A New Classification for Power Quality Events in Distribution Systems

A new classification for power quality events in distribution systems

Okan Ozgonenela , , ,

Turgay Yalcina , ,

Irfan Guneyb , ,

Unal Kurtc ,

a Ondokuz Mayis University, Electrical & Electronic Eng. Depart., Kurupelit, Samsun, Turkey

b Acibadem University, Dean of Engineering Faculty, Gulsuyu Mah. Fevzi Cakmak Cad. No. 1, Maltepe, Istanbul, Turkey

c Amasya University, Technology Faculty, Electrical & Electronic Eng. Depart., Amasya, Turkey

Received 19 January 2012

Revised 22 July 2012

Accepted 18 September 2012

Available online 12 October 2012

http://dx.doi.org/10.1016/j.epsr.2012.09.007, How to Cite or Link Using DOI

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Abstract

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 of power quality disturbances. Feature vectors are extracted from the sampled power

signals with the Hilbert Huang Transform (HHT) technique. HHT is a combination of EEMD 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 features—i.e., the mean, standard deviation, singular values, maxima and

minima—of both IF and IA are then calculated. These features are normalized along with the inputs of SVM and other classifiers.

Highlights

► Hilbert Huang Transform is first introduced for all possible power quality issues. ► Feature extracting and selection are done by an effective way. ► Singular value decomposition method is

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applied to get additional features. ► Support vector machine is used as classifier of all possible power quality issues. ► Performance of the suggested algorithm is then compared to well-known

algorithms.

Keywords

Power quality disturbances ;

Hilbert Huang Transform ;

Ensemble Empirical Mode Decomposition ;

Support Vector Machines ;

One-against-all method

1 .Introduction

Power monitoring has taken on an important role in power systems. 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, electromagnetic interference (EMI), and power system outage [1] and [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 variations 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 waveform disturbances [3].

To analyze these disturbances, data are often 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 capable of providing time information about signal disturbances. Short

time Fourier transform (STFT) provides both time and frequency information, but it suffers severely from the Heisenberg uncertainty 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 multiresolution analysis in general), which can give good time resolution for high-frequency events, and good frequency resolution for low-frequency events, which is the type of

analysis best suited for many real signals. Wavelet transform [5], which is a popular signal analysis method, offers continuous and discrete wavelet transforms (CWT and DWT) [6] and wavelet packet

transform (WPT) [7] and [8] for the feature extraction of signals.

Many classification algorithms have been developed by researchers for classification of power electrical disturbances. Integrated 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 time–time (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 transform 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 distortions 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 estimation

algorithm has been used to measure the flicker voltage magnitude [13], while the Simulated Annealing (SA) optimization algorithm has been used for measuring the voltage flicker magnitude,

frequency and the harmonics contents of the voltage 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 standards in force because of its accuracy and working characteristics [15]. Wavelet multi-resolution decomposition, which combines frequency

domain with time-domain analysis for power disturbance 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 propagation algorithm (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 decomposition methods, EMD is intuitive and direct, with the basic functions 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 the time between the successive 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 by Huang et al. [20] as it allowed the decomposition of signals that were all positive, all negative, or both. This implied that the data did not have to have a zero mean. This also allowed a

finer resolution of the oscillatory modes.

This paper presents a hybrid power quality monitoring approach based on EEMD and SVM. EEMD and basic statistical calculations 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 classification 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 accuracy.

2 .Materials and methods

The suggested classification process is shown in Fig. 1.

<img class="figure large" border="0" alt="Full-size image (60 K)" src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr1.jpg" data-

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Fig. 1. Basic stages of the classification process.

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As seen in Fig. 1, all major power quality issues such as voltage sag/swell, flicker, harmonics, transients, DC offset, electromagnetic interference (EMI), and interruptions have been investigated. Training procedure has been achieved by using synthetically generated data. Moreover, the supply frequency and flicker frequency are randomly chosen between 49.5 Hz and 50.5 Hz and between 8

Hz and 25 Hz, respectively. Sampling frequency is set at 25.6 kHz in compliance with the real-time data. This yields a 3200 × 3 data matrix for a three-phase system. The testing procedure was

completed 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 event breaking 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 distinctive features; namely, IMFs. Feature selection requires a series of calculations based on statistics such as maxima, minima, singular value,

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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 chosen 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 with a one-against-all

(OAA) approach is used for classification of PQ events.

