Classification of Disturbances in Hybrid Power System Using Modular PNN and SVMs

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Classication of disturbances in hybrid DG system using modular PNN and SVM

Soumya R. Mohanty a, Prakash K. Ray b, Nand Kishor a, , B.K. Panigrahi ca Electrical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad, Indiab Electrical and Electronics Engineering Department, International Institute of Information Technology, Bhubaneswar, Indiac Electrical Engineering Department, Indian Institute of Technology, New Delhi, India

a r t i c l e i n f o

Article history:Received 18 April 2012Received in revised form 3 August 2012Accepted 9 August 2012Available online 26 September 2012

Keywords:DetectionClassicationPower qualityProbabilistic neural networkS-transformSupport vector machines

a b s t r a c t

This paper presents the classication of islanding and power quality (PQ) disturbances in grid-connecteddistributedgeneration(DG) based hybrid power system. The penetration of DG inuences thePQ levels inthedistributionnetworks. Islanding disturbances areseparated out fromthe PQdisturbances based on theselection of suitable threshold value, at the initial stage of classication process. Further, the power qual-ity disturbances are automatically classied into distinct classes based on feature extraction using S-transform followed by training of two classiers, namely, modular probabilistic neural network (MPNN)and support vector machines (SVMs). Five different types of disturbances are considered for the classi-cation problem. Thestudy reveals thatS-transform(ST) in association withMPNN andSVMcan effectivelydetect andclassify islanding andPQ disturbances. The proposedmethodology uses features instead of realdata set and thereby reduces the data size to classify disturbance signal without losing its original prop-erty. The accuracy and reliability of proposed classier is also tested on signals contaminated with noiseand PQ disturbances caused due to wind speed variation on an experimental prototype set-up.

2012 Elsevier Ltd. All rights reserved.

1. Introduction

The fast growing industrialization, liberalization and deregula-tion of electricity market, green-house effect along with possibleshortage of fossil fuels for conventional power generating plants,lead to nd an alternative power generation from renewable re-sources. Therefore, the research and technology for alternative en-ergy resources like wind, photovoltaic, and fuel cell are developingvery fast to make them more consumer friendly. With integrationof these sources in the form of distributedgeneration (DG) into theexisting grid, the system will be reliable and robust as far as powermanagement is concerned along with reduction of environmentalpollutions. But on other hand, unpredictable characteristics of wind speed and solar radiations lead to unreliable performanceof wind energy conversion systems and photovoltaic in grid-con-nected/isolated mode of electric supply [1]. As such, these re-sources may be integrated along with some energy storingdevices like battery energy, ywheel energy systems, and ultraca-pacitors for isolated hybrid system or connected to the power gridto enhance the quality and reliability of power supply [26] .

An increase in DG penetration due to its inherent characteristic;network topology and operation characteristic bring un-favorable

factors inuencing power quality (PQ) of thedistribution networks.Since DG can reduce or improve the PQ levels, different aspectsshould be taken into account. In particular, large current variationsduring DG connection or disconnection can lead to signicant volt-age transients. The cyclic variation of DG power output can causevoltage uctuations. The changes of DG active and reactive powercan lead to long-duration voltage variations. In addition, DG canintroduce a number of unusual effects, such as bi-directionalpower ows and an increase of fault current levels. An increase va-lue of fault currents modies the voltage sag characteristics. Also,the waveform distortion levels are inuenced in a different waywith respect to conventional power system according to the typeof DG connection to the grid; direct connection or by power elec-tronic interfaces. Inverters associated with DG, such as photo-vol-taic (PV) systems, carry inherent characteristics of creating PQ disturbances. Integration of solar PV and wind generation re-sources to the utility grid raises serious concerns towards PQ prob-lems. The output of solar PV system experience PQ problems notonly due to solar irradiation/partial cloud, but also due to incorpo-ration of inverter, lter, controlling mechanism, etc. which leads toremarkable degradation in its reliability and performance. Theinstallation of large PVDG units feeding into the medium voltagenetworks or the signicant penetration of small PVDG units con-nected to the low voltage networks can be the source of PQ prob-lems similar to those produced by disturbing loads. Similarly, suchproblem arises in wind energy based power system due to varia-tion in wind speed.

0142-0615/$ - see front matter 2012 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.ijepes.2012.08.020

Corresponding author. Tel.: +91 532 2271411.E-mail addresses: [email protected] (S.R. Mohanty), [email protected]

(P.K. Ray), [email protected] (N. Kishor), [email protected] (B.K.Panigrahi).

Electrical Power and Energy Systems 44 (2013) 764777

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As a consequence, power quality disturbances like voltage sag,swell, notch, momentary interruption, and transients create vari-ous operational problems such as malfunctions of protective de-vices, failure of electrical equipments, instabilities and so on[7,8] . Usually, sag/swells in voltage signal are caused due to start-ing/rejection of large non-linear loads such as induction motorsand transformers. Solid-state switching and power electronic dri-ven devices cause harmonic distortion and notch in the voltageand current signals. Furthermore, sudden switching of linear ornon-linear load and power supply causes momentary interruptionin the voltage signal at the point of common coupling (PCC).

In addition to PQ related issues, another important issue in DGbased hybrid system is islanding event which occurs when a sec-tion of the utility grid is disconnected but the independent DGscontinue to energize the local load in the isolated section. Itsbehavior is unpredictable due to the power mismatch betweenthe load and generation and the lack of voltage and frequency con-trol. The existing grid codes and practice require that DG must dis-continue to feed into circuits that have been islanded from themain grid and that the DG units have to be disconnected by anti-islanding relays before re-closing. It also has been recognized thatexisting standards often do not deliver consistent policy amongnetwork operators or consensus with their customers, developersand operators of distributed generation.

