Mahesh Babu

2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 PAPER IDENTIFICATION NUMBER-32

978-1-4244-4331-4/09/$25.00 ©2009 IEEE

A new approach to Islanding detection in Distributed Generations

S.R.Samantaray Dept. of Electrical Engineering

National Institute of Technology Rourkela-769008, India

[email protected]

Trupti Mayee Pujhari, B.D.Subudhi Dept. of Electrical Engineering

National Institute of Technology Rourkela-769008, India

[email protected],[email protected]

Abstract— This paper presents new techniques for islanding detection in distributed generations. In the proposed approach, the negative sequence component of the voltage and current at the target DG location are extracted and processed using Wavelet Transform. The detailed coefficient at the level-1 (d1) clearly localizes the event and thus detects the islanding condition. Also the change in energy and standard deviations of the detailed coefficients for one cycle current and voltage signals distinguishes islanding conditions from non-islanding ones. Also the negative sequence impedance is calculated at the target DG location and effectively detects the islanding conditions. The proposed techniques are tested on islanding and possible non-islanding conditions such as normal operation, sudden load change and tripping of other DG etc. and found to be highly effective in islanding detection at different target DG locations in the power distribution network including multiple DGs.

Keywords- Change in energy, Islanding detection, standard deviation, Negative Sequence Impedance, Wavelet Transform.

I. INTRODUCTION Integrations of Distributed Generations (DGs) in the

distribution network is expected to play an increasingly important role in the electric power system infrastructure and market. As more DG systems become part of the power grid, there is an increased safety hazard for personnel and an increased risk of damage to the power system. Despite the favorable aspects grid-connected DGs can provide to the distribution system, a critical demanding concern is islanding detection and prevention.

Islanding operation is a condition that occurs when a part of a network is disconnected from the remainder of power system but remains energized by DG units interconnected to the distribution system, which normally comprises multiple DGs with diverse technologies. Failure to trip islanded DG can lead to a number of problems for these resources and the connected loads, which includes power quality, safety and operation problems. Therefore, the current industry practice is to disconnect all DR’s immediately after the occurrence of islands [3], [4]. The disconnection is normally performed by a special protection scheme called islanding detection relays which can be implemented using different techniques.

Islanding detection techniques may be classified as passive or active. Passive techniques use information available at the DG side to determine whether the DG system is isolated from

the grid. The advantage of passive techniques is that the implementation does not have an impact on the normal operation of the DG system. Active techniques introduce an external perturbation at the output of the inverter. These tend to have a faster response and a smaller non-detection zone compared to passive approaches. However, the power quality (PQ) of the inverter can be degraded by the perturbation.

Different method for islanding detection techniques [1-12] have been reported in recent years. Some of the papers provides [2, 3] detailed review of islanding detection for DGs in distributed networks. The islanding detection based upon the rate-of-change of power signal [1], the rate-of-change of voltage and change in power factor [8], the vector surge technique [5], the rate-of-change of frequency [6], the phase-shift method [7], the harmonic impedance estimation technique [8] have attracted wide spread attention. The proposed data mining technique [11] using Decision Tree (DT) may find limitations as the thresholds depend on the splitting criteria of the corresponding DT. Thus deciding optimal splitting criteria is also a complex task as well. For ROCOF relays, the rate of change of frequency is calculated within a measurement window and used to detect islanding operation. The ROCOF relays, however, may become ineffective if the power imbalance in the islanded system is less than 15%, resulting in a high risk of false detection [6].

Recently pattern recognition technique based on Wavelet Transform [12-14] has been found to be an effective tool in monitoring and analyzing power system disturbances including power quality assessment and system protection against faults. This paper investigates the time-localization property of Wavelet transform for islanding detection by processing negative sequence components of voltage and current signals retrieved at the target DG location. As negative sequence components provide vital information in case of unbalanced conditions in power system, thus same has been considered for the proposed islanding detection technique which is subjected to disturbance during islanding process such as deviations in frequency, voltage and active power etc.

The negative sequence component of the voltage and current signals are extracted from the derived voltage and current signal at the target DG locations. The one cycle negative sequence voltage and current signal are processed through Wavelet transform (db4). The time-frequency


978-1-4244-4331-4/09/$25.00 ©2009 IEEE

2

information derived at the level-1 decompositions (d1), localizes the corresponding islanding events. Further to provide a threshold for detecting islanding conditions from non-islanding ones, the standard deviations (std) and change in energy (ce) of the d1 level coefficients for one cycle are computed.

