smart grid islanding, fault detection and …€¦ · narayana presented a scheme on a priority...

http://www.iaeme.com/IJMET/index.asp 903 [email protected]

International Journal of Mechanical Engineering and Technology (IJMET)

Volume 10, Issue 04, April 2019, pp. 903–916, Article ID: IJMET_10_04_090

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication Scopus Indexed

SMART GRID ISLANDING, FAULT DETECTION

AND CLASSIFICATION WITH DISTRIBUTED

GENERATION BASED ON WAVELET

ALIENATION CURRENT SIGNALS APPROACH

Kamala Devi Kolavennu

Department of Electrical and Electronics Engineering

Bapatla Engineering College, Bapatla, Andhra Pradesh, India

Abdul Gafoor Shaik

Department of Electrical Engineering

IIT- Jodhpur, Rajasthan, India

ABSTRACT

The Distributed generation (DG) is gaining significant attention due to increase in the

demand for electricity. Distributed generations are mostly used with the association of power

distribution systems to energize local loads and network. Islanding is technically an

undesirable condition that demands necessary steps to reduce negative effects in maintaining

stability of the system. In the present work, alienation technique has been applied to detect

and differentiate islanding, faults and sudden load change and faults have been classified. A

radial system with four Distributed Generations (DFIG wind generator) connected to the

source through common coupling point (PCC) has been used for comprehensive study of this

technique. Current signals were decomposed with Daubechies (db1) wavelet in order to get

approximate coefficients at each bus. Coefficients of Alienation are computed by using the

wavelet based approximations over a length of half cycle moving window . The alienation

coefficients were used to compute Islanding index and fault index. The same indices were

compared with threshold to differentiate Islanding, faults and sudden load change. The

proposed algorithm has been tested for various incidence angles for both islanding condition

and faults. This technique is established to be robust in detecting islanding condition, faults

and impact of sudden load change.

Key words: Distributed generation, Alienation coefficients, Distribution network,

load change, Islanding index, Wavelet transform

Cite this Article: Kamala Devi Kolavennu and Abdul Gafoor Shaik, Smart Grid

Islanding, Fault Detection and Classification with Distributed Generation Based on

Wavelet Alienation Current Signals Approach, International Journal of Mechanical

Engineering and Technology 10(4), 2019, pp. 903–916.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=4

Smart Grid Islanding, Fault Detection and Classification with Distributed Generation Based on

Wavelet Alienation Current Signals Approach


1. INTRODUCTION

Penetration of the distributed generation in power distribution networks increases rapidly in

the present days. Distributed generation (DG) is also known as non-centralized power

generation or dispersed generation which refers to the production of Electricity near the

consumption place. The resources used in distributed generation are renewable energy sources

and cogeneration i.e., production of heat and electricity simultaneously. DGs are having some

advantageous characteristics like free location in the network area, relatively small generated

power, increased reliability of the Grid, reduced transmission and distribution line losses,

better voltage support and power quality improvement[1].

The standby generation, peak shaving, utility grid enhancement, load management are

various applications of the distributed generation. However many technical issues and

difficulties arise in the association of DG units with the Grid. Islanding condition is one of the

most undesirable conditions intentionally or accidentally takes place in DGs. According to

IEEE STD 1547-2003, in islanding condition, there is accidental or intentional disconnection

of the grid from distribution network system and the distributed generation continuously feed

the network and local loads[2]. Due to this Islanding condition, various operational problems

arise related to power quality, safety hazard, voltage and frequency instability and damage to

system equipment [3, 4]. The unsynchronized reclosing of the grid to distribution system

may trigger damage to the power electronic conditioning equipment of the distributed

generation. The standard of IEEE 929-2000 specifies particularly the disconnection of

distributed generation once it is islanded [5] and this issue is addressed by IEEE STD 1547-