In this context, anti-islanding remains a challenge in operationof renewable based DG system connected to grid. The major issuein the islanding event and control schemes is the protection coor-dination of distributed system with bidirectional ow of fault cur-rent, unlike the conventional over-current protection for radialsystems. Therefore, these problems need to be effectively detectedand classied for reliable operation, control and protection of thepower system. In fact, islanding events have different characteris-tics and effect as compared to PQ disturbances. Thus, these shouldbe separated out as a different class in the initial stage of classi-cation process. And then, the PQ disturbances should be furthersub-classied automatically using suitable pattern recognition/classication techniques. Hence, islanding event should not bemis-interpreted and mis-classiedwith the PQ disturbances. Basedon type of disturbance, proper mitigation technique is to be ap-plied to protect the hybrid DG system. The important steps indetection and classication of PQ disturbances involve signal pre-processing, feature extraction followed by classication.

A comprehensive research works are reported in literatureswhere detection and classication problem of PQ disturbances inconventional power system is discussed. But, few research worksare reported in view of these problems in DG hybrid system. Var-ious active and passive methods are available in literature forislanding detection. The approach based on voltage unbalanceand total harmonic distortion of current [9] , injection of negativesequence current [10] , active frequency drifting methods [11] are

reported in literature. However, the selection of most signicantparameter is a challenging task which may affect the islandingdetection process. Therefore, wavelet transform (WT) can be usedto detect and localize the non-stationary signals in time and fre-quency format [1216] . Similarly, detection and classication of PQ disturbances using wavelet transform [12] and hybrid tech-niques using WT and articial neural network (ANN) is presentedin [1519] . With feature extracted through wavelet transformand trained by ANN, PQ disturbances are classied automatically[1517] . But the main disadvantage of WT is its inability to detectthedisturbances under thepresenceof noise in thevoltage/currentsignal at PCC [20] . As a result, modied wavelet transform in theform of S-transform [20,21] can be used for detection of both islan-ding and PQ disturbances. The S-transform is an extension of the

wavelet transform with a phase correction and thus can providesignicant improvement in detection of PQ disturbances. The ST

has an advantage of providing multi-resolution while retainingthe absolute phase of each frequency component. Its superiorproperties are due to the fact that the modulating sinusoids arexed with respect to the time axis while the localizing scalableGaussianwindow dilates and translates [2226] . Enhancedby suchan approach, it is anticipated that any abrupt change occurred inthe acquired signal would be effectively caught, hence increasingthe reliability of detection of islanding and PQ disturbances. In thiscontext, ST gives important information about the disturbances fordetection and classications even under different noisy scenarios[22,23] . Also, a rule based technique using the S-transform andthe Kalman lter is used to classify the disturbances [24] . As thistechnique is implemented in three stages, the computational bur-den is increased. Moreover, it may be difcult to classify thedistur-bances using rule based algorithm, when the type of disturbancesare increased.

As discussed earlier in the literature, the feature extraction isindispensable for enhanced classication accuracy. The followingparagraph highlights the contribution of the research work pre-sented in this paper. Ten statistical features such as standard devi-ation, energy, mean, skewness and kurtosis are extracted using S-transform and subsequently fed to modular probabilistic neuralnetwork (MPNN) [27] and support vector machine (SVM) [2831] to accomplish automatic classication objective. Articial neu-ral networks with classical learning show some limitations like;the error function to be minimized is multimodal with many localminima, where the learning process may get trapped. And thelearning algorithm is unable to control the complexity of the archi-tecture of ANNs; therefore, the chosen architecture determines thegeneralization abilities. Therefore, to eliminate these limitations,the merits of MPNN and SVM classiers are exploited for classify-ing the disturbances. Probabilistic neural network (PNN) providesafaster and convergent optimal solution [27] . Further, PNN withmodular concept is exploited for classication objective. On theother hand, SVM gives single, optimum and automatic sparse solu-tion by minimizing the generalization and training error. Theparameters of SVM are selected through cross validation so thathighest accuracy for classicationis obtained [28] . Further, the var-iation of environmental characteristics; solar radiation in PV sys-tem and wind speed in wind generation system is said to have aremarkable impact on detection and classication of islandingand PQ disturbances [32] . In this context, islanding and PQ distur-bances are created in experimental prototype wind energy conver-sion system with variation of wind speed as well as for hybrid DGsystem in MATLAB/SIMULINK with variation in wind speed/solarradiation/load demand. First, islanding event and PQ disturbancesare detected by WT and ST techniques with selection of suitablethreshold value. Then hybrid technique, i.e. feature extractionthrough S-transform followed by training through MPNN andSVM is used to separate islanding event as special disturbance. Fur-

ther the PQ disturbances are sub-classied into its different forms.From the result analysis, the hybrid technique is observed to showimproved accuracy in classication because of the advantage of STover WT in feature extraction even under noisy scenario as well asdue to the superiority in training by PNN with modularity and SVMas comparison to the conventional ANNs and PNN.

This paper is organized as follows: DG based hybrid power sys-tem conguration is introduced in Section 2, detection and classi-cation approach is given in Section 3, followed by discussion onseparation of islanding event and PQ disturbances in Section 4.The feature extraction is explained in Section 5 followed by theclassication strategies using MPNN and SVM in Sections 6 and 7respectively. In addition to simulation based classication, thestudy is further extended to signals retrieved from experimental

set-up in Section 8 followed by classication of PQ disturbance;swell with presence of harmonics for hybrid DG system in

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Section 9. Finally, the conclusions drawn from the study is given inSection 10 .

