soumya r. mohanty,senior member, ieee prakash k. ray ...webx.ubi.pt/~catalao/tste2362797.pdf ·...

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 1

Comparative Study of Advanced Signal ProcessingTechniques for Islanding Detection in a Hybrid

Distributed Generation System

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Soumya R. Mohanty, Senior Member, IEEE, Nand Kishor, Senior Member, IEEE,Prakash K. Ray, Member, IEEE, and João P. S. Catalão, Senior Member, IEEE

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Abstract—In this paper, islanding detection in a hybrid dis-6tributed generation (DG) system is analyzed by the use of hyper-7bolic S-transform (HST), time–time transform, and mathematical8morphology methods. The merits of these methods are thoroughly9compared against commonly adopted wavelet transform (WT)10and S-transform (ST) techniques, as a new contribution to ear-11lier studies. The hybrid DG system consists of photovoltaic and12wind energy systems connected to the grid within the IEEE 30-bus13system. Negative sequence component of the voltage signal is14extracted at the point of common coupling and passed through the15above-mentioned techniques. The efficacy of the proposed meth-16ods is also compared by an energy-based technique with proper17threshold selection to accurately detect the islanding phenomena.18Further, to augment the accuracy of the result, the classifica-19tion is done using support vector machine (SVM) to distinguish20islanding from other power quality (PQ) disturbances. The results21demonstrate effective performance and feasibility of the proposed22techniques for islanding detection under both noise-free and noisy23environments, and also in the presence of harmonics.24

Index Terms—Distributed generation (DG), islanding detection,25power quality (PQ), signal processing, support vector machine26(SVM).27

I. INTRODUCTION28

A LTHOUGH there is a huge expansion of transmission and29

distribution of power supply, it is still inadequate to meet30

the scarcity of the power demand. In this context, penetration of31

renewable energy resources-based distributed generation (DG)32

is gradually increasing, which seems a suitable alternative.33

However, due to the intermittency in wind speed and solar34

radiation intensity, they can be integrated with fuel cells along35

Manuscript received February 16, 2014; revised June 17, 2014 andSeptember 11, 2014; accepted October 05, 2014. The work of J. P. S.Catalão was supported by FEDER funds (European Union) throughCOMPETE and from Portuguese funds through FCT-Portugal, under ProjectsFCOMP-01-0124-FEDER-020282 (PTDC/EEA-EEL/118519/2010) and PEst-OE/EEI/LA0021/2013. Paper no. TSTE-00069-2014.

S. R. Mohanty and N. Kishor are with the Motilal Nehru National Instituteof Technology, Allahabad 211004, India (e-mail: [email protected];[email protected]).

P. K. Ray is with the International Institute of Information Technology,Bhubaneswar 751003, India (e-mail: [email protected]).

J. P. S. Catalão is with the University of Beira Interior, 6201-001 Covilha,Portugal, with the Instituto de Engenharia de Sistemas e Computadores—Investigação e Desenvolvimento (INESC-ID), 1000-029 Lisbon, Portugal, andalso with the Instituto Superior Técnico (IST), University of Lisbon, 1049-001Lisbon, Portugal (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TSTE.2014.2362797