2003 [6].Islanding detection techniques are broadly classified into remote (central) and local

techniques. A detailed review on islanding has been given by Khamis, Aziah [7]. Remote

methods are associated with islanding detection methods on the utility side where as local

methods are associated with detection of islanding on DG side. Remote methods are used to

detect unintentional islanding by monitoring voltage and frequency parameters [8]. Remote or

central methods are independent of number of inverters, generator type, and system size and

penetration level. Local techniques for islanding detection are based on the measurement of

the system parameters on DG side such as voltage, frequency, current and harmonic distortion

[9]. Comparative analysis of anti islanding technique depends on application and cost and

ability of occurrence of islanding on feeder has been presented by P. Deshbhratar, R.

Somalwar and S. G. Kadwane [10]. Local Islanding detection techniques are further classified

into passive and active technique methods, and hybrid technique methods [11, 12]. Anti-

islanding technique method has been introduced by J. Yim, C.P.Diduch, L. Chang on

the basis of proportional power spectral density as a formalised measure [13]. A new

approach to islanding detection by extracting the negative sequence component of the current

and voltage has been presented by S. R Samantha Ray and Trupti Mayee Pujhari[14]. K.

Narayana presented a scheme on a priority based load shedding to detect islanding in case of

multiple DG units [15].Ahmad G. AbdElkadar reported a new islanding technique for DFIG

wind turbine by using artificial neural network [16]. R. K. Ray, N. Kishore [17] proposed a

wavelet and S-transform based scheme by considering a negative sequence component of the

voltage signal extracted at point of common coupling.J. A. Laghari [18] developed an

islanding detection technique by using average rate of change of reactive power and load shift

strategy.

The effectiveness of different fault detection methods were studied by S. S. Gururajapathy

H. Mokhilis like fuzzy logic, artificial intelligence, , Genetic algorithm etc, [19].

Transmission line protection scheme based on Wavelets are proposed by Abdul Gafoor and

Ramanarao [20]. Rathore and Shaik has proposed the protection schemes for transmission line

from faults with the use of wavelet based alienation technique [21, 22].A protection scheme

Kamala Devi Kolavennu and Abdul Gafoor Shaik


has been introduced by Masoud and Mahfouz for transmission lines based on alienation

coefficients [23].

In deed there is a need to investigate a protective scheme which requires fast detection

with less computational complex. In this present work the proposed algorithm can detect

islanding and faults within quarter cycle.

2. THE ALIENATION ALGORITHM BASED ON WAVELETS

2.1. The Wavelet Transform

There is effective use of Wavelet transform to analyse transients of current and voltage

signals in frequency as well as time-band. To decompose the signal in high and low frequency

bands a set of basic functions called Wavelets, are used, which are acquired from a mother

wavelet by dilation and translation. Therefore, the incidence of each frequency and amplitude

can be found precisely. The Wavelet Transform is explained as a series of a function h (n)

(high pass filter) and g (n) (low pass filter). The scaling and wavelet functions can be

described in equations as follows.

( ) √ ∑ ( ) ( )(1)

( ) √ ∑ ( ) ( ) (2)

Where, g (n) = (-1) n

h (1-n)

The factor √2 maintains the norm of the function for the time compression factor 2. The

time compression factor is generally correlates to the scale. The selection of mother wavelet is

based on the type of application. This paper used Daubachies wavelet which is suited for this

application to get best results.

2.2. Alienation Coefficients

In the suggested algorithm the current signals have been sampled over a half cycle.

Approximation coefficients are acquired by applying wavelets to these current signals The

alienation coefficient based on approximation decomposition (Coefficients) is calculated as:

(3)

Where, pa is the coefficient of correlation calculated based on approximations. The

correlation coefficient based on approximation coefficient is calculated as

(∑ ) (∑ ∑ )

√ ∑ (∑ )

∑

(∑ ) (4)

Where, Ns is the number of samples per half cycle, absolute value of samples at t0 is

denoted by ra .Absolute value of samples consider in previous moving window of half cycle is

denoted by sa. The variance between two signals is defined as the alienation coefficient. Its

value is between 0 and 1.