2. Grid-connected hybrid system

Alternative energy resources, such as solar energy, wind energy,and fuel cell have attracted energy sectors to generate power on a

large scale. But, the intermittency characteristics of these resourceslargely affect the quality of power supply. Fortunately, the prob-lems can be partially overcome by integrating these resources toform a hybrid power system, using the merits of one source toovercome the limitations of the other. The hybrid power systemmay be operated as isolated system or grid connected to sharethe excess or decit power as per situation demands. But the gridinterfacing of these resources, as considered in the present study,lead to several power quality and islanding problems which mustbe detected, analyzed and classied for possible mitigation, effec-tive design, operation, co-ordination and control of such hybridpower system. Fig. 1 shows the conguration of hybrid grid-con-nected DG system considered in detection and classication of islanding and power quality disturbances. The modeling of various

resources such as wind, photovoltaic, and fuel cell used in thehybrid system, are based on the available literatures [14] .The parameters of different components of the hybrid system arereferred from [22] and given in tabular form in Table A1 of Appendix A .

3. Detection and classication approach

This section explains techniques for detection of islanding andpower quality disturbances using S-transforms followed by classi-cation of PQ disturbances based on MPNN and SVM. Wavelet andS-transforms are powerful and efcient techniques for detection of non-stationary PQ disturbances. Though, a detail technique isalready described in [22] , a brief description is given in followingparagraphs. In addition to detection methods, classicationmethodology based on modular PNN and SVM is also discussedsubsequently.

3.1. Modied wavelet transform: S-transform

S-transform is a powerful timefrequency analysis which isfound to be suitable for power engineering related problems suchas detection of PQ disturbances and its classication. It is a timefrequency spectral localization technique that combines theconcept of WT and short time Fourier transform (STFT). TheS-transform uses an analysis window, whose width decreases withfrequency providing a frequency dependent resolution and is anextension of continuous WT with a phase correction. It produces

a constant relative bandwidth analysis like wavelet, while it main-tains a direct link with Fourier spectrum. The S-transform has anadvantage that it provides multi-resolution analysis while

DC

AC

DC

DC

DC/DC BOOSTCONVERTER

DC/ACCONVERTER

PV Array60 kW

Solid Oxide Fuel

Cell 60 kW

DC

AC

DC

DC

DC/DC BOOSTCONVERTER

DC/ACCONVERTER

Gear box

DFIG600kW

rotor sideconverter

grid sideconverter

capacitor

Transformer 1 MVA

GRID

wind

LinearLoad

Non-linearLoad

PCC

Transformer 100 kVA

Transformer 100 kVA

CB1

CB2

CB3 CB4

Fig. 1. DG based hybrid power system.

P Q

d i s t u r b a n c e c

l a s s i f i c a

t i o n

I n p u

t s e

t o

f f e a

t u r e

d a

t a

I n p u

t w e

i g h t s

O u

t p u

t w e

i g h t s

Input layer Output layer Hidden layer

M o

d u

l e - 1

O / P

M o

d u

l e - N

O / P

Fig. 2. The structure of modular PNN.

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retaining the absolute phase of each frequency. These propertieshave led to interpret islanding and PQ disturbance signals in termsof a time-series to enhance the detection capability. Therefore, inorder to improve the performance of detection under noisy condi-tions, it is necessary to modify the phase of the mother wavelet.The continuous wavelet transform (CWT) W (s ,a) of a functionh(t ) is dened as:

W s ; a Z 11 h t x t s ; a dt 1where W (s ,a) is a scaled representation of the fundamental motherwavelet; a is the dilation, which corresponds to the width of thewavelet that controls the resolution. The S-transform is formulatedby multiplying the CWT with a phase term as:

S s ; f exp i 2p f s W s ; a 2where the mother wavelet case is dened as:

x t ; f j f j ffiffiffi2p p

exp t 2 f 22 exp i 2p ft 3

In Eq. (2) , the dilation factor a is inverse of the frequency f andhence continuous S-transform is [20,26] :

S s ; f Z 11 h t f

ffiffiffi2p p exp s t

2 f 2

2 !exp i 2p ft dt 4

and the width of the Gaussian window r is given by:

r f T 1

j f j 5

3.2. Discrete S-transform

The power system disturbance signal h(t ) can be expressed in adiscrete form as h(kT ), k = 1, 2, 3, . . ., (N 1) where T is the sam-pling time interval. The discrete Fourier transform of h (kT ) is ob-tained as [25,26] :

H nNT h i 1N XN 1

k1h k T exp i2p nkN 6

where n = 1, 2, 3, . . ., (N 1). TheS-transformof a discrete time ser-ies h(kT ) is obtained by transforming f ? n/NT and s ? jT as:

S jT ;n

NT h i XN 1

m0H

m nNT h i Gm ; n exp

i 2 p m jN 7

where G(m ,n) = exp( 2p 2m2/n2), n 0 and j, m = 0, 1, 2, 3, . . . ,(N 1) and n = 1, 2, 3, . . . , (N 1). Now for n = 0

S jT ; 0 1N X

N 1

m0H

mNT 8

Class 1

Class 2

m

w HP2

HPHP1

w .s + b = + 1 w .s + b = 0 w .s + b = - 1

2m

w

Class 1

C l a s s

2

=

Fig. 3. Optimal hyperplane.

Acquisition of voltagesignal

S transform computation andObtaining S-matrix

Computing energy matrix andstandard deviation

Is energy and SD >threshold

Other power qualitydisturbances, i.e., swell, sag,

notch and momentaryinterruptions

Start

Fix a threshold value

Islanding

Yes No

Acquisition of nextvoltage signal

Fig. 4. Flowchart for islanding and power quality disturbance detection using S-transform.