with other storage systems to constitute a hybrid system for reli- 36

able and improved power quality (PQ) [1], [2]. On the other 37

hand, high penetration of such systems raises serious issues 38

related to islanding occurrence events and PQ problems that 39

need to be addressed [3]. 40

Islanding is a phenomenon that occurs when DG resources 41

feed to the local load and the utility grid is disconnected. 42

Accurate and early detection of these disturbances is very 43

important to improve the operation of DG systems. In the past 44

few years, a number of research papers have been reported. 45

Islanding detection techniques can be categorized into active 46

and passive methods [4]. Usually, active methods are based on 47

the injection of a small disturbance into the system and ana- 48

lyzing the change in output parameters for islanding detection. 49

Some of the popular active detection techniques are Sandia fre- 50

quency shift (SFS), active frequency drift (AFD) [5], automatic 51

phase shift (APS), and slip mode frequency shift (SMS) [6]. 52

Although these methods provide less nondetection zone (NDZ), 53

SFS may lead to poor PQ because of positive feedback, while 54

AFD, APS, and SMS may show higher NDZ with an increase 55

of reactive load. Passive methods require monitoring of system 56

parameters and the selection of a suitable threshold in order to 57

detect an islanding event. In fact, challenges exist in selecting 58

the most significant parameters and an appropriate threshold 59

value. The use of voltage unbalance/total harmonic distortion 60

(VU/THD) methodology may cause undesirable trip signal to 61

the circuit breaker due to accumulated sum over the perfor- 62

mance indices within one cycle of load switching, which may 63

be misinterpreted as islanding [7], [8]. The rate of change of 64

frequency (ROCOF) [9], [10] has also been considered for the 65

detection of islanding events. Nonetheless, with the variation 66

of real and reactive power imbalance, this method may lead to 67

NDZ [11]. Intelligent-based techniques for islanding detection 68

are discussed in [12] based on the variations in system param- 69

eter indices and data-mining technology. Then, the transient 70

characteristic of disturbance signals is utilized for detecting 71

power islands [13], [14]. 72

Nevertheless, to minimize NDZ in the above-mentioned 73

methods, signal processing techniques like short-time Fourier 74

transform (STFT), wavelet transform (WT), S-transform (ST), 75

hyperbolic S-transform (HST), and TT transform (TTT) Q176

[15]–[18] are being considered to improve detection perfor- 77

mance. Brief time–frequency characteristics can be conve- 78

niently achieved by using STFT, but the transient signals cannot 79

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2 IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