2.3. Weighted alienation coefficients

It is needed to apply the concept of weighted alienation coefficients to detect the transients of

faults at PCC when faults are computed at DGs and for load changes. Simple arithmetic gives

equal importance to all values in a series. In some cases, all the values in a series do not give

same weight age. In such cases weighted average is more suitable for calculations. The

concept of average of weighted Alienation coefficients is as follows. It is used to increase the

relative importance of any quantity (of our interest) with respect to other quantities. For which




each value is multiplied by a weight according to its importance. The weighted average for

any input x can be computed by using the following equation (5) and procedure. 5

1

i

ix avg WiXi

(5)

i= Total number of inputs.

Xi is alienation coefficient at each bus of four DGs and PCC where, i = 1, 2,3,4,5.

Get Max (x1, x2, x3, x4, x5) = n.

Divide each alienation value of DGs and PCC by n to get weights. The weights will be

xi/n=wi where i=1 to 5.

Multiply every alienation coefficient of four DGs and PCC with their respective weights

(wi).Then get average of these values for all three phases.

Figure 1. Line diagram of the system

The single line diagram of proposed system is shown in Fig. 1. The base power is about

10 MVA. This system contain radial distribution system with 4 wind farms (DG units), which

connected through Point of Common Coupling (PCC) to the main supply system and it

operates at a power frequency of about 50 Hz. These DG units are connected at about a

distance of 30 km with the distribution lines of pi-sections. The data of the DGs, generator,

distribution lines and loads and transformers are considered from Ref. [17].

The three phase currents at a point of common coupling of distribution line and DGs are

sampled at 6400 Hz. These samples are acquired over a moving window of half cycle length.

These current samples have been decomposed with a db1 wavelet to get approximation

coefficients of third level (A3). CA, the Alienation coefficient is evaluated by comparing the

approximate coefficients of the current window, with those of the previous window of same

polarity.



These two consecutive windows, under normal conditions, have similar set of

approximations, hence the Aa remains zero. But in the case of islanding, fault or any other

abnormal condition, the approximate coefficient of the current window should differ from

those of previous window of same polarity. Hence, the alienation coefficient would increase

from zero to a certain value indicating disturbances.

3. PROPOSED ALGORITHM

Table 1 Parameters adopted in the present work

S.NO COMPONENT SPECIFICATION

1. Generator Rated short circuit MVA=1000

Rated KV=120,Vbase=120kv,f=50Hz

2. Distributed generations(DGs)

(DG-1 to DG-4)

Six Doubly fed induction generators

(9MW) of each wind turbine of rating1.5MW

are joined to a 25kv grid through

a length of 30km, 25 kv feeder.

3.

Distribution lines 1 to 4

PI-Section,30km each, rated MVA=

20, Rated KV=25kv,Vbase=25kv, R0=

0.11530Ω/km,R1=0.4130Ω/km,L0=

1.05e-3H/km, L1=3.32e-3H/km,

C0=11.33e-009F/km,X1=5.01e-009

F/km.

4. Transformer T1

Rated MVA=25,Vbase=25kv,Rated

KV=120/25, X1=0.10p.u, R1=0.003750

p.u,Rm=500.00p.u, Xm=500.00 p.u.

5. TransformerT2 to

Transformer T5.

Rated MVA=10.00,rated kv=575v/25kv,

Vbase=25KV, X1=0.10, R1=0.003750

p.u, Rm=500.00 p.u, Xm=500.00 p.u,f=50hz

6. Load L-1 15MW,5MVAR.

7. Load L-2 to Load L-5 8.0MW,3MVAR.




Figure 2. Flow chart of the proposed algorithm



4. RESULTS AND DISCUSSION

4.1. Comparison of islanding and load change

4.1.1. Detection of islanding

As per Fig.1 the single line diagram is modelled in Matlab / Simulink environment. The flow

chart of the present work is shown in Fig 2. The 6400 Hz sampling frequency is considered

with 128 samples per cycle. The simulation has been carried out for 25 cycles and run for 0.5

sec (25 cycles) and fault islanding and load change were simulated after 20 cycles (at 0.4sec).