Further to know the impact of negative sequence impedance in the islanding detection, the same is found out at the target DG location. It is observed that time variation of the negative sequence impedance provides effective islanding detection compared to non-islanding situations. Further, the standard deviation of the negative sequence impedance for one cycle data, detects the islanding conditions accurately over non-sanding ones. Thus the above two techniques based on negative sequence components provide effective islanding detection techniques, which has edge over some earlier techniques.

II. DISTRIBUTION NETWORK WITH MULTIPLE DGS

Fig. 1 The studied Power Distribution network with multiple DGs

The detailed studied system is shown in Fig. 1. The base power has been chosen as 10 MVA. The studied system consists of radial distribution system with 4 DG units (wind farms), connected to the main supply system through Point of Common Coupling (PCC). The DG units are placed at a distance of 30 km with distribution lines of pi-sections. The details of the generator, DGs, transformers, distribution lines and loads are mentioned as below.

• Generator: rated short-circuit MVA=1000, f=50 Hz, rated kV =120, Vbase = 120 kV.

• Distributed Generations (DGs): Wind farm (9 MW) consisting of six 1.5-MW wind turbines (Doubly Fed Induction Generator) is connected to a 25-kV distribution system exports power to a 120-kV grid through a 30-km 25-kV feeder.

• Transformer T1: rated MVA = 25, f = 50 Hz, rated kV = 120/25, Vbase = 25 kV, R1 = 0.00375 pu, X1= 0.1 pu, Rm= 500 pu, Xm= 500 pu .

• Transformer T2, T3, T4 and T5: rated MVA = 10, f = 50 Hz, rated kV = 575 V/ 25 kV, Vbase = 25 kV, R1 = 0.00375 pu, X1= 0.1 pu, Rm= 500 pu, Xm= 500 pu

• Distribution lines (DL): DL-1, DL-2, DL-3 and DL-4: PI-Section, 30 km each, Rated kV = 25, rated MVA = 20, Vbase = 25 kV, R0 = 0.1153 ohms/km, R1 = 0.413 ohms/km, L0 = 1.05e-3 H/km, L1 = 3.32e-3 H/km, C0 = 11.33e-009 F/km, X1 = 5.01e-009 F/km,

• Normal Loading data: L1 = 15 MW, 5 MVAR., L2, L-3, L4, L5=8.0MW, 3 MVAR.

The voltage and current signals are retrieved at the target DG location for islanding conditions and non-islanding conditions (other disturbances). The relays for each DG units are placed at the DG end. For example, the relay for CB_DG-1 is placed at DG-1 to collect the voltage and current information for both islanding and non-islanding conditions. The possible situations of islanding and non-islanding conditions studied are given as follows

• Tripping of main circuit breaker (CB) for islanding conditions.

• Opening of any breakers between the power system and DG.

• Loss of power on the PCC bus. • Sudden load change at the target DG location. • Tripping of other DGs apart from the target one.

The above conditions are simulated under possible variations in operating loading at normal, minimum and maximum loading conditions. The loads are varied at the DG end as well as at the PCC. The model is simulated at 1.6 kHz (32 samples on 50 Hz base frequency). The voltage and current signals are retrieved at the target DG location (DG-1, DG-4). The islanding starts at 0.3 sec as shown in the Fig. 2. The complete simulation is carried out using Matlab-SIMULINK software package.

0.2 0.25 0.3 0.35 0.4 0.45 0.5-1

-0.5

0

0.5

volta

ge(p

u)

0.2 0.25 0.3 0.35 0.4 0.45 0.5

-1

0

1

time in second

curren

t(pu

)

Fig. 2 Three-phase voltage and current signals under islanding condition retrieved at the target DG location (starts at 0.3 sec)

TR-3CB_DG-1 CB_D

G-2

CB_DG-4 CB_DG-3

TR-5 TR-4

TR-2

CB

~

Gen

DG-3 DG-4

DG-1 DG-2

DL-1 DL-2

DL-3 DL-4

L1

L4 L5

L2 L3

TR-1

PCC


978-1-4244-4331-4/09/$25.00 ©2009 IEEE

3

III. WAVELET TRANSFORM FOR EVENT DETECTION Given a function f(t), its continuous Wavelet Transform

(CWT) can be calculated as follows:

∫∞

∞−

⎟⎠⎞

⎜⎝⎛ −= dt

xyttf

ayxf *)(1),,(CWT ψ (1)

Where x and y are scaling (dilation) and translation (time shift) constants, respectively, and ψ is the wavelet function. Wavelet Transform of sampled waveforms can be obtained by implementing the discrete Wavelet Transform, which is given by

∑ ⎟⎟⎠

⎞⎜⎜⎝

⎛ −=k

m

m

mx xkxnkfy)(f, x,0

0

0

1 *)( DWT ψ (2)

Where the parameters x and y in (1) are replaced by mx0

and mkx0 , k and m being integer variables. In a standard DWT, the coefficients are sampled from the CWT on a dyadic grid.