be adequately described due to its fixed window size. Again,80

wavelet is a strong candidate for classification scheme to ana-81

lyze the signal signature, but is oversensitive to noise signals82

and involves computational complexity. The extension of WT83

with phasor information, known as ST [17], [18], was devel-84

oped for the detection of various islanding and PQ disturbances,85

based on moving a varying and scalable localizing Gaussian86

window [19], [20]. However, in some nonstationary signals87

accompanied with transients, the detection capability of ST also88

degrades [21].89

Therefore, considering the review of available techniques90

reported in the literature, the study in this paper considers a91

modified version of ST, i.e., HST, to compare with WT and92

ST for islanding detection. TTT [22]–[24] that represents time–93

time resolution is also used in the comparative study. These94

techniques are applied to extract more distinctive and pertinent95

statistical features, such as standard deviation (STD), energy96

and cumulative-sum (CUSUM) with selection of an appro-97

priate threshold value, so that islanding events are localized.98

Moreover, mathematical morphology with simple addition and99

subtraction operator [25]–[27] is also explored in this paper for100

islanding detection.101

Hence, as a new contribution to earlier studies, a plethora of102

techniques (HST, TTT, and mathematical morphology, against103

WT and ST) along with support vector machine (SVM) is104

proposed and studied in a comprehensive way for island-105

ing detection and classification of disturbances under various106

operating scenarios.107

This paper is organized as follows. The DG-based hybrid108

IEEE 30-bus system is introduced in Section II. Islanding detec-109

tion methods are given in Section III. The simulation results110

with the comparative study of detection methods performance111

are provided in Section IV. Finally, the conclusion drawn from112

the study is presented in Section V.113

II. DG-BASED HYBRID SYSTEM114

Alternative energy resources, such as photovoltaic, wind115

energy systems, and fuel cells have gained momentum with116

penetration in the main grid to meet the scarcity of power to117

a greater extent. But, the intermittent nature of both wind speed118

and solar radiation intensity affects the reliability of power119

supply.120

In this context, integration of the resources to form a hybrid121

DG system becomes a suitable alternative in order to minimize122

the problems. The system may be connected to the grid to share123

the excess or deficit power, as per situation demands.124

Fig. 1 shows the IEEE 30-bus hybrid DG system to study125

the effect of wind/photovoltaic penetration on the detection of126

islanding event.127

This system consists of three wind generators denoted as128

DG1 to DG3, and a photovoltaic system denoted as DG4. The129

modeling of the wind energy system, and photovoltaic system,130

with their specifications, in the current study is referred in [2]131

and [18].132

The overall specifications of this system are as follows.133133

1) Generator: rated short-circuit 1200 kVA, 50 Hz, and134

11 kV.135

Fig. 1. IEEE 30-bus hybrid system with wind and photovoltaic penetration. F1:1

2) DG: 136136

a) DG1, DG2, and DG3: wind generators with 200 kW 137

each, 500 V, doubly-fed induction generator type. 138

b) DG4: photovoltaic system with 100 kW, 500 V. 139

c) Boost converter: switching frequency of 8 kHz, 140

input dc voltage of 200 V, output ac of 500 V, 141

line-to-line. 142

d) Loads: 150 kW (at DG1, DG2, and DG3), 80 kW 143

(at DG4). 144

III. ISLANDING DETECTION METHODS 145

This section presents a brief description of the signal pro- 146

cessing techniques used for islanding detection. 147

The application of wavelet and ST was already demonstrated 148

in [18] and [19] and thus not discussed here. 149

The following sections describe HST, TTT, and mathemat- 150

ical morphology that are explored in this paper for islanding 151

detection. Fig. 2 shows the flowchart for islanding detection. 152

A. Hyperbolic S-Transform 153

The phasor information on contrary to WT is provided for 154

the detection of signatures in time–frequency plane. Yet, the 155

localization is carried out by fixed Gaussian window that has 156

no parameter variation with respect to time/frequency and thus 157

sometimes degrade its performance [21]. 158

On the other hand, ST with Gaussian window fails to localize 159

disturbances in time domain, while its variant known as HST 160

that has pseudo-Gaussian hyperbolic window offers greater 161

control over the window function, providing better time and 162

frequency resolutions at low and high frequencies. The hyper- 163

bolic window provides frequency dependence shape along with 164

its width and height. A higher asymmetry of the window at 165

low frequencies leads to an increase in width in the frequency 166

domain, with consequent interference between dominant noise 167

frequencies [21]. 168

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MOHANTY et al.: COMPARATIVE STUDY OF ADVANCED SIGNAL PROCESSING TECHNIQUES FOR ISLANDING DETECTION 3

Fig. 2. Flowchart for islanding and PQ disturbance detection using ST, HST,and TTT.

F2:1F2:2

In this study, HST contour is used for islanding detection169

where the hyperbolic window is defined as170

Whb =2fs√

2π (αhb + βhb)exp

{−−f2

sX2

2

}(1)

where171

X =(αhb + βhb)

2αhbβhb(τ − t− ξ)

+(αhb − βhb)

2αhbβhb

√(τ − t− ξ)2 + λ2

hb

0 < αhb < βhb and ξ =

√(βhb − αhb)

2λ2hb

4αhbβhb. (2)

The discrete version of HST can be calculated, and172

G(mF , nF ) denotes Fourier transform of hyperbolic window173

G(mF , nF ) =2fs√

2π(αhb + βhb)exp

{−−f2

sX2D

2

}(3)

where174

XD =(αhb + βhb)

2αhbβhbt+

(αhb − βhb)

2αhbβhb

√t2 + λ2

hb. (4)

H[mF , nF ] is the frequency-shifted Fourier transform,175

H[mF ] which is given by176

H[mF ] =1

N

N−1∑

mF=0

h(k) exp(−i2πnF k) (5)

S[nF , j] =

N−1∑

mF=0

H(mF + nF )G(mF , nF ) exp(−i2πmF j)

(6)

where N is the total number of samples and G(mF , nF )177

represents the Fourier transform of the hyperbolic window.178

B. TTT 179

The computation of inverse Fourier transform of discrete ST 180

provides discrete TTT that maps single-dimensional signal in 181

time domain to two-dimensional signal in time domain. 182

Since each of these is associated with a particular window 183

position on the time axis, collectively they give a time–time 184

distribution. The selection of different level as in WT is not 185

required in the TTT. Further, high-frequency component of the 186

signal is better localized in TTT and its output. In this tech- 187

nique, among the different frequency components, the high- 188

frequency components have more energy concentration, thus 189

leading to better localization property. One of the major utility 190

of the TTT is the time-local view, through the scaled windows 191

of the primary time series that makes it an appropriate approach 192

for disturbance detection [22]–[24]. 193

The Fourier transform of the signal is taken as a win- 194

dow sliding along the time axis. Hence, it is designated as 195

the two-dimensional representation of the signal. The general 196

expression of short-time Fourier transform is given by 197

STFT(t, f) =

∞∫

−∞h(τ)w(t− τ) exp(−2πifτ)dτ. (7)

By applying the inverse FT of (7) brings 198

[h(τ)w(t− τ)] =

∞∫

−∞STFT(t, f) exp(−2πifτ)df. (8)

When all values of t are considered, the window function 199

h(τ)w(t− τ) becomes a two-dimensional function denoted by 200

STFTTT, given by 201

STFTTT(t, τ) =

∞∫

−∞STFT(t, f) exp(2πifτ)df. (9)

TTT is obtained from ST as 202

TT(t, τ) =

∞∫

−∞S(t, f) exp(2πifτ)df. (10)

The discrete form appears as 203

TT(kΔt, jΔt) =

N/2−1∑

n=N/2

S(nΔf, jΔt) exp(i2πnk/N) (11)

h(ktΔt) =

N−1∑

j=0

TT(ktΔt, jΔt) (12)

where Δt is the time interval, N is the sampling number, kt 204

is the time index, kt = 0, 1, . . . , N − 1, j is the time shifting 205

index, and nF is the frequency index. 206

C. Mathematical Morphology 207

Morphological filters are nonlinear signal transformation 208

tools that modify shapes of signals [25]. Mathematical mor- 209

phology is developed from set theory and integral geometry, 210

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and derives its name because it deals with the shape of signals.211