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Amplitude and phase of the ST-matrix are obtained as:

S jT ;n

NT h i and tan 1 imag S jT ;n

NT h i .real S jT ;n

NT h i n o 9 3.3. Modular probabilistic neural network (MPNN)

PNN is a supervised neural network suitable for pattern classi-cation which works on probabilistic approach based on Bayesianclassier. The basic operation estimates the probability densityfunction (PDF) of features belonging to each class from the trainingsample using Gaussian kernels. The estimated PDFs are used in aBayesian decision rule to perform classication objective. Themainadvantages of PNNare its guaranteed convergence to optimal solu-tion with increased training data, no need of weights initialization,simple and fast learning process. To further enhance its perfor-mance, PNN with modular approach can be used which is basedon the fact that brain performs a specic task by sub-dividing it

to a number of sub-task which works independently and in paral-lel. Thus, its advantages include simple architecture, the functionof components are independent to each other and is faster thanthe conventional monolithic structure. The modular neural net-work structure is shown in Fig. 2. Thearchitectureof PNN structureconsists of three layers. First, thepattern layer assigns onenode foreach of the training example. There are two parameters associatedwith each node namely; w i

, the center with the dimension

p q,

and R i, the covariance matrix of p p size, where p is the lengthof input vector. The output of the nodes in this layer is given by[23,27] :

v i exp f x w iT R 1

i x w ig; i 1 ; 2 ; . . . ; M 10where x is the input pattern/vector and M is the number of inputs.Next, is summation layer, wherein the number of nodes is consid-ered same as the number of disturbance classes. Output of the pat-tern layer is given as input to this layer with associated weights andthus output of this layer can be expressed as:

0 200 400 600 800 1000 1200 1400 1600 1800 20000

0.01

0.02

0.03

sample

m a g n

i t u d e

(a) Negative sequence voltage.

0 200 400 600 800 10000

0.02

0.04 Approx. coef. for dB4

sample

m a g n

i t u d e

0 200 400 600 800 1000-2

0

2x 10

-3 Detail coef. for dB4

sample

m a g n

i t u d e

(b) Detection using dB4 wavelet.

0 200 400 600 800 1000-0.02

0

0.02

0.04 Approx. coef. for DMW

sample

m a g n

i t u d e

0 200 400 600 800 1000-1

0

1x 10

-3 Detail coef. for DMW

sample

m a g n

i t u d e

(c) Detection using (Discrete Mayer Wavelet)DMW wavelet.

sample

a b s o

l u t e v a

l u e o

f S T m a

t r i x

200 400 600 800 1000 1200 1400 1600 1800 2000

10

20

30

40

50

60

(d) Detection using S-transform.

Fig. 5. Islanding detection event for wind and fuel cell system connected to grid with 20-dB noise.



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o j XM

k1w s jk v k ; j 1 ; 2 ; . . . ; q 11

where q is the number of classes. w s jk is the weight associated withthe kth pattern node to jth summation node and o j is the output of the jth summation node. In the next stage, the decision layer takesnecessary action on pattern classication belonging to a particularclass. The modular neural network for classication of PQ distur-bances is designed using least square (LS) algorithm. The value of the smoothing factor [27] in the training of MPNN is varied from0.3 to 1.0 and selected as 0.6 based on the best accuracy of classi-cation results.

3.4. Support vector machines

SVM is a potential tool for solving pattern classication prob-lems that can handle very large feature spaces. They have goodgeneralization properties compared to conventional classiers.Also, the training of SVM is based on statistical learning theorywhere the so-called structural misclassication risk is to be mini-mized as comparison to the empirical risk minimization in tradi-tional classiers [28] . SVM is suitable for different binary andmulti-class automatic classication problems like pattern recogni-tion in protein classication, regression estimation, power systemfault, and PQ disturbances classication [29] . The input vectorspace in SVM is usually mapped into a high dimensional feature

space and a hyperplane in the feature space is used to maximizeits classication ability. SVM can potentially handle large featurespaces since its training is carried out such that the dimension of classied vectors does not affect the performance of SVM. Thissuits for application requiring classication of PQ disturbancesproblem.

SVM provides better generalization properties as compared toconventional neural networks because its training is based onsequentially minimized optimization (SMO) technique [30,31] .For n-dimensional inputs si(i = 1, 2, . . . , M ), M is the number of samples that belong to class 1 or class 2 with outputs oi = 1 for class1 and oi = 1 for class 2, respectively. The hyperplane for linearlyseparable data s is represented as:

f s wT

s b Xn

j1 w js j b 0 12

where w isan n-dimensional vector and b is a constant.Thepositionof the separating hyperplane is decided by the values of w and sca-lar b. Theconstraints to be followed by thehyperplane are: f si P 1if oi = 1 and f si P 1 if oi = 1 and thus,

oi f si oiwT s bP 1 for i 1 ; 2 ; . . . ; M 13The hyperplane that creates the maximum distance between

the plane and the nearest data is called the optimal separatinghyperplane as shown in Fig. 3. The geometrical distance is found

0 200 400 600 800 1000 1200 1400 1600 1800 2000-1

0

1

sample

a m p

l i t u

d e

0 200 400 600 800 1000-2

0

2 Approx. coef. for dB4

sample

m a g n

i t u d e

0 200 400 600 800 1000-1

0

1Detail coef. for dB4

sample

m a g n

i t u d e

(a) Voltage signal with notch (b) Detection by dB4 wavelet transform

sample a

b s o

l u t e v a

l u e o

f S T m a

t r i x

200 400 600 800 1000 1200 1400 1600 1800 2000

100200300400500600700

(c) Detection by S-transform contour with 20 dB noise

Fig. 6. Detection of notch in voltage signal with 20dB noise.