Associated with the wavelet is a scaling function ( )tϕ . The scaling function along with the wavelet function creates a multi-resolution analysis (MRA) of the signal. The scaling function of one level can be represented as a sum of a scaling function of the next finer level.

( ) ( ) 2 (2 )n

t h n t nϕ ϕ∞

=−∞

= −∑ (3)

The wavelet function is also related to the scaling function by

1( ) ( ) 2 (2 )n

t h n t nψ ϕ∞

=−∞= −∑ (4)

Where )(kh and )(1 kh represent the scaling and wavelet functions, respectively, and are related as )1()1()(1 khkh k −−= (5) We can make use of the scaling function to represent the signal as

2 2( ) ( )2 (2 ) ( )2 (2 )jo j

jo jjo j

k k j joy t c k t k d k t kϕ ψ

∞ ∞ ∞

=−∞ =−∞ == − + −∑ ∑ ∑ (6)

Where jo represents the coarsest scale spanned by the scaling function. The scaling and wavelet coefficients of the signal ( )y t can be evaluated by using a filter bank of quadrature mirror filters

1( ) ( ) ( 2 )j jm

c k c m h m k∞

+=−∞

= −∑ (7)

1 1( ) ( ) ( 2 )j j

md k c m h m k

∞

+=−∞

= −∑ (8)

Equations (7) and (8) show that the coefficients at a coarser level can be attained by passing the coefficients at the finer level to their respective filters followed by a decimation of two. This will result in the number of samples in the coarser level to be approximately half of the number of samples at the finer level. For a signal that is sampled at a frequency higher than the Nyquist frequency, the samples are used as 1( )jc m+ .

The filter bandwidth and center frequency for a dyadic wavelet filter at scale k is given as

12s

k k

fB += (9)

2

32

sk k

ff += (10)

Actual implementation of DWT involves successive pairs of high pass and low pass filters at each scaling stage of Wavelet Transform. This can be thought of as successive approximations of the same function, each approximation providing the incremental information related to a particular scale (frequency range), the first scale covering a broad frequency range at the high frequency end of the frequency spectrum, however, with progressively shorter bandwidths. Conversely, the first scale will have the highest time resolution, higher scales will cover increasingly longer time intervals. While in principle any admissible wavelet can be used in the Wavelet analysis, Daubechies Wavelet (db4) is used in this work for islanding detection.

If the used scaling function and the Wavelet function from an orthogonal basis, then Parseval’s theorem relates the energy of the distorted signal to the energy in each expansion coefficients and their Wavelet coefficients. This means that the norm of energy of the signal can be partitioned in terms of expansion coefficients. The energy of the distorted signal will be partitioned at different resolution levels in different ways depending on the signals to be analyzed. The Energy of the signal is given by

22 2( ) ( ) ( )signal jk j jo k

E y t dt c k d k∞ ∞ ∞

=−∞ = =−∞

= = +∑ ∑ ∑∫ (11)

The change in energy is found out by deducting the energy content of the d1 coefficients for one cycle signal before islanding inception from the energy content of the d1 coefficients for one cycle signal after islanding inception. Similarly the standard deviation of the d1 coefficients for one cycle signal is computed for detecting islanding events from non-islanding ones. Standard deviation can be considered as a measure of the energy for a distorted signal with zero mean and is utilized in this work as a feature to detect the islanding conditions from non-islanding ones.

IV. SIMULTION RESULTS

A. Negative Sequence Component and d-1 coefficients for islanding detection

Negative sequence component is one of the key indicator in case of any disturbance conditons. Thus , in this technique, the negative sequence component of the current and voltage siglans retrieved at the target DG location are found out as

)(31 2

cban aVVaVV ++= (12)

)(31 2

cban aIIaII ++= (13)


978-1-4244-4331-4/09/$25.00 ©2009 IEEE

4

where aV , bV , cV are three phase voltages, and aI , bI , cI are three phase currents retrieved at the taget DG location, and

°∠= 1201a , is the complex operator. The negative sequence components are found out using sequence analyser block of Simulink.