It is a nonlinear signal processing approach applied to the study212

on power system fault/disturbance.213

The wavelet, HST, and TTT are all used as integral trans-214

form for detection objective. In fact, computational complexity215

is higher for transients and harmonics. Further, it assumes216

the periodicity of the signal by which accuracy of detection217

degrades. On the contrary, mathematical morphology deals with218

simple addition and subtraction of signals without undergoing219

multiplication and division. With a smaller data window, i.e.,220

with the knowledge of a small portion of the signal, it is able221

to detect the abnormalities quickly and accurately. Dilation and222

erosion are two basic operations with which generalized open223

close operation can be explored. Erosion is a kind of shrink-224

ing transform, which can make the target signal contract with225

holes enlarging. Dually, dilation is an expanding process, which226

realizes the target signal, enlarging together with holes con-227

tracting. Generally, erosion and dilation are not reversible each228

other. So the conjugation of them can form new morphologi-229

cal operators, named by opening and closing, which are also230

important operators in mathematical morphology. Further, the231

choice of structural element should be optimum to enable better232

performance for the morphological filter [26], [27].233

Consider ds(n) as a disturbance voltage signal, defined in234

domain Dds= {x0, x1, . . . , xn}, and gs(m) be the structur-235

ing element (SE) defined in domain Dgs = {y0, y1, . . . , ym},236

where n and m are integers, such that n > m. Then dilation237

(D) and erosion (E) of ds(n) by gs(m) are given as [25]238

yD(n) = (ds ⊕ gs)(n) = max

{ds(n−m) + gs(m)

0 ≤ (n−m) ≤ n,m ≥ 0

}

(13)

yE(n) = (ds Θ gs)(n) = min

{ds(n+m)− gs(m)

0 ≤ (n+m) ≤ n,m ≥ 0

}.

(14)

Now, with the above dilation and erosion, two composite239

operations [opening (O) and closing (C)] are given as240

yO(n) = (ds ◦ gs)(n) = ((ds Θ gs)⊕ gs)(n) (15)

yC(n) = (ds • gs)(n) = ((ds ⊕ gs)Θ gs)(n). (16)

An SE is a function used as a probe to extract features,241

keeping details, and reducing noise from a signal. There is no242

specific rule for appropriate choice of SE. The length of SE243

depends upon the sampling period of the signal. If sampling244

rate increases, accordingly the length of SE will also increase,245

not strictly linearly [28]. Islanding being an online application,246

speed is always an important factor so that a quick response247

is governed in the detection of system disturbances. In a con-248

ventional opening–closing (OC) and closing–opening (CO)249

filter, there exists serious statistical deflection that influences250

noise suppression of morphological filter directly. Because of251

dissimilarity of expansion process in opening operation and252

contraction process in closing operation, overall filtering per-253

formance may not be good. In this context, in order to suppress254

noise and achieve better performance, a different SE is taken.255

To overcome this, generalized morphological filter is imple-256

mented for the detection application. The output of such filter257

Fig. 3. Hyperplane for SVM. F3:1

depends upon proper choice of SE along with morphological 258

transformation 259

FGOC(ds(n)) = ds ◦ gs1 • gs2(n) (17)

FGCO(ds(n)) = ds • gs1 ◦ gs2(n) (18)

where gs1 and gs2 are two SEs such that gs1 ⊆ gs2 . In the 260

present study, gs1 is linear and gs2 is semicircular having length 261

seven. This paper employs the difference in the generalized 262

opening and closing, open, close erosion, and dilation, in order 263

to detect any abnormality present in the signal 264

detGCO(n) = FGCO(ds(n))− FGOC(ds(n))

detOC(n) = FCO(ds(n))− FOC(ds(n))

detDE(n) = FD(ds(n))− FE(ds(n))

⎫⎪⎬

⎪⎭. (19)