The detection of islanding is illustrated in Fig.3 at various DGs by opening circuit breaker at

0.4sec at PCC. In islanding, fault and load changing the islanding index and fault indexes are

compared with the threshold value. The threshold value is set as 0.01 in case of islanding and

load change. Further, a threshold value of 0.4 at DGs and 0.15 at PCC were suitably

considered for detection and classification of faults. It has been observed that the islanding

index of islanding at every DG is greater than that of threshold and islanding index of load

change at every DG is found to be lesser than the threshold. The fault index was observed as

higher than the threshold for the fault and lower than the threshold for islanding. Fig.3(a), (b),

(c), (d) illustrate variation of islanding index above the threshold which indicate islanding

condition at DG-1, DG-2, DG-3, DG-4 for current signals which satisfies proposed algorithm.

Figure 3.Variation of islanding indexes with time at a) DG-1 located at 30km from PCC, b) DG-2 located at

60km from PCC, c) DG-3 located at 30km from PCC and d) DG-4 located at 60km from PCC.

4.1.2. The Variation of islanding incidence angle

The proposed algorithm has been tested at regular intervals of 300

by applying Islanding.

Variation of islanding indexes of three phases with incidence angle has been illustrated in Fig.

4. It is evident from graph that the islanding index is always greater than the threshold one for

various incidence angles at PCC which shows islanding condition.




Table 2 Islanding Incidence Angle

Incidence Angle PHASE A PHASE B PHASE C I-TH

0° 0.03466 0.0144 0.0222 0.01

30° 0.033375 0.0255 0.0319 0.01

60° 0.013 0.0189 0.0369 0.01

90° 0.038 0.0255 0.04215 0.01

120° 0.0155 0.023 0.01442 0.01

150° 0.0331 0.0222 0.039 0.01

180° 0.0331 0.01422 0.02312 0.01

Figure 4.Variation of islanding index for different incidence angle at four DGs.

4.1.3. The Load changing

The effect of sudden load change at DG-1 on distribution network is observed. The effect of

load change is shown in Fig.5 in a distribution line at DG-1 at 0.4sec through a circuit

breaker. System at different levels of load increment on 3MVAR and 0.8 power factor base

has been added to the existing system load at similar time of 0.4 sec. Fig5

(a),5(b),5(c),5(d),5(e) show the load change of 5% , 10%,20%, 25% and 30% and it is

observed that the magnitude of disturbance during load change is found to be less than that of

threshold.

4.2. Comparison of Islanding and faults

4.2.1. Detection of islanding

Fig.6 demonstrates the detection of islanding at various DGs with threshold value 0.15 by

opening circuit breaker at 0.4sec at PCC. In order to compare islanding and fault , the fault

index value is set as 0.15. The transients of islanding occurred in between 0.01 and 0.15.

Islanding index and fault indexes are compared with the threshold value. It has been observed

that the fault index of islanding at every DG is lower than the threshold and fault index of

faults were observed as greater than the threshold value. Fig.6(a), (b), (c), (d),(e),(f) illustrate

variation of islanding index below the threshold which indicate islanding condition at DG-1,

DG-2, DG-3, DG-4.