Sections 2.1, 2.2, 2.3 and 2.4 give the mathematical background of the proposed 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 signal and

could decompose the complicated signal function into a number of IMFs. IMFs reflect the intrinsic and real physical information of analyzed the signals. Moreover, the superposition of IMF

components can reconstruct the analyzed signal (Eq. (1)). EMD method is a sifting process; to decompose one IMF component requires many sifting cycles. The sifting process actually serves two

purposes: to eliminate riding waves and to make the wave profiles more symmetrical with respect to zero (Fig. 2).

<img class="figure large" border="0" alt="Full-size image (46 K)" src="http://ars.els-cdn.com/content/image/1-s2.0-

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Fig. 2. A flowchart of EMD algorithm.


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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 signal d(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 replace x(t) with d(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 obtain cn(t), continue steps (i)–(vi) after n iterations. 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 collection of n IMF, named from c1(t) to cn(t). Now, the original signal can be represented as:

(1)

<img height="46" border="0" style="vertical-align:bottom" width="141" alt="View the MathML source" title="View the MathML source"

src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-si1.gif>"

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 frequency modulated signal of constant amplitude. To guarantee that the IMF components maintain enough physical sense of both amplitude 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 convergence test, computed from the two

consecutive sifting results as

(2)

<img height="49" border="0" style="vertical-align:bottom" width="198" alt="View the MathML source" title="View the MathML source" src="http://ars.els-

cdn.com/content/image/1-s2.0-S0378779612002891-si2.gif>"

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

A typical value for SD can be set between 0.2 and 0.3. As a comparison the two Fourier spectra, computed by shifting only five out of 1024 points from the same data, can have an equivalent SD of 0.2–0.3 calculated point-by-point. Therefore, SD value of 0.2–0.3 for the sifting procedure is a very

rigorous limitation for the difference between siftings [20].

In Fig. 2, Extrema <3 indicates that the residue becomes a monotonic function from which no more IMFs can be extracted.

2.2 .EEMD

One of the major drawbacks of the EMD algorithm is the appearance of mode mixing. Therefore, the EEMD algorithm was introduced. The algorithm defines the IMF set for an ensemble of trials, each

one obtained by applying EMD to the signal of interest with added independent identically distributed white noise of the same standard deviation. The problem of mode mixing can be

overcome by taking into account the properties of white noise [21].

The steps for the EEMD algorithm are as follows:

i.

Add a white noise series n(t) to the targeted signal, named x1(t) in the following description, and x2(t) = x1(t) + n(t).

ii.

Decompose the data x2(t) with the EMD algorithm.

iii.

Repeat Steps (i) and (ii) until the trial numbers, regardless of added white noise, consistently register the same power. The new IMF combination Cij(t) is achieved when i is the iteration number and j is

the IMF scale.

iv.

Estimate the mean (ensemble) of the final IMF of the decompositions as the desired output:

(3)



where ni denotes the trial (ensemble) numbers.

(4)

<img height="36" border="0" style="vertical-align:bottom" width="68" alt="View the MathML source" title="View the MathML source" src="http://ars.els-cdn.com/content/image/1-

s2.0-S0378779612002891-si4.gif>"

where ni is the number of ensemble members, ɛ is the final standard deviation of error, which is defined as the difference between the input signal and the corresponding IMFs [22].

In practice, the number of ensemble members is often set to 100 and the standard deviation of white noise series is set to 0.1 or 0.2.

2.3 .Comparison of EMD and EEMD decomposition for feature extraction and selection

The extracted IMFs by EMD and EEMD are shown in Fig. 3a and b. The low-level IMF contains high-frequency components; while the high-level IMF contains low-frequency components.

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src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr3.jpg" data-thumbsrc="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr3.sml" data-

fullsrc="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr3.jpg>"

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



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In Fig. 3a, the IMFs with EMD algorithm have side effects that reduce the accuracy of the classification algorithms. However, the EEMD algorithm does not present side effects and is,

therefore, used in this study. Another criterion is the Pearson product-moment correlation coefficient (R). It is a measure of the correlation (linear dependence) between two variables (X and

Y), giving a value between +1 and −1 inclusive.

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).

(5)



where <img height="18" border="0" style="vertical-align:bottom" width="87" alt="View the MathML source" title="View the MathML source"

src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-si6.gif">, <img height="14" border="0" style="vertical-align:bottom" width="13" alt="View the MathML source"

title="View the MathML source" src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-si7.gif"> and σX are the standard score, sample mean, and sample standard

deviation of X. Table 1 shows the R values for EMD and EEMD.