Thus, the difference in generalized open and close operation 265

is implemented to detect islanding and PQ disturbances. 266

D. SVM 267

SVM utilizes a statistical learning technique which provides 268

very good accuracy in high-dimensional feature spaces. The 269

classification of patterns is based on structural risk minimiza- 270

tion method. SVM provides improved generalization perfor- 271

mance than other classical techniques, such as ANN and Bays 272

classifier because its training is supported on a sequential mini- 273

mization technique. This makes it suitable for the classification 274

of islanding and different PQ disturbances [17]. 275

For n-dimensional inputs si(i = 1, 2, . . . ,M), M is the sam- 276

ple number fitting to class1 or class2 with outputs oi = 1 for 277

class1 and oi = −1 for class2, correspondingly. The hyperplane 278

is given as 279

f(s) = wT s+ b =

n∑

j=1

wjsj + b = 0 (20)

where w is the n-dimensional vector and b is a parameter. The 280

separating hyperplane position is defined by the w and b values 281

as depicted in Fig. 3. 282

The constraints are f(si) ≥ 1, if oi = 1 and f(si) ≥ −1, if 283

oi = −1, thus 284

oif(si) = oi(wT s+ b) ≥ +1, for i = 1, 2, . . . ,M. (21)

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Fig. 4. Islanding detection event for the grid-connected wind energy system.(a) Negative sequence voltage at PCC of bus 13. (b) Detection using dB4wavelet. (c) Detection using S-transform. (d) Detection using TTT. (e) STDprofile using TTT. (f) Detection using HST. (g) Detection using mathematicalmorphology.

F4:1F4:2F4:3F4:4F4:5

Fig. 5. Islanding detection event for the grid-connected wind energy systemwith 20-dB signal to noise ratio. (a) Negative sequence voltage. (b) Detectionusing dB4 wavelet. (c) De-noised negative sequence voltage. (d) Detectionusing dB4 wavelet de-noising. (e) Detection using S-transform. (f) Detectionusing TTT. (g) STD profile using TTT. (h) Detection using HST. (i) Detectionusing mathematical morphology.

F5:1F5:2F5:3F5:4F5:5F5:6

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Fig. 6. CUSUM-based detection of VU and islanding events.F6:1

Fig. 7. Energy-based detection of islanding events.F7:1

The geometrical distance is given as ||w||−2. The opti-285

mal hyperplane can be driven by the following optimization286

problem [25]:287

Minimize1

2||w||−2 + C

M∑

i=1

ξi (22)

subject to288

oi(wT s+ b) ≥ 1− ξi, for i = 1, 2, . . . ,M (23)

ξi ≥ 0, for all i. (24)

289

The optimal bias value b∗ is given as290

b∗ = −1

2

∑

SVs

oiα∗i (v

T1 si + vT2 si) (25)

where v1 and v2 are random SVM for class1 and class2,291

respectively.292

The decision function corresponds to293

f(s) =∑

SVs

αioisTi s+ b∗. (26)

Unidentified data sample s is categorized as294

s ∈{Class− 1, f(s) ≥ 0

Class− 2, otherwise

}

. (27)

The disturbances classification is accomplished applying a295

kernel function, such as Gaussian radial basis kernel function.296

IV. RESULTS AND DISCUSSION297

Simulation results for the detection of islanding events in the298

hybrid DG system, using the above discussed techniques, are299

presented in this section under various operating scenarios. The 300

operating scenarios for the grid-connected hybrid DG system 301

are created in MATLAB/Simulink. The sampling rate in the 302

simulations is 5 kHz with 50 Hz frequency. 303

The voltage signal at PCC, at different DG locations, is 304

retrieved and processed through sequence analyzer to obtain 305

its negative sequence component. The signals are then passed 306

through the algorithms in order to detect and localize the island- 307

ing instants. The results of contour analysis by HST, TTT, 308

and mathematical morphology are compared with the results 309

obtained by using Daubechies 4 (dB4) as the mother wavelet 310

and ST. 311

The quantitative analysis in terms of energy content, STD 312

and CUSUM, of the transformed signal is also carried out 313

in order to demonstrate the efficacy of the proposed detec- 314

tion techniques. The determination of the energy content of 315

the transformed signal is based on “Parseval’s theorem,” which 316

states that the energy of a signal ds(t) remains the same whether 317

it is computed in a signal domain (time) or in a transform 318

domain (frequency) [29]. Thus, the energy and STD of a signal 319

is expressed as 320

Esignal =1

T

T∫

0

|ds(t)|2dt =N∑

n=0

|ds[n]|2 (28)

where T and N are the time period and the length of the signal, 321

respectively, and ds[n] is the Fourier transform of the signal. 322

Similarly, STD can be calculated from 323

STD =

[1

n

n∑

i=1

(ei − e)