0

0.01

0.02

0.03

0.04

0.05

0° 30° 60° 90° 120° 150° 180°

Wei

ghte

d a

lien

atio

n c

oef

fici

ents

Incidence angle

A B C I-TH



Figure 5. The effect of load changing in a distribution line at DG-1: (a). 5% load changing at DG-1,

(b). 10% load changing at DG-1, (c). 20% load changing at DG-1, (d). 25% load changing at DG-1,

(e). 30% load changing at DG-1

Figure 6.Variation of islanding indexes with time at a) DG-1 located at 30km from PCC, b) DG-2

located at 60km from PCC, c) DG-3 located at 30km from PCC and d) DG-4 located at 60km from

PCC, e) Variation of Islanding index with time at DG-3 located at 35km from PCC and f) Variation of

Islanding index with time at DG-2 located at 50km from PCC.




4.2.2. Variation of islanding incidence angle

Islanding condition has been tested at regular interval of 300. The variation of islanding

indexes of three phases with incidence angle has been illustrated in Fig.7. It is evident from

the graph that the islanding index is always lower than the threshold one for various incidence

angles which shows the islanding condition

Figure 7. Variation of islanding indexes with islanding incidence angles at DG-1, b) DG-2, c) DG-3

and d)DG4

4.2.3. Detection and classification of faults

Figure 8 (a) AG fault at DG1 and (b) AG fault at PCC

Figure 9 (a) ABG fault at DG2 and (b) ABG fault at PCC



Figure 10 (a) AB fault at DG3 and (b) AB fault at PCC

Figure 11 (a) ABG fault at DG4 and (b) ABG fault at PCC

Simulation of faults has been done after 20-cycles for obtaining post fault transients for 5-

cycles. The performance of the proposed algorithm for different faults at a distance of 30 km

from PCC and DGs from each other at various DGs and PCC is demonstrated in Fig.8 to

Fig.11.

Fig.8 shows variation of fault indexes of three phase currents with time at DG1 and PCC.

Transients due to faults at PCC occurred in between0.15 to 0.4 and at DGs transients obtained

above 0.4. Hence .Threshold value for identification of faults and their classification ihas

been set at 0.15 at PCC and 0.4 at four DGs. It has been observed from Fig. 8 (a) and (b) that

the fault index of phase A is greater than that of the threshold and for those of phase B and

phase C are lower than the threshold (F-TH). Thus the suggested scheme is identified and

classified as single line to ground fault (AG) at DG-1 and PCC. It has been illustrated from

Fig. 9 (a) and (b) that the fault index of phase A and phase B are above the threshold and that

of phase C is found to be below the threshold. Thus this fault is identified and classified as

ABG fault at DG-2 and its respective PCC. Fig.10(a) and (b) illustrate that the fault index of

both phase A and phase B are above threshold and phase C is below the threshold. Thus fault

is recognized as AB fault at DG-3 and its PCC. Fig.11(a) and (b) show that the fault index of

all the three phases are above threshold which is acknowledged as three phase fault. From the

graphs it is observed that the healthy phase never cross the threshold value and faulty phase

crosses the threshold value. Once fault index of any phase is greater than the threshold value,

it is considered as faulty phase even though the fault index is lower than the threshold for a

moment after detecting the fault. In the present work, fault was detected within quarter cycle

at every DG and PCC.




4.2.4. Variation of the fault incidence angles

To test the suggested algorithm at equal intervals of 300 faults have been simulated. The

difference of fault indexes of the three phases with the angle of fault incidence has been

illustrated in Fig 12. It is apparent from Fig 12(a) that the fault index of the faulty phases A

and B are greater than that of threshold for various angles of fault incidence for AB fault at

DG-1. It is illustrated from the Fig12 (b) to Fig 12(e) that the fault index of the faulty phase is

higher than that of threshold value and fault index of healthy phase is lower than the threshold

value for different fault incidence angles for AG fault atDG-2,ACG fault at DG-3, ABG fault

at DG-4 and ABCG fault at PCC.

Figure 12.Variation of fault index with fault incidence angle: (a) AB fault at DG-1, (b)AG fault at

DG-2, (c)ACG fault at DG-3, (d) ABG fault at DG-4, (e) ABCG fault at PCC.