Table 1. Relation with first IMFs and current power signal.

Cross-CorrelationEMDEEMD

IMF–healthy state0.976

21.000

0

IMF–voltage sag0.881

90.908

1

IMF–voltage swell0.930

00.968

5

IMF–voltage flicker0.970

00.995

0

Cross-CorrelationEMDEEMD

IMF–harmonics (3, 5, 7)0.785

50.786

1

IMF–transient0.568

50.610

4

IMF–dc component0.924

40.935

7

IMF–EMI0.533

40.642

9

IMF–outage0.931

30.985

6

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Table 1 also proves that EEMD has better resolution for the analyzed PQ events.

2.4 .Hilbert-Huang Transformation Method (HHT)

HHT represents the original signal X(t) into the time frequency domain by combining the empirical mode decomposition with the Hilbert transform. The advantage of using HHT for feature extraction

procedure can be summarized in Table 2.

Table 2. Comparison of frequently used mathematical methods for feature extraction [20] and [21].

FourierSTFTWaveletHHT

BasisNon-adaptiveNon-adaptiveNon-adaptiveAdaptive

FrequencyConvolution:

GlobalConvolution:

RegionalConvolution:

RegionalDifferentiation:

Local

PresentationEnergy-

frequencyEnergy-time-



frequency

Non-linearNoNoNoYes

Non-stationaryNoNoYesYes

Feature Extraction

NoDiscrete: No

Continuous: YesDiscrete: No

Continuous: YesYes

FourierSTFTWaveletHHT

Theoretical base

Theory complete

Theory completeTheory completeempirical

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EMD can decompose signal into a finite number of n IMFs (Cj, j = 1,2,3...,n), which extract the energy associated with various intrinsic time scales and residual rn; this step is called the sifting 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 by one.

At any point, the mean value of the envelope defined by the local maxima and minima is zero.

The Hilbert transform is then applied to each IMF component Cj:

(6)



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

(7)



With amplitude, (IA), and phase defined by the expressions:

(8)



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

(9)


s2.0-S0378779612002891-si11.gif>"

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

(10)



Fig. 4 and Fig. 5 show IA and IF curves for healthy and voltage sag signals. Fig. 6 shows the IA values for all analyzed PQ events.



S0378779612002891-gr4.jpg>"

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


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S0378779612002891-gr5.jpg>"

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


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S0378779612002891-gr6.jpg>"

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




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3 .Singular value decomposition (SVD)

SVD is used for calculating singular values and increases the accuracy of the classification algorithm. Singular values give more information for identification of the system. SVD is a part of hierarchical

classification on multiple feature groups in this study.

For any real m × n matrix A, there exist orthogonal matrices

(11)



such that:

(12)

A=UΣVT

where Σ=diag(σ1,σ2,...,σmin(m,n))) Rm×n and σ1≥σ2≥...≥σmin(m,n)≥0∈.

The σi is the ith singular value of A in non-increasing order and the vectors ui and vi are the ith left singular vector and the ith right singular vector of A for i = min(m,n), respectively [22]. The singular

values of matrix A are unique, while the singular vectors corresponding to distinct singular values are uniquely determined up to the sign.

Fig. 7 shows singular values for analyzed power signals. Fig. 7 only 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.

<img class="figure large" border="0" alt="Full-size image (46 K)"

src="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr7.jpg" data-thumbsrc="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr7.sml" data-

fullsrc="http://ars.els-cdn.com/content/image/1-s2.0-S0378779612002891-gr7.jpg>"

Fig. 7. Singular values for training data set.


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4 .Support vector machines (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 classified

using the individual hyper-planes. The optimal hyper-plane, found during a training phase, makes the smallest number of training errors [23]. Fig. 8 illustrates an optimal hyper-plane for two classes

of training data.




S0378779612002891-gr8.jpg>"

Fig. 8. Optimal hyperplane.


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Let {xi,yi}, i = 1,2,…,N be N training data vectors xi with class label yi. Given an input vector x, an SVM constructs a classifier of the form:

(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 of Mercer's theorem [24] and [25].

Frequently used kernel functions are the polynomial kernel K(xi,xj)=d(xi.xj+1) and Gaussian Radial Basis Function (RBF), K(xi,xj)=e−|xi−xj|2/2σ2.