0 500 1000 1500 2000-1

0

1

Sample

A m p l

i t u d e

(a) Voltage signal with swell and harmonics

0 200 400 600 800 1000-0.02

0

Detail coef. for dB4

Sample

A m p

l i t u

d e

(b) Detection by dB4 wavelet transform

Sample

A b s o

l u t e v a

l u e o

f S T m a

t r i x

500 1000 1500 2000

15

20

25

30

(c) Detection by S-transform

Fig. 7. Detection of voltage sag with presence of harmonics in hybrid system.



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as kwk 2 [29] . The optimal hyperplane is obtained based on thequadratic optimization problem:

Minimize

12 kwk

2

C XM

i1ni

subject to

oiwT s bP 1 ni for i 1 ; 2 ; . . . ; M ni P 0 for all i

14

where n i is the distance between the margin, parameter C is er-ror penalty factor that takes into account misclassied point intraining/testing set and the examples si lying on the wrong sideof themargin. Based on KuhnTucker conditions, a maximizeprob-lem [30] can be formulated and the solution of these optimal prob-lem leads to determination of support vector (SV) which lie on theseparating hyper planes. The number of SVM is less than the num-ber of training samples to make SVM computationally efcient[30,31] . The value of the optimal bias b can be found from theexpression:

00.2

0.40.6

0.81

-40

-20

0

200

0.2

0.4

0.6

0.8

1

EMCSMC

S D M C

sagswellnotch

MIislanding

00.2

0.40.6

0.81

0

0.5

10.4

0.5

0.6

0.7

0.8

0.9

1

EPCSDMC

M M C

sagswellnotch

MIislanding

(a) EMC vs SMC vs SDMC (b) EPC vs SDMC vs MMC

00.2

0.40.6

0.81

-6-4

-20

2

x 1015

0

0.2

0.4

0.6

0.8

1

EPCMPC

S D M C

sagswellnotchMIislanding

00.2

0.40.6

0.81

-6-4

-20

2-1.5

-1

-0.5

0

0.5

1

EPCMPC

S P C

sagswellnotchMIislanding

(c) EPC vs MPC vs SDMC (d) EPC vs MPC vs SPC

00.2

0.40.6

0.8

-40

-20

0

200

0.2

0.4

0.6

0.8

1

EPCSMC

S D M C

sagswellnotchM Iislanding

(e) EPC vs SMC vs SDMC

x 1015

Fig. 8. 3-D plot of features.



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b 12XSVs oia i v T 1 si v T 2 si 15

where v 1 and v 2 arethearbitrarySVM forclass 1 and class 2, respec-tively and a is the optimal Lagrangian multiplier.

Then the nal decision function is given by:

f

s

XSVs

a ioisT i s

b

16

Any unknown data sample s is thus classied as:s 2

Class 1 ; f sP 0Class 2 ; otherwise 17

The non-linear classication of PQ disturbances can accom-plished using SVM applying a kernel function by mapping the clas-sied data to a high-dimensional feature space where the linearclassication is possible [31] . There are different kernel functionswhich are used according to type of classication scenario. In thispaper, Gaussian radial basis kernel function which gives the bestresults, as described later in Section 7, is selected and the classi-cation accuracy results are compared with other kernel functions,i.e. polynomial kernel. The Gaussian radial basis kernel functionis dened as:

K s; z exp js z j

2

2r 2 ! 18where r is the width of the Gaussian function known as Gaussiankernel parameter. The detailed explanation on properties of SVMis given in [2931] .

4. Separation of islanding and power quality disturbances

This section explains S-transform based techniques for detec-tion of the islanding and power quality disturbances and method-ology of their separation from each other. The ow chart toimplement this methodology is shown in Fig. 4 and discussedexhaustively in [22] . Two different performance indices (PI)namely standard deviation (SD) and energy content are computedusing ST-matrix and Parsevals theorem [22] for normal, islandingand PQ disturbancescenarios separately. Then, a suitable thresholdvalue is determined by comparing the PI in terms of energy/SD forthe disturbance cases with that of normal operating condition.When the value of PI is greater than threshold value, islanding dis-turbance is detected, otherwise, PQ disturbances are detected [22] .

Different islanding and PQ disturbances are created in MATLAB/SIMULINK by isolating the grid andload switching. Fig. 5 shows thecase when wind and fuel cell hybrid system is connected to gridand suddenly the grid is isolated by circuit breaker action due toa three-phase fault on grid-side. During the islanding event the

voltage waveform at PCC is captured and passed through three-phase sequence analyser block-set in order to obtain the negativesequence component and shown in Fig. 5a. This gure clearlyshows thesudden increase in thevoltage magnitude at the instantsof islanding. Then synthetic noise of 20 dB signal-to-noise ratio(SNR) is added to the negative sequence voltage and is passedthrough WT and ST in order to study the detection capabilities.Fig. 5bd shows the comparison for islanding detection using WT(Daubechies 4 and Discrete Mayer Wavelet). It is clearly observedfrom the results that ST detects the islanding instant whileWT failsin presence of noise. Similarly, other islandingscenarios can be cre-ated by taking different topologies of network with different com-binations of wind, PV and FC connected to grid. The effectiveperformance of ST approach in detection of PQ disturbance such

as notch in voltage signal is further supported in Fig. 6. It is re-ected from the results that the WT approach is susceptible to

noise and thus deteriorates the detection capability. Similarly,the detection of voltage swell in presence of harmonics is pre-sented in Fig. 7 and again the performance of WT is observed toget deteriorated and detection by ST outperforms. The perfor-mance comparison between these two techniques for differentcongurations of renewable resources is reported in [22] . Thus,based on the above discussion, islanding event is distinguished

from the PQ disturbances as a separate class at the initial level of the classication process. Then, the PQ disturbances will be

Decision forClassification of Power

Quality disturbances

Feature extraction such asmean, SD, Energy, Skewness

and Kurtosis from theMagnitude and Phase contour

Acquisition of data from thetransformed signal and

Normalization of data

Passing the voltage signalthrough S-transform andCalculation of ST-matrtix

Extraction of voltagesignal at PCC

Training and Testing of Dataset with different features by

MPNN or SVM

Voltage sag, swell, notchand momentary

interruption

Fig. 9. Flow-chart of the classication strategy using MPNN and SVM.