]1/2

(29)

where n is the number of sample data and ei, e are the voltage 324

and mean of the voltage signal. CUSUM is computed by the 325

sum of the consecutive samples of the energy of the voltage 326

signal after being passed through different transforms. CUSUM 327

of voltage signal is calculated as 328

CUSUMv(p) =

r∑

1

Evq(r) (30)

where Evq is the spectral energy of the negative sequence volt- 329

age signal at PCC and “r” is the length of signal over which 330

energy and CUSUM are calculated, and “p” is the entire length 331

of the signal. 332

The islanding event is detected when energy content/ 333

STD/CUSUM becomes greater than a threshold value. The 334

results presented in the following sections will clearly reflect 335

the advantages of HST, TTT, and mathematical morphology 336

over ST and WT in the detection of disturbances. 337

A. Islanding Detection for Grid-Connected Energy Resources 338

This subsection describes the detection using above 339

described signal processing techniques. 340

Fig. 4(a) shows the extracted negative sequence voltage sig- 341

nal at PCC13 (i.e., at bus 13), where DG3 is connected, when 342

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TABLE IT1:1CLASSIFICATION OF ISLANDING AND PQ DISTURBANCES BASED ON SVMT1:2

the wind energy system is suddenly disconnected from the grid.343

It is observed that islanding event occurs at around 350 sec. The344

detection using dB4 wavelet is shown in Fig. 4(b).345

The detection using ST, TTT, HST, and mathematical mor-346

phology is shown in Fig. 4(c), (d), (f), and (g), respectively.347

These results clearly show a sudden increase in the magni-348

tudes at the instant of islanding event, thus clearly localizing349

and detecting the disturbance.350

The islanding detection in the same system with 20-dB signal351

to noise ratio is shown in Fig. 5.352

Negative sequence voltage signal at PCC, at bus 13 where353

DG3 is connected, with 20-dB noise is shown in Fig. 5(a). It354

can be observed from the graphical results of Fig. 5(b) that the355

WT completely fails in the presence of noise, whereas, Fig. 5(c)356

and (d) show the de-noised negative sequence voltage signal357

and detection using WT, respectively. After de-noising, it is358

observed that the signal contains still some noise and as a result359

the detection using WT shows some deterioration. While ST360

shows the detection of islanding with an increased contour in361

Fig. 5(e), it is accompanied with scattered characteristic, i.e.,362

deterioration in localization.363

Hence, with ST, the detection process gets affected with364

some degradation with the increase in noise level in the voltage365

signal. The presence of noise obstructs an accurate detection,366

so these two techniques (wavelet and ST) are suggested to be367

noise sensitive.368

Instead, as shown in Fig. 5(f)–(i), TTT, HST, and mathemati-369

cal morphology are capable to detect and localize the islanding370

instant effectively. The transient noise characteristic leads to371

the appearance of bulge along the TT contours. Its STD value372

shows an apparent rise at the time of the islanding event.373

The performance can also be presented quantitatively with374

the computation of performance indices: STD, energy content,375

and CUSUM [30], [31]. These features are the most distin-376

guished ones for discriminating islanding events with those of377

nonislanding scenarios. A threshold is selected by comparing 378

these indices for normal operating condition with that of the 379

islanding event. The threshold value for the configuration in 380

which all the resources (wind energy system, fuel cell, and pho- 381

tovoltaic system) are connected to the grid, is selected as 1.0145 382

for STD, 2.2314 p.u. for energy and 3.2546 for CUSUM by 383

using HST. When the STD is more than 1.0145, energy content 384

is more than 2.2314 p.u. and CUSUM is more than 3.2546, then 385

the islanding event is detected; otherwise it corresponds to a PQ 386

disturbance [18], [19]. 387

The CUSUM-based detection is also tested on the IEEE 388

30-bus system, with disturbances in the form of islanding along 389

with reduced 40% and 60% load change at PCC1 of DG1 loca- 390

tion. Fig. 6 shows the discrimination of a 2% VU from islanding 391

of DG1 location at bus 1 (including load) based on CUSUM, 392

with selection of 0.411 as threshold value. Similarly, energy is 393

considered as a common performance index in Fig. 7, which 394

displays the performance of the proposed transforms/MM along 395

with conventional techniques such as THD and VU. 396

It is clear that indices value in case of THD and VU come 397

nearer to the selected threshold, thereby increasing the chance 398

of misdetecting islanding event, whereas, the indices value in 399

case of proposed techniques come much higher with respect to 400

the threshold value, proving the improvement in discriminating 401

islanding from PQ disturbances. ST and HST show oscilla- 402

tions because of variations of frequency during disturbances 403

and being time–frequency resolution analysis. 404

To augment this, the proposed transforms and SVM-based 405

technique are used to classify islanding and different PQ dis- 406

turbances, as shown in Table I. The voltage signal is passed 407

through the transforms ST, HST, TTT, and MM, to find the fea- 408

ture dataset to be trained and tested by SVM in order to classify 409

all the disturbances. The features such as STD, energy, kurto- 410

sis, and skewness are selected and the procedure and parameters 411

cited in [1] and [17] is followed for the classification objective. 412

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Fig. 8. Boundary plot for discriminating islanding and PQ. (a) Islanding versussag with harmonics and swell with harmonics. (b) Islanding versus flicker andoscillatory transients.