The negative sequence voltage and currents are processed through Wavlet Tranfrom (db4) for time localization of the islanding event. The negative sequence current and corresponding d1 coefficints of the current signal is shown in Fig. 3 for islaning condition. The negative sequence volatge and corresponding d1 coefficients are shown in Fig. 4 for islanding condition. The d1 coefficients clearly localizes the islanding event and thus helps in detecting the same.

The comparision between islanding and non-islanidng conditions (normal operation) is given in Fig. 5. Similar comaprision between islanidng and non-islanding conditions such as sudden load change and DG line cut-off are shown in Fig. 6 and 7 respectively. It is observed that the d1 coefficients are highly pronounced in case of islanding compared to non-islaning situations. In case of 50 % load change (non-islanding) , even the d1 coefficients are highly pronounced compared to other non-islanding situations, but still a threshold will work to distinguish between islanding and non-islanding conditon.

0 50 100 150 200 250 300 350 4000

0.05

0.1

0.15

0.2

sample(time)

-ve

seq

curren

t

0 50 100 150 200-0.02

-0.01

0

0.01

0.02

sample(time)

d1

Localization

Fig. 3. The negative sequence component of current and d-1 coefficient for islanding condition

0 50 100 150 200 250 300 350 4000

0.02

0.04

0.06

0.08

sample(time)

-ve se

q vo

ltage

0 50 100 150 200-0.01

-0.005

0

0.005

0.01

sample(time)

d1

Localization

Fig.4. The negative sequence component of voltage and d-1 coefficient for islanding condition

0 50 100 150 200-8

-6

-4

-2

0

2

4

6x 10

-3

sample(time)

-ve se

q vo

ltage

at d1

islanding

normal

Fig. 5. Comparison between d-1 coefficient for islanding and non-islanding condition (normal condition)

0 50 100 150 200-8

-6

-4

-2

0

2

4

6x 10

-3

sample(time)-ve se

q vo

ltage

at d1

islanding

load change

Fig. 6. Comparison between d-1 coefficient for islanding and non-islanding condition (sudden load change- 50%)

0 50 100 150 200-8

-6

-4

-2

0

2

4

6x 10

-3

sample(time)

-ve

seq

volta

ge a

t d1

islanding

DG line cutoff

Fig. 7. Comparison between d-1 coefficient for islanding and non-islanding condition (DG line cut-off)

The complete statistics of the derived standard devitaions and change in energy of the d1 coefficients are depicetd in Table-I . It is found from the Table-I that the change in energy is 0.1225 for islanidng conditon compared to 0.0041, 5.4797e-004 and 0.00675 for non-islanding cases. Similar observations are made for standard deviation for islanding conditions. The above results are for negative sequence voltage retrieved at target DG location DG-1. Similar observations are made for negative sequence currents retrieved at same target DG location DG-1 as depicted in Table-II. Thus the change in energy and standard deviations are high valued compared to non-islanding cases and thus effective in distinguishing them. To verify the effect of changing target DG locations, the change in energy and standard deviations are found out for islanding and non-islanding situations at target DG location DG-4. It is observed from the Table-III and IV that the change in energy and standard deviations for islanding case are


978-1-4244-4331-4/09/$25.00 ©2009 IEEE

5

substantially high compared to non-islanding cases. Thus a threshold can easily be selected for detecting islanding events from non-islanding ones.

TABLE I STANDARD DEVAITIONS AT D1-COEFFICIENTS (NEAGTIVE

SEQUENCE VOLTAGE) FOR ISLANDING AND NON-ISLANIDNG SITUATIONS AT DG-1

Events Change in energy (∆E)

Change in standard deviation(std)

Islanding condition 0.1225 0.0234 Normal condition 0.0041 6.9840e-004

DG line cutoff 5.4797e-004 8.5193e-005 sudden load change 0.00675 0.00121

TABLE-II STANDARD DEVAITIONS AT D1-COEFFICIENTS (NEAGTIVE

SEQUENCE CURRENT) FOR ISLANDING AND NON-ISLANIDNG SITUATIONS AT DG-1

Conditions Change in energy((∆E)

Change in standard deviation((∆std))