Table 1

Classication performance of MPNN.

Normal Sag Swell Notch MI

Normal 99 0 1 0 0Sag 2 96 0 1 1Swell 0 0 98 1 1Notch 0 0 1 98 1MI 1 1 2 0 96

Overall accuracy = 97.4%



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sub-classied using MPNN and SVMtechniques as discussed in thefollowing sections.

5. Feature extraction based on S-transform

In this paper, the PQ disturbance signals are simulated in MAT-LAB platform using Simulink model for DG based grid-connectedhybrid power system. Voltage sag, swell, notch and momentaryinterruption as power quality disturbances are simulated and fea-tures of these types of disturbances are extracted from the ST-ma-trix. The sampling frequency of retrieved signals at PCC isconsidered to be 3.0 kHz. From the ST-matrix, important informa-tion in terms of magnitude, phase and frequency are obtained. Tendifferent statistical features extracted from the ST-matrix are de-scribed as follows:

Feature 1: Energy of the magnitude contour (EMC) correspond-ing to maximum magnitude of each column of the ST-matrix.

Feature 2: SD of the magnitude contour (SDMC) corresponding

to maximum magnitude of each column of the ST-matrix. Feature 3: Energy of the phase contour (EPC).

87

88

8990

9192

93

94

9596

97

98

normal sag swell notch MI

Power quality disturbance

C l a s s

i f i c a

t i o n a c c u r a c y

( i n

% )

EMC-SMC-KMC EPC-SPC-KPC SDMC-SMC-KMC SDPC-SPC-KPC

Fig. 10. Classication performance for three combinations of features.

86

88

90

92

94

96

98

100

102

normal sag swell notch MI

Power quality disturbance

C l a s s

i f i c a

t i o n a c c u r a c y

( i n

% )

3-features 5-features 7-features 10-features

Fig. 11. Classication performance for different number of features.

Table 2

Classication performance of MPNN under noise conditions.


(a) 20 dB SNRNormal 94 2 1 1 2Sag 1 93 3 3 0Swell 2 1 94 2 1Notch 2 2 2 91 3MI 3 1 2 3 91


(b) 30 dB SNRNormal 95 2 2 1 1Sag 2 95 0 3 0Swell 2 1 96 0 1Notch 4 1 0 94 1MI 1 1 2 2 96


(c) 40 dB SNRNormal 97 2 0 0 1Sag 0 96 1 1 2Swell 0 0 98 0 2Notch 2 1 0 95 2MI 2 0 4 0 96


Table 3

Performance of SVM with kernel function.

Types of function (for r = 1.0 and C = 0.7) RBF kernel Polynomial kernel

Training accuracy 99.7 98.0Testing accuracy 100 97.8No. of support vectors 62 69



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Feature 4: Standard deviation of the phase contour (SDPC). Feature 5: Mean of the magnitude contour (MMC). Feature 6: Mean of the phase contour (MPC). Feature 7: Skewness of the magnitude contour (SMC). Feature 8: Skewness of the phase contour (SPC).

Feature 9: Kurtosis of the magnitude contour (KMC). Feature 10: Kurtosis of the phase contour (KPC).

The analysis of extracted features of the disturbance signals arecarried out graphically in 3-dimensional (3-D) plots as shown inFig. 8. In these plots, projections of data corresponding to a partic-ular combination of features are shown. These illustrate the dis-tinct nature, i.e. linearly separable characteristics betweenislanding and power quality disturbances such as voltage sag,swell, notch, and momentary interruption. However, close obser-vation suggests that, some of the PQ disturbances are classiedaccurately as discriminative class while other patterns are not lin-early separable, thereby overlappingwith each other andintroduc-ing ambiguity in classication of the disturbances. Thus, these

plots illustrate an approximate classication. As a matter of fact,suitable pattern classication techniques like MPNN and SVM

should be used to further classify the PQ disturbances effectivelyand accurately.

6. Classication based on MPNN

After the separation of islanding event as a distinct class basedon a selection of an appropriate threshold value, PQ disturbances

(a) Training/testing accuracy vs C (b) Number of support vector vs C

Fig. 12. Performance of SVM with variation in parameter C .

(a) Training/testing accuracy vs (b) Number of support vector vs

Fig. 13. Performance of SVM with variation in parameter r .

Table 4

Classication performance of SVM.


Normal 99 0 1 0 0Sag 2 100 0 1 1Swell 0 0 100 1 1Notch 0 0 1 98 1MI 1 1 2 0 98


Table 5

Classication performance of SVM under noise condition.