F8:1F8:2F8:3

The disturbances presented in Table I are created at differ-413

ent DG locations of the IEEE 30-bus system. Again, to support414

the classification of disturbances, boundary plots are shown in415

Fig. 8, which justify the discrimination of islanding from other416

PQ disturbances. Similarly, other disturbance’s classification417

are studied using the above hybrid technique. A feature data418

matrix equal to 500× 8 is formulated for all the disturbances,419

out of which half of the dataset is used for training and the420

remaining half for the testing of the SVM classifier.421

The classification results given in Table I demonstrate that422

TTT and MM (being time–time transforms) show better accu-423

racy than the time–frequency multiresolution analyses in the424

form of ST and HST. The detection time taken by different425

classification techniques, computed on Pentium-IV, 2.8 GHz,426

1 GB RAM computer in MATLAB environment, is presented in427

Table II. It is observed that the detection time follows IEEE Std.428

1547 in all cases. The islanding event occurs at bus 1, having429

DG1 connected and 2% VU in load is considered. In addition,430

detection time involved for other disturbances such as inrush,431

flicker, oscillatory transient, sag with harmonics are also given432

in that table.433

B. Effect of Harmonics on Islanding Detection434

This section describes the effect of harmonics presence in435

the voltage signal at PCC on the islanding detection (Fig. 9).436

The harmonics in the voltage waveform may be caused due437

to the connection of nonlinear loads in the hybrid system.438

Harmonics, being a vital effect, must be eliminated because439

TABLE II T2:1DETECTION TIME OF DIFFERENT TECHNIQUES T2:2

they lead to malfunctioning and failure of equipment, over- 440

heating, misdetection and misclassification of islanding and PQ 441

problems, among others. 442

To test this effect, a nonlinear load is connected at PCC1, 443

having DG1 connected, followed by islanding at the 825 sam- 444

ple. The corresponding voltage signal is as shown in Fig. 9(a). 445

The increase in voltage in the form of a spike is observed due to 446

islanding occurrence in addition to the presence of harmonics. 447

This voltage signal is processed through WT, ST, HST, TTT, 448

and mathematical morphology to detect the islanding event, 449

shown in Fig. 9(b)–(d), (f), and (g), respectively. Fig. 9(e) 450

represents the variation in STD due to islanding using TTT. 451

The results clearly show again that islanding detection com- 452

pletely fails using WT. Similarly, the detection is degraded to 453

some extent in the case of ST. Instead, using HST, TTT, and 454

mathematical morphology, islanding detection is observed to 455

be much more effective and accurate even in the presence of 456

harmonics. 457

With increased penetration of DG, the islanding and PQ 458

issues become common occurrences that need to be ana- 459

lyzed by means of advanced signal processing techniques, as 460

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Fig. 9. Effect of harmonics on islanding detection. (a) Negative sequencevoltage. (b) Detection using dB4 wavelet. (c) Detection using S-transform.(d) Detection using TTT. (e) STD profile using TTT. (f) Detection using HST.(g) Detection using mathematical morphology.

F9:1F9:2F9:3F9:4

discussed in the introduction of the paper. The fast emergence 461

of real-time processors these advanced signal processing tech- 462

niques can be explored to accomplish disturbance detection. 463

Accordingly, various hardware platforms, such as DSP, FPGA, 464

and/or LABVIEW, can be adopted in order to validate the cor- 465

rectness of the proposed method for the detection of islanding 466

and PQ disturbance. Subsequently, the corrective action can 467

be initiated to enhance the reliability of the system, which is 468

indispensable. 469

V. CONCLUSION 470

This paper has presented a new study on the detection of 471

islanding events in a grid-connected hybrid DG system using 472

a plethora of techniques (WT, ST, HST, TTT, and mathematical 473

morphology), under various operating scenarios. The perfor- 474

mance of these approaches for islanding detection was tested 475

and compared under different operating conditions. The con- 476

tour analysis of HST/TTT and mathematical morphology tech- 477

niques demonstrated their capability to clearly detect islanding 478

events under both noise and noise-free conditions. Moreover, 479

the presence of harmonics in the voltage signal confirmed 480

the merits of HST, TTT, and mathematical morphology over 481

WT and ST for the detection and localization of islanding 482

events, which is an important characteristic. Further classifica- 483

tion is accomplished with use of SVM in order to discriminate 484

islanding with other PQ disturbances. 485

ACKNOWLEDGMENT 486

The authors acknowledge the support and facility available 487

from the Department of Electrical Engineering, Motilal Nehru 488

National Institute of Technology, Allahabad, India, for carrying 489

out the research work. 490

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597Soumya R. Mohanty (M’08–SM’13) received the 597Ph.D. degree from Indian Institute of Technology Q2598(IIT), Kharagpur, India. 599