Table 3. Fault detection time at different DGs for various faults

TYPE OF

FAULT

PHASE A PHASE B PHASE C

IS FAULT

PRESE

NT

DETECTI

ON

TIME(SE

C)

IS

FAULTPR

ESENT

DETECCTI

ON

TIME(SEC)

IS

FAULTP

RESENT

DETECTI

ON

TIME(SE

C)

AG atDG-1 YES 0.0025 NO N.A NO N.A

ABG at DG-2 YES 0.00125 YES 0.00125 NO N.A

AB at DG-3 YES 0.00375 YES 0.00125 NO N.A

ABCG at DG-4 YES 0.0025 YES 0.0025 YES 0.0025

The above table-3 and table-4 illustrate that the time required detecting fault from

incidence in seconds at DGs and PCC. From table-1 and table-2, it is clear that the fault is

detected in 0.0025 sec from the incidence at both DG-1 and PCC in case of AG fault. It is

evident that the fault is detected within the quarter cycle in all types of faults at both DGs and

corresponding PCC.



Table 4. Fault detection time at PCC during the faults at various DGs

TYPE OF

FAULT

PHASE A PHASE B PHASE C

IS FAULT

PRESEN

T

DETECTI

ON

TIME(SE

C)

IS

FAULTPR

ESENT

DETECTIO

N

TIME(SEC)

IS FAULT

PRESEN

T

DETECTI

ON

TIME(SE

C)

AG at DG-1 YES 0.0025 NO N.A NO N.A

ABG at DG-2 YES 0.0025 YES 0.00125 NO N.A

AB at DG-3 YES 0.005 YES 0.00125 NO N.A

ABCG atDG-

4 YES 0.00375 YES 0.0025 YES 0.005

5. CONCLUSION

The suggested algorithm investigates the successful implementation of the wavelet transform

based alienation coefficient approach for effective detection of faults, islanding, and load

changing and further their comparison in the distribution system with the penetration of DGs.

Alienation coefficients over a quarter cycle clearly detect and localize the event. It has been

found that the islanding is greater than threshold value and load changing in a distribution line

is very less than the threshold. Faults are found to be greater than threshold value and

islanding condition is observed to be lower than the threshold value. Therefore, the proposed

method is successful, fast and reliable for the detection of faults, islanding, and load change.

REFERENCES

[1] Dulău, Lucian Ioan, MihailAbrudean, and DorinBică "Effects of distributed generation on

electric power systems" Procedia Technology 12 (2014): 681-686.

[2] Kroposki, Benjamin, Thomas Basso, and Richard DeBlasio "Microgrid standards and

technologies’’ In Power and Energy Society General Meeting-Conversion and Delivery of

Electrical Energy in the 21st Century, 2008 IEEE, pp.1-4. IEEE, 2008.

[3] Conti, Stefania "Analysis of distribution network protection issues in presence of

dispersed generation" Electric Power Systems Research 79, no. 1 (2009): 49-56.

[4] Ropp, Michael E., MiroslavBegovic, AjeetRohatgi, Gregory A. Kern, R. H. Bonn, and S.

Gonzalez "Determining the relative effectiveness of islanding detection methods using

phase criteria and nondetection zones" IEEE transactions on energy conversion 15,(2000):

290-29.

[5] Recommended “Practice for Utility Interconnected Photovoltaic (PV) Systems”, IEEE

Standard 929-2000, 2000.

[6] IEEE “Standard for Interconnecting Distributed Resources into Electric Power Systems”,

IEEE Standard 1547TM, June 2003.

[7] Khamis, Aziah, HussainShareef, ErdalBizkevelci, and Tamer Khatib "A review of

islanding detection techniques for renewable distributed generation systems" Renewable

and sustainable energy reviews 28 (2013): 483-493.