Normal Sag Swell Notch MI(a) 20 dB SNRNormal 96 1 1 1 1Sag 1 95 2 0 2Swell 0 1 94 1 2Notch 1 2 1 95 1MI 1 2 2 2 93


(b) 30 dB SNRNormal 98 0 0 1 1Sag 1 96 0 1 2Swell 0 1 98 0 1Notch 0 1 1 96 2MI 1 0 1 1 97


(c) 40 dB SNRNormal 99 0 0 0 1Sag 0 98 0 1 1Swell 0 0 99 1 0Notch 0 1 0 98 1MI 1 1 0 1 97




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are further sub-classied using probabilistic neural network inmodular form. The ow chart to illustrate the proposed classica-tion strategy is shown in Fig. 9. The different features discussed inprevious section, based on statistical parameters, are calculatedcorresponding to magnitude and phase contour of the S-transform.These set of features constitute as input to the MPNN. The numberof modules in PNN is decided according to the number of distur-bances to be classied. Each neural network is trained with 50 in-put data of each class and equal number of data is considered fortesting purpose. The overall classication accuracy of PQ distur-bances by use of MPNN is observed to be 97.4% and the resultsare given in Table 1 . The diagonal elements represent correctlyclassied PQ events and the off-diagonal elements represent the

misclassication cases. The overall classication accuracy is de-ned as the ratio of successfully classied events to total number

of events. Further, to test the classication capability of MPNN, itis trained and tested for different combination of features.However, to make the study much simpler and in-exhaustive, thefeatures which provide signicant information about the distur-bance signals are considered. Fig. 10 shows the classier perfor-mance with some combinations of features extracted frommagnitude and phase contour. It is observed that the selection of features is crucial on the performance of MPNN. The combinationof features in terms of statistical indices; SDPCSPCKPC on phasecontour provides satisfactory accuracy in contrast to EMCSMCKMC, EPCSPCKPC and SDMCSMCKMC. Of course, some opti-mization techniques can be implemented for the selection of bestfeature set for optimum classication accuracy, however, this part

of study is not considered in the present study. The classicationperformance comparison with different number of features is

4

4

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

classifier

sag

swell

notch

MI

4

4

4

-1 -0.5 0 0.5 1

-1-0.8

-0.6-0.4

-0.20

0.20.4

0.60.8

1 ClassifiersagswellnotchMI

(a) Sag vs all other disturbances (b) MI vs all other disturbances

4

4

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1 ClassifiersagswellnotchMI

4

4

4

4

-1 -0.5 0 0.5 1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

ClassifiersagswellnotchMI

(c) Notch vs all other disturbances (d) Swell vs all other disturbances

Fig. 14. Boundary plots for one-against-all other classes.

- 1

- 1

1

1

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

classifier

sagswell

- 1

- 1

1

1

-1 -0.8 -0.6 -0.4 -0.2 0 0.2

-0.8

-0.6

-0.4

-0.2

0

0.2

classifiernotchMI

(a) Sag vs Swell (b) Notch vs MI

Fig. 15. Boundary plots for one-against-one.



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shown in Fig. 11 . It is observed that the accuracy improves with theincrease in number of features for classication.

In a hybrid power system with wind energy and photovoltaicsystem, the PQ disturbance signals get contaminated with noisedue to environmental effects such as variation in solar radiationand wind speed [32] . Therefore, the proposed classication ap-proach has to be analyzed under noisy environment in order todemonstrate the efciency of MPNN. In practice, Gaussian noiseis popularly added to synthetic signal for detection and classica-tion of PQ disturbances. Noise with different SNR levels are furtheradded to the extracted signal at PCC. Then, the noisy signal ispassed through S-transform to extract the features for trainingand testing of MPNN for automatic classication of the distur-bances. Theclassication accuracy in percentage is given in Table 2and is observed to be92.6%, 95.2%, and 96.4% with 20 dB, 30 dB and40 dB SNRs respectively. This suggests a satisfactory classicationperformance of MPNN, using features initially extracted throughS-transform. It is observed that these features provide discrimina-tive information for classication of PQ disturbances.

7. Classication based on SVM

Five different data types of normal and power quality distur-bances are used as training and testing sets for SVM classier.The extracted signal is rst transformed into timefrequency do-main using S-transform to obtain the magnitude and phase con-tour as well as the S-transform matrix. The above mentioned tenfeatures are determined corresponding to magnitude and phasecontour of the S-transform. In total, 100 different data sets are ta-ken for each PQ disturbance. Therefore, for ve PQ disturbances,the nal size of feature matrix become equals to 500 10, out of which half of the data set is used for training and remaining forthe testing of SVM classier. For accurate and effective classica-tion, it is desired to implicitly map the input vector into a highdimensional feature space using kernel functions; radial basisfunction, polynomial, sigmoid, etc. In the study, two kernel func-tions; radialbasis function (RBF) andpolynomial functions arecon-sidered to evaluate the performance of SVM. The choice of kernelfunction is data dependent and there are no denite rules govern-ing its choice that results in satisfactory accuracy. However, it isobserved that the RBF kernel function gives better performanceas compared to polynomial kernel function. The comparativeassessment is shown in Table 3 .

Further, basedon binary classier, there are different multiclassclassication methods like one-against-one (OAO) and one-against-all (OAA) but OAO method is said to give best results forPQ classication problems [33] . The binary classier rstly sepa-rates the disturbance signal from the normal signal. Then basedon OAO method, the disturbance signals are classied into differ-

ent classes. For multiclass SVM with N kinds of PQ disturbances,N (N 1)/2 binary classiers are required. After the suitable selec-tion of these binary classiers, thenal decision regarding theclas-sication is taken. Till now, few research literatures are availablethat suggests the appropriate value of C and kernel function argu-ment parameter r . In this context, the variation of regularizationparameter C and kernel function argument r of SVM classierhas been suitably chosenfor better performance. Fig. 12 shows var-iation of training and testing accuracy and number of support vec-tors with the changes in the parameter C. The graphical plotsclearly show that the training and testing accuracies are maximumwith C = 1.0. Similarly, the number of support vectors decreaseswith the increase in the value of parameter C . The investigationof training and testing accuracies with changes in parameter r is

shown in Fig. 13 . The accuracy is obtained to be maximum whilenumber of support vectors is least for the value of r = 1.0. As can be observed from Table 4 , the SVM classies the PQ dis-turbances successfully with an overall accuracy of 99%, which is

Fig. 16. Experimental set-up of a prototype of wind energy conversion system.

