then attempts to match thelements in S sum similarly. Once the disturbance type is deter-ined, the process is terminated for that particular waveform.The above procedure is further illustrated by an example distur-

ance. Let us revisit the transient signal and it features as depicted

574 T. Nguyen, Y. Liao / Electric Power Systems Research 79 (2009) 569–575

Table 2Classification results yielded by the proposed binary feature matrix system

Type of PQ disturbances Number of disturbances Number of cases correctly identified Number of cases mistakenly identified Correct identification rate (%)

Sag 3,362 3,361 1Swell 3,350 3,350 0Outage 3,350 3,350 0Harmonic 3,310 3,310 0Switching transient 3,320 3,300 20Impulse 3,315 3,275 40Flicker 3,350 3,350 0NSSS

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otch 3,340 3,305ag and harmonic 100 100well and harmonic 100 100um 26,897 26,801

n Fig. 1. Since the voltage magnitude stays within the proximityf 1, therefore according to Section 2.3 the fundamental voltage iscl5. Also, the total number of cycles in the waveform that have totalarmonic distortion greater than the threshold of 0.03 is 1. Thus, H

s of category of Hcl1.By examining the phase angle shift of all cycles of the signal, we

an determine that the maximum phase angle shift is very small,ess than 0.4. For this reason, the phase angle shift belongs to classcl1. Moreover, since the signal possesses a significant number ofscillations during the disturbance, the oscillation number is oflass Ocl2. In addition, the value of 0.0095 for ˇ classifies it as ˇcl3s defined in Section 2.3. The value of Z being 0.0026 makes it acl3 class.

Therefore, the extracted feature set is [ˇcl3, �cl1, Ocl2, Hcl1,cl3, Vcl5]. If an expert system based decision-making technique isdopted, these six features would be compared feature by featureith the features contained in the 27 rules as follows. Let us look at

ule 1 first. Since rule 1 of the given rules set has ˇcl2, this indicateshat the extracted feature set does not match this rule, regardlessf the rest of the features in rule 1. Move on to rule 2 and we canee that rule 2 starts with ˇcl1. Again, the set of features in this ruleoes not match the set of extracted features. Keep moving downhe list of the 27 rules, the extracted features eventually matcheshose of rule 19. Consequently, rule 19 is triggered, and the signals thus determined as a transient disturbance.

On the other hand, if the proposed binary feature based system ismployed, the result can be reached more efficiently, as explaineds follows. First, six binary numbers are assigned to the six fea-ures extracted from the signal; the six binary numbers 00010001,0001101, 00010110, 00000011, 00110011, and 10001010 representcl3, �cl1, Ocl2, Hcl1, Zcl3, and Vcl5, respectively. We then add the sixinary numbers together and achieve the output in (19):

0010001 + 00001101 + 00010110 + 00000011 + 00110011

+ 10001010 = 11110100 (19)

he program then compares (19) to the elements of the columnector P sum as shown in (17). It matches one of the value in thisector. Thus, the matching process is terminated for this waveforms the output indicated that the disturbance is a transient.

With the proposed method, the program only needs to matchne binary number to another binary number. In this case it trieso match 11110100 to one of the binary number in the vector in17). Therefore, the proposed method displays the advantage ofeing faster and more efficient than the traditional expert system,specially if the set of rules is large.

. Evaluation studies

Tens of thousands of waveforms of various types of disturbancesave been obtained and utilized in the evaluation studies. They

3500

96 99.64

nclude field data from Tennessee Valley Authority, and simulatedata generated by employing Electromagnetic Transients ProgramEMTP) [26] and algebraic equations method [27]. For the wave-orms, sampling frequency ranges from 7680 Hz to 30,720 Hz, withower system fundamental frequency being 60 Hz. The designedlgorithm is applicable to various sampling frequencies withouthe need of modification. 10 fundamental cycles of waveforms aretilized for analysis.

The results are shown in Table 2. In this table, the first col-mn represents the type of PQ disturbances being studied. Theollowing two columns, 2 and 3, represent the total number ofases being studied of each disturbance and the number of caseshat are correctly identified, respectively. Column 4 indicateshe number of cases of each disturbance that are mistakenlydentified. The last column shows the correct identification rate.he last row of this table represents the aggregate results. It cane seen that the new system yields a correct identification ratef 99.64%. These studies show that the proposed methods foreature extraction and decision-making are quite efficient for PQisturbance classification.

. Conclusion

This paper proposes six distinctive features for power qualityisturbance waveforms employing Fourier and S-transform tech-iques. Based on the features, a binary feature matrix system isesigned for classifying the types of the disturbances, which has thedvantages of simplicity and computational efficiency. Both simu-ated and real-world data have been utilized to test the designedlgorithms and quite promising results have been achieved.

eferences

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[3] W. Kanitpanyacharoean, S. Premrudeepreechacharn, Power quality problemclassification using wavelet transformation and artificial neural networks, in:IEEE Region 10 Conference, C, 2004, pp. 252–255.

[4] E.M. Tag Eldin, A new algorithm for the classification of different transient phe-nomena in power transformers combining wavelet transforms and fuzzy logic,in: Proceedings of the 46th IEEE International Midwest Symposium on Circuitsand System, vol. 3, 2003, pp. 1116–1121.

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Ycomputer engineering at the University of Kentucky as an Assistant Profes-sor since August 2005. From August 2000 to August 2005, he was with the

T. Nguyen, Y. Liao / Electric Power

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hai Nguyen (S’07) received the B.S., and M.Eng. degrees in electrical engineeringrom the University of Louisville, Louisville, Kentucky, U.S.A., in 2002 and 2003,espectively. Currently, he is a Ph.D. student of the electrical engineering departmentt the University of Kentucky, Kentucky, U.S.A. His research interests are in poweruality and power system protection.

uan Liao (S’98, M’00, SM’05) has been with the department of electrical and

BB Corporate Research Center as a R&D Consulting Engineer and then Princi-al R&D Consulting Engineer. His research interests include protection, poweruality analysis, large-scale resource scheduling optimization and EMS/SCADAesign.