Islanding condition 0.1153 0.0264 Normal condition 0.0065 0.0019

DG line cutoff 0.0013 0.0029 Sudden load change 0.0032 0.0085

TABLE III

STANDARD DEVAITIONS AT D1-COEFFICIENTS (NEAGTIVE SEQUENCE VOLTAGE) FOR ISLANDING AND NON-ISLANIDNG

SITUATIONS AT DG-4 Events Change in

energy (∆E) Change in standard

deviation(∆std) Islanding condition 0.1315 0.0127

Normal condition 0.0021 5.24550e-004 DG line cutoff 6.3167e-004 7.8934e-005

sudden load change 0.00524 0.00142

TABEL-IV STANDARD DEVAITIONS AT D1-COEFFICIENTS (NEAGTIVE

SEQUENCE VOLTAGE) FOR ISLANDING AND NON-ISLANIDNG SITUATIONS AT DG-4

Events Change in energy((∆E)

Change in standard deviation((∆std))

Islanding condition 0.1256 0.0189 Normal condition 0.0087 0.0021

DG line cutoff 0.0013 0.0018 Sudden load change 0.0032 0.0098

B. Negativ Sequence Impedance for Islanding Detection As negative seqence components of the volatage and

current signals at the traget DG loaction are highly pronounced in case of islanding situations compatred to non-islanding situations, thus the negative sequience impedance seen at the traget DG location has been computed to detect the islanding conditions. The negative seuqece impedance has been one of the key indicator in distrubance conditions such as fault process. Thus, during the islanding process, the negative sequence impedance provides vital information which can be effecetvely used for islanding detection. The negative sequence impedance can be found as

n

nn I

VZ = (14)

where nV is the negative sequence voltage and nI is the negative sequence current derived at taget DG location.

The negative sequence imepdance of the islanding versus non-islanding conditions such as normal operation is shown in Fig. 8. Similarly, negative sequence impedance comparision between islanding and non-islanding conditions such as sudden load change (non-islanding) and DG line cut-off are shown in Fig. 9 and Fig. 10, respectively. It is found that the negative sequence impedance becomes steady after islanding compared to non-islanding situations.

To further provide a thershhold for islanding detection, the standard deviations of the negative sequence imepdance for one cycle is found out and given in Table- V and VI. It is found that the standard deviation is 0.0152 compraed to 0.3551,0.2977,0.1336 for non-islanding cases at atrget DG location DG-1. To furhter know the efect of changing DG locations, the similar observations are made for standard devtaions for negative equence impadance at target DG location DG-4. It is seen that the standard deviation is very low for islanding condition compared to non-islanding ones, and thus providing a threshold effectively distinguishes the islanding events from non-islanindg conditions. It is observed that the negative sequence impedance is marginally affected when the target DG location is changed. Thus the negative sequence impedance is a potential measure for detecting islanding conditions in distributed generations.

TABLE V

STANDARD DEVAITIONS OF THE NEGATIVE SEQUENCE IMPEDANCE SEEN AT DG-1

Conditions Change in standard deviation((std)

Islanding condition 0.0152 Normal condition 0.3521

DG line cutoff 0.2977 Sudden load change 0.1336

TABLE VI

STANDARD DEVAITIONS OF THE NEGATIVE SEQUENCE IMPEDANCE SEEN AT DG-4

Conditions Change in standard deviation((std)

Islanding condition 0.0172 Normal condition 0.4123

DG line cutoff 0.3178 Sudden load change 0.1542

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

sample(time)

-ve se

q im

peda

nce

islandingnormal

Fig. 8. The negative sequence impedance comparison between islanding vs non-islanding condition (normal condition)


978-1-4244-4331-4/09/$25.00 ©2009 IEEE

6

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

samples(time)

-ve se

q im

peda

nce

islandingload change

Fig. 9. The negative sequence impedance comparison between islanding vs load change by 50% (non-islanding)

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5

3

sample(time)

-ve

seq

impe

danc

e

islanding

DG line cutoff

Fig. 10. The negative sequence impedance comparison between islanding vs DG line cut-off

V. DISCUSSION

The proposed research investigates the potential of negative

sequence components of voltage, current and impedance for islanding detection in distributed generation in power distribution network. It is observed that the d1 coefficients of the negative sequence current and voltage is highly pronounced in case of islanding events compared to non-islanding ones. But in case of 50% load change (considered as non-islanding condition), the d1 coefficients are more close to islanding condition compared to other non-islanding conditions and still a threshold can separate them. But 50% load change is one of the extreme non-islanding conditions, where ROCOF fails to detect islanding. Thus improved results are obtained with the proposed approach.