(a) Decrease in wind speed

(b) Increase in wind speed

Fig. 17. PCC voltage prole obtained experimentally for PQ disturbanceclassication.



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slightly higher than that obtained by MPNN. The classication per-formance of SVM for different noise levels is also analyzed. Table 5depicts the classier results obtained with different level of noise

introduced in the signal of PQ disturbances. The overall classica-tion accuracies in case of 20 dB, 30 dB and 40 dB SNRs are 94.6%,97% and 98.2% respectively. This shows that with the increase innoise levels, the classication accuracy decreases. These resultsare comparable with MPNN discussed in the previous section.The simulation results show that SVM performs slightly betterthan PNN with modular approach.

When more than two classes of PQ disturbances are required tobe classied, OAA method seems to be appropriate [34] . In the clas-sication task, PQ disturbances as pattern are aimed to be classi-ed into one of all classes. Thus, for the 2-class classicationtask, the classier generates a hyperplane decision surface thatseparates the classes distinctly. Fig. 14 illustrates the class of eachPQ disturbance against all other PQ disturbances through theresulting decision hyperplane into two regions. As observed inFig. 14 a, sag disturbance is separated from other PQ disturbances.Fig. 14 b and c shows similar linear separable regions of one classfrom other, but with overlapping of notch with MI classes. Simi-larly, as indicated in Fig. 14d, there exists a signicant overlappingbetween the features of sag and swell in a hyperplane surface. Inorder overcome this overlapping of classes, one-against-one meth-od is implemented to discriminate the distinct classes. Fig. 15aillustrates a remarkable distinct separable region between sagand swell. Similarly Fig. 15 b shows the separation of notch and MI.

8. Classication of PQ disturbances on a prototype set-up

This section describes the results of PQ disturbances classica-

tion basedon signal retrieved from experimental set-up of a proto-

type wind energy system connected to grid. The completeexperimental set-up consisting of two alternators, other equip-ments required for parallel operation along with arrangements forpossible speed control of prime-movers and is shown in Fig. 16.The two alternators each of 9 kVA supply power to a common loadof 5 kW (maximum capacity) under synchronism. During the oper-ation, one alternator acts as grid while the other alternator whichsupposed to be wind energy system supplies power to the load.Since the rotor speed of wind turbine depends on the input windspeed, the output power and the voltage signal at PCC is greatly af-fected by their variations. Actually, the change in wind speedchanges theprimemover speed of thewind generator. Thus, theef-fect of windspeed on PCC voltage signal is carried out on the proto-type set-up with increase/decrease of the speed of DC motor (i.e.the prime mover of the alternator representing wind energy sys-tem) by 20% of the rated speed. The variation in wind speed leadsto distortion in PCC voltage signal as indicated in Fig. 17. It is ob-served that decrease in wind speed causes a sag occurrence in thevoltage prole. Similarly, an increase in wind speed ultimately re-sults into swell in voltage prole. These sag and swell in voltageprole at PCC are captured using a TDS 2002, two-channels,60 MHz, digital storage oscilloscope (DSO). Thus, the obtained PQ disturbances are characterized due to environmental impact. Thesagand swell occurrence due to variationin wind speedis classiedagainst other PQ disturbances such as notch and momentary inter-ruptions caused because of load variation at constant wind speed.Table 6 presents theclassication accuracy of MPNN andSVM clas-sier.Again, SVMclassier is observedto show slightly better accu-racy in classifying the disturbances. Thus, the classication of PQ disturbances is accomplished based on simulation and prototypeexperimental set-up. The detail specications of the componentsused in the experimental set-up are outlined in Appendix A .

9. Classication of PQ disturbances in hybrid system in presenceof harmonics

In this section, the classication capability of MPNN and SVM istested with presence of harmonics in one of the PQ disturbances,i.e. voltage swell, for windFCPV hybrid system connected to grid.The voltage swell signal with presence of harmonics considered instudy is as shown in Fig. 7 in Section 4. The comparison of classi-cation accuracy by MPNN and SVM is presented in Table 7 . Thoughit is observed that presence of harmonics in swell disturbance doesaffects the classication by fewpercentage in the range of 0.51.5%but still remains in satisfactory range above 96%.

10. Conclusions

The work in this paper has presented study on detection of

islanding and PQ disturbances in grid-connected hybrid power

Table 6

PQ disturbances classication in a prototype wind energy conversion system.


(a) MPNN Normal 97 2 0 0 1Sag 1 98 1 1 1Swell 0 0 98 0 0Notch 1 0 0 99 2

MI 1 0 1 0 96Overall accuracy = 97.6%

(b) SVM Normal 98 0 0 0 1Sag 0 99 1 1 0Swell 1 0 97 1 0Notch 1 1 1 98 0MI 0 0 1 0 99


Table 7

PQ disturbances classication in hybrid system with presence of harmonics in voltage swell.

MPNN SVM

Normal Sag Swell Notch MI Swell withharmonics

Normal Sag Swell Notch MI Swell withharmonics

Normal 97 1 0 0 0 2 Normal 99 0 0 0 0 1Sag 0 97 1 1 0 1 Sag 1 97 1 1 0 0Swell 0 1 96 1 0 2 Swell 1 0 99 0 0 0Notch 1 0 0 97 2 0 Notch 1 1 0 98 0 0MI 1 1 1 1 96 0 MI 0 0 1 0 97 2Swell with

harmonics2 1 2 0 1 94 Swell with

harmonics2 0 0 1 1 96

Overall accuracy =96.17% Overall accuracy =97.67%



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Classification of Disturbances in Hybrid Power System Using Modular PNN and SVMs

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