Currently, he is an Assistant Professor with 600the Department of Electrical Engineering, Motilal 601Nehru National Institute of Technology (MNNIT), 602Allahabad, India. His research interests include dig- 603ital signal processing applications in power system 604relaying and power quality, and pattern recognition 605applications to distributed generation-based system. 606

607Nand Kishor (SM’13) received the Ph.D. degree Q3607from Indian Institute of Technology (IIT), Roorkee, 608India. 609

Currently, he is an Associate Professor with 610the Department of Electrical Engineering, Motilal 611Nehru National Institute of Technology (MNNIT), 612Allahabad, India. Since August 2012, he has been 613working as a Marie Curie Experienced Researcher 614(Marie Curie Fellow) with the Electrical Engineering 615Department, Aalto University, Espoo, Finland. His 616research interests include artificial intelligence (AI) 617

applications in power system, distributed generations, wireless sensor network, 618and digital signal processing applications in power system. 619

620Prakash K. Ray (S’10–M’12) received the Ph.D. Q4620degree from Motilal Nehru National Institute of 621Technology (MNNIT), Allahabad, India. 622

Currently, he is working as an Assistant Professor 623with the Department of Electrical and Electronics 624Engineering, International Institute of Information 625Technology (IIIT), Bhubaneswar, India. His research 626interests include distributed generations, renewable 627energy resources, digital signal processing, and soft 628computing applications in power system. 629

630João P. S. Catalão (M’04–SM’12) received the 630M.Sc. degree from the Instituto Superior Técnico Q5631(IST), Lisbon, Portugal, in 2003, and the Ph.D. degree 632and Habilitation for Full Professor (“Agregação”) 633from the University of Beira Interior (UBI), Covilha, 634Portugal, in 2007 and 2013, respectively. 635

Currently, he is a Professor with UBI and 636Researcher at the Instituto de Engenharia de Sistemas 637e Computadores—Investigação e Desenvolvimento 638(INESC-ID), Lisbon, Portugal. He is the Primary 639Coordinator of the EU-funded FP7 project 640

SiNGULAR (“Smart and Sustainable Insular Electricity Grids Under Large- 641Scale Renewable Integration”), a 5.2 million euro project involving 11 industry 642partners. He has published more than 95 journal papers, 175 conference 643proceedings papers and 12 book chapters, with an h-index of 21 (according 644to Google Scholar), having supervised more than 25 post-docs, Ph.D., and 645M.Sc. students. He is the Editor of the book entitled Electric Power Systems: 646Advanced Forecasting Techniques and Optimal Generation Scheduling 647(CRC Press, 2012) and translated into Chinese in January 2014. Currently, 648he is editing another book entitled Smart and Sustainable Power Systems: 649Operations, Planning, and Economics of Insular Electricity Grids (CRC 650Press, forthcoming). His research interests include power system operations 651and planning, hydro and thermal scheduling, wind and price forecasting, 652distributed renewable generation, demand response, and smart grids. 653

Prof. Catalão is the Editor of the IEEE TRANSACTIONS ON SUSTAINABLE 654ENERGY, Editor of the IEEE TRANSACTIONS ON SMART GRID, and 655Associate Editor of the IET Renewable Power Generation. He was the Guest 656Editor-in-Chief for the Special Section on “Real-Time Demand Response” 657of the IEEE TRANSACTIONS ON SMART GRID, published in December 6582012, and he is currently Guest Editor-in-Chief for the Special Section on 659“Reserve and Flexibility for Handling Variability and Uncertainty of Renewable 660Generation” of the IEEE TRANSACTIONS ON SUSTAINABLE ENERGY. He is 661the recipient of the 2011 Scientific Merit Award UBI-FE/Santander Universities 662and the 2012 Scientific Award UTL/Santander Totta. 663

soumya r. mohanty,senior member, ieee prakash k. ray ...webx.ubi.pt/~catalao/tste2362797.pdf ·...

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