The problem of finding the optimal hyperplane is specified by the following quadratic programming problem:

Minimizing:

(14)




subject to:

(15)


s2.0-S0378779612002891-si21.gif>"

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-all method

For a k-class problem, the one-against-all method constructs k SVM models. The ith SVM is trained with all of the training examples in the ith class with positive labels and the others 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)}, where the ith sample xi∈Rn and yj∈{1,...,k} is the class of xj the ith SVM solves the following optimization problem

[26], [27] and [28] (where c > 0 is a regularization parameter for the tradeoff between model complexity and training error, and ξi measures the (absolute) difference between f(x)=T(wi)ϕ(x)+bi

and yi.):

(16)



subject to

(17)



The decision function of the ith SVM is

(18)



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

(19)



A comparative study is carried out in the next section that compares 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.

Table 3. Comparisons of the performances of the one-against-all (OAA) and the one-against-one (OAO) SVM classifiers.

Total number of training/testing segments

Classification (SVM) One Against All (OAA)–One Against One (OAO)

90/25HSVSGVSWVFVHVTRVDCEMIVI

HS3–200000000

VSG12–20000000

VSW002–2100000

VF0003–200000

VH00003–30000

VTR000004–3010

VDC0000003–300

EMI00000103–20

VI000000002–2

HS: healthy state, VSG: voltage sag, VSW: voltage swell, VF: voltage flicker, VH: voltage harmonics, VTR: voltage transients, VDC: DC voltage, EMI: electro magnetic interference.

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Diagonal elements represent the correctly classified PQ disturbances while non-diagonal elements represent incorrect classification results (Table 3).

5 .Experimental results and discussions

To evaluate the performance of the proposed power quality classification 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

System through field measurements carried out by application-specific mobile monitoring equipment. In this project, PQ measurements are being carried out according to IEC 61000–4-30,

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 measurement 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 [29], [30] and

[31]. The real-time data are acquired in 3 s, which corresponds to 150 periods.

Table 4. Number of measurement point [29], [30] and [31].

Number of measurement points

Voltage levelLoad type

Heavy IndustryIndustry + urbanUrban onlyTotal

NBNFNBNFNBNFNB

NF

33 kV816172814163960

154 kV351013992227

Number of measurement points

Voltage levelLoad type

Heavy IndustryIndustry + urbanUrban onlyTotal

NBNFNBNFNBNFNB

NF

380 kV11470058

Total1222314823256695

NB: number of buses, NF: number of feeders.

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The most accurate multi classifiers (OAA) for each PQ dataset are 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 method is chosen for the multiclass SVM in this

study. According to the results of the test data, the effectiveness of the proposed SVM algorithm is made suitable by increasing the RBF kernel parameter's standard deviation (sigma 0.1 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 performance of SVM algorithm with OAA and OAO is calculated as 100% and 84%, respectively. Nevertheless, the performance of SVM algorithm 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.

ClassifierErrorAccuracyTime (s)

SVM-Linear0.400

00.60002.136

SVM-Poly d = 20.0001.00012.711

ClassifierErrorAccuracyTime (s)

SVM-RBF sigma = 0.10.280

00.72008.208

SVM-RBF sigma = 10.000

01.00010.686

Gauss Mixture Model0.255

50.74441.493

AdaBoost + Naive Bayes0.040

00.96003.529

Bagging + Naive Bayes0.160

00.84002.577

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6 .Conclusion

In this paper, a different PQ monitoring approach based on EEMD, HHT, and SVM was proposed. EEMD was used for signal conditioning technique and two distinctive features, IA and IF, were

obtained by HHT. Then a series of statistical calculations was performed 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 potential capability of the suggested hybrid algorithm in classifying the distorted PQ waveforms.

Acknowledgement

The authors would like to thank TUBITAK for supplying real-time data.

References

[1]

M.D.L. Angrisani, P. Daponte, A. Testa

A measurement method based on the wavelet transform for power quality analysis

IEEE Transactions on Power Delivery, 13 (4) (1988), pp. 990–998

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Corresponding author. Tel.: +90 362 4377204.

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A new classif ication for pow er quality events in distribution systems¶Okan Ozgonenel, Turgay Yalcin, Irfan Guney, Unal Kurt¶Published in:¶Electric Pow er Systems Research¶Volume 95, February 2013, Pages 192-199¶doi: 10.1016/j.epsr.2012.09.007

http://www.elsevier.com.pallas2.tcl.sc.edu/

http://www.sciencedirect.com.pallas2.tcl.sc.edu/science/article/pii/S0378779612002891

A New Classification for Power Quality Events in Distribution Systems

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