The second approach uses negative sequence impedance for islanding detection. Generally, the negative sequence impedance is one of the potential parameter for detecting unbalanced conditions in the power systems. Thus the potential of negative sequence impedance has been investigated for islanding detection. It is observed that the negative sequence impedance for islanding and non-islanding cases is clearly separable and thus able to detect islanding events accurately.

VI. CONCLUSIONS The proposed technique investigates the negative sequence

component of voltage, current and impedance for islanding detection in distributed generations. Wavelet transform is used

to process the negative sequence voltage and current signals and the d1 coefficients clearly detect the islanding events from non-islanding ones. Further, the change in energy and standard deviation of d1 coefficients for one cycle signal data is found out which clearly detects the islanding conditions. Also the negative sequence impedance is found out for both islanding and non-islanding events, and it is observed that the standard deviation of the negative sequence impedance of islanding event is very low compared to non-islanding condition, thus able to detect the islanding events effectively. Thus the proposed methods are highly effective for islanding detection with extreme operating situations of the Power distribution network with multiple DG interface.

REFERENCES [1] M. A. Redfern, O. Usta, “A new microprocessor based islanding

protection algorithm for dispersed storage and generation units,” IEEE Trans. Power Delivery, vol. 10, pp. 1249-1254, July 1995.

[2] T. Funabashi, K. Koyanagi and R. Yokoyama, “A review of islanding detection methods for distributed resources,” IEEE Bologna Power Tech Conference, Bologna, Italy, Vol. 2, pp. 23-26, June 23-26, 2003.

[3] 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 July 2004.

[4] S. K. Salman, D. J. King, G. Weller, “New loss of mains detection algorithm for embedded generation using rate of change of voltage and changes in power factors,” Developments in Power System Protection Conference, IEE Publication No. 479, pp. 82-85, 2001.

[5] W. Freitas, Z. Huang, and W. Xu, “A practical method for assessing the effectiveness of vector surge relays for distribute generation applications,” IEEE Trans. Power Delivery, vol. 20, pp. 57-63, Jan. 2005.

[6] W. Freitas, W. Xu, C. M. Affonso, and Z. Huang, ‘‘Comparative analysis between ROCOF and vector surge relays for distributed generation applications,’’ IEEE Trans. Power Del., vol. 20, no. 2, pt. 2, pp.1315---1324, Apr. 2005.

[7] G. Hung, C. Chang and C. Chen, “Automatic phase-shift method for islanding detection of grid-connected photovoltaic inverter,” IEEE Trans. Energy Conversion, vol. 18, pp. 169-173, Mar. 2003.

[8] M. Sumner, B. Patethorpe, D. W. P. Thomas, P. Zanchetta and M. C. D. Piazza, “A technique for power supply harmonic impedance estimation using a controlled voltage disturbance,” IEEE Trans. Power Electronics, vol. 17, pp. 207-215, Mar. 2002.

[9] J.C. Vieira, W. Freitas, W. Xu, A.Moriduch, L. elato, “Performance of Frequency Relays for Distributed Generation protection”, IEEE Trans. On Power Delivery, vol.21, no.3, pp.1120-1127, July-2006.

[10] J.Yin, C.P. Diduch, L. Chang, “ Islanding detection using proportional power spectral density” IEEE Trans. On Power Delivery, vol.23, no.2, pp.776-784, Apri-2008.

[11] El-Arroudi, K.; Joos, G.; Kamwa, I., McGillis, D.T., “Intelligent-Based Approach to Islanding Detection in Distributed Generation”; IEEE Transactions on Power Delivery, Volume 22, Issue 2, pp.828 – 835, April 2007

[12] O.A.S. Youssef, New algorithms for phase selection based on wavelet transforms, IEEE Trans. Power Deliv. 17 (2002) 908---914. [13] A.H. Osman, O.P. Malik, Transmission line protection based on wavelet transform, IEEE Trans. Power Deliv. 19 (2) (2004) 515---523. [14] D. Chanda, N.K. Kishore, A.K. Sinha, A wavelet multi-resolution

analysis for location of faults on transmission lines, Electr. Power Energy Syst. 25 (2003) 59---69.

Documents

Mahesh Babu