[8] Mahat, Pukar, Zhe Chen, and BirgitteBak-Jensen. "A hybrid islanding detection technique

using average rate of voltage change and real power shift." IEEE Transactions on Power

delivery 24, no. 2 (2009): 764-771

[9] Trujillo, César, David Velasco, Emilio Figures, and Gabriel Garcerá. "Local and remote

techniques for islanding detection in distributed generators" In Distributed Generation.

InTech, 2010.

[10] P. Deshbhratar, R. Somalwar and S. G. Kadwane, "Comparative analysis of islanding

detection methods for multiple DG based system," 2016 International Conference on

Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai,2016,pp.1525-

1530.




[11] Funabashi, Toshihisa, KaoruyKoyanagi, and R. Yokoyama. "A review of islanding

detection methods for distributed resources." In Power Tech Conference Proceedings,

2003 IEEE Bologna, vol. 2, pp. 6-pp. IEEE, 2003.

[12] Mahat, Pukar, Zhe Chen, and BirgitteBak-Jensen. "Review of islanding detection methods

for distributed generation." In Electric Utility Deregulation and Restructuring and Power

Technologies, 2008. DRPT 2008. Third International Conference on, pp. 2743-2748.

IEEE, 2008.

[13] J. Yin, L. Chang, and C. Diduch. “Recent development in islanding detection for

distributed power generation,” Large engineering Systems Conference on Power

Engineering” (LESCOPE), pp. 124-128, 28-30 July2004.

[14] S.R.Samantaray, TruptiMayeePujhari, B.D.Subudhi. "A new approach to Islanding

detection in Distributed Generations",2009 Third International Conference on Power

Systems, Kharagpur,27-29, 978-1-4244-4331.

[15] Narayanan, K., Shahbaz A. Siddiqui, and ManojFozdar. "An Improved Islanding

Detection Technique and priority based load shedding for distribution system with

multiple DGs." In Power Systems Conference (NPSC), 2016 National, pp. 1-6. IEEE,

2016.

[16] Abd-Elkader, Ahmad G., Dalia F. Allam, and ElsayedTageldin. "Islanding detection

method for DFIG wind turbines using artificial neural networks" International Journal of

Electrical Power & Energy Systems 62 (2014): 335-343.

[17] Ray, Prakash K., NandKishor, and Soumya R. Mohanty. "Islanding and Power Quality

Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $ S

$-Transform." IEEE Transactions on Smart Grid 3, no. 3 (2012): 1082-1094.

[18] Laghari, J. A., H. Mokhlis, M. Karimi, A. H. A. Bakar, and Hasmaini Mohamad. "An

islanding detection strategy for distribution network connected with hybrid DG

resources." Renewable and Sustainable Energy Reviews 45 (2015): 662-676.

[19] Gururajapathy, S. S., H. Mokhlis, and H. A. Illias. "Fault location and detection

techniques in power distribution systems with distributed generation: A review."

Renewable and Sustainable Energy Reviews 74 (2017): 949-958.

[20] Abdul GafoorShaik, P.V. RamanaRao, Wavelet based fault detection, classification and

locationin transmission lines, Proc. 2006 IEEE Power and Energy Conference (2018)

114–118.

[21] BhuvneshRathore, Abdul GafoorShaik, Fault detection and classification on transmission

line using wavelet based alienation algorithm, Smart Grid Technologies-Asia (ISGT

ASIA), 2015 IEEE Innovative, IEEE (2015) 1–6.

[22] Bhuvnesh Rathore, Abdul Gafoor Shaik, Wavelet-alienation based transmission line

protection scheme, IET Gener. Transm. Distrib. 11 (4) (2017)995–1003.

[23] Masoud, M. E., and M. M. A. Mahfouz. "Protection scheme for transmission lines based

on alienation coefficients for current signals." IET generation, transmission & distribution

4, no. 11 (2010): 1236-1244.

smart grid islanding, fault detection and …€¦ · narayana presented a scheme on a priority...

Documents