1

Abstract— A differential energy based fault protection in

microgrid is presented in this paper. Initially the currents at the respective buses are retrieved and processed through a novel time-frequency transform known as S-transform to generate time-frequency contours. Spectral energy content of the time-frequency contours of fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid-connected and islanded mode. The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and High Impedance Faults (HIF) in the microgrid with radial and loop structure. The results based on extensive study indicate that the differential energy based protection scheme can reliably protect the microgrid against different fault situations.

Index Terms—Differential energy, Microgrid protection, Shunt Faults, High Impedance Faults (HIF), Time-frequency contours, Fault patterns.

I. INTRODUCTION icro-grid implementations present an effective means of distributing high quality power more efficiently to residential, urban and rural areas and to commercial

facilities. They will enable Distributed Energy Resources (DER) usage worldwide at various levels of power delivery and increase the efficiency with multiple renewable energy sources such as photo-voltaic cells, fuel cells and wind power, which can provide aggregate power to a group of loads. In general, a microgrid can operate in both the grid-connected mode and islanded mode where the microgrid is interfaced to the main power. It is essential to protect a microgrid in grid-connected and the islanded modes of operations against all types of faults.

Different techniques have been proposed for microgrid protection against faulty situations. It is found [1, 2] that fault currents in microgrid and islanded mode are significantly different and protection measure needs to be a robust one for

S.R.Samantaray is with School of Electrical Sciences, Indian Institute of Technology Bhubaneswar-751013 (e-mail: sbh_samant@yahoo.co.in). Geza Joos are with the Department of Electrical and Computer Engineering, McGill University, McConnell, 633, 3480 University Street, Montreal, Quebec, H3A 2A7, Canada (geza.joos@mcgill.ca). I. Kamwa is with the Hydro-Quebec/IREQ, Power System Analysis, Operation and Control, Varennes QC J3X 1S1, Canada (e-mail:kamwa.innocent@ireq.ca)

reliable decision making. In another work [3], sequence components are derived to design the protection and relay coordination. However, the relay coordination depends upon multiple settings on the derived sequence components and making scheme more complex. Microgrid protection using voltage protection [4] uses synchronous reference frame to compare with the reference of the phase voltage at the DG resource. Another technique uses over current differential protection [5] on each line with back up voltage and frequency protection at each DG. Moreover, the above mentioned approaches are suitable for solid shunt (L-G, LL-G) faults and fail to detect High Impedance Faults (HIF). Recently a microgrid protection scheme using communications assisted digital relays [6] has been proposed. This scheme is based on differential current approach where time-synchronization is avoided. However, no time-synchronization will have serious impact as differential current is the key indicator for issuing tripping signal. To overcome such type of problems, a differential energy based microgrid protection scheme is proposed, which is less sensitive to synchronization error compared to time-domain data difference such as differential currents.

The proposed technique develops a differential energy based protection scheme for the microgrid using time-frequency transform such as S-transform. The scheme can viewed as a pattern recognition approach for microgrid protection. The S-transform [8-10] is an invertible time-frequency spectral localization technique that combines elements of wavelet transforms and short-time Fourier transform. The S-transform has an advantage in that it provides multiresolution analysis while retaining the absolute phase of each frequency. Compared to S-transform, the other time-frequency transform technique such as Wavelet transform finds limitations as wavelet transform is highly prone to noise and provides erroneous results even with noise of SNR 30 dB [7].

In the proposed technique, fault currents at both ends of the respective feeders in a microgrid, are retrieved and processed through modified S-transform to find out the spectral energy content. The difference in spectral energy (differential energy) content of time-frequency contours of both signals is the key indicator for registering fault patterns in the microgrid. Further, the tripping signal can be issued based on a set threshold on the differential energy for different fault situations in microgrid. The proposed technique provides highly promising results on extensive study carried out during testing including radial and loop network structures in both

Differential Energy based Microgrid Protection against Fault conditions

S.R.Samantaray, Senior Member, IEEE, Geza Joos, FIEEE, I. Kamwa, FIEEE

M

978-1-4577-2159-5/12/$31.00 ©2011 IEEE

2

grid-connected and islanded mode. The following sections deals with generalized S-transform, studied systems, computational results, discussion and conclusions.

II. GENERALIZED S-TRANSFORM The S-transform [8-10] is an extension to the Gabor

Transform and wavelet transform, and is based on a moving and scalable localizing Gaussian window. The interesting phenomena in the S-transform are that it is fully convertible both forward and inverse from time domain to frequency domain. This property is due to the fact that the modulating sinusoids are fixed with respect to the time axis while the localizing scalable Gaussian window dilates and translates. The S-transform falls within the broad range of multi-resolution spectral analysis, where the standard deviation is an inverse function of the frequency, thus reducing the dimension of the transform.

The expression for S-transform of a continuous signal )t(x is given as

dt)ift2exp(.2

)t(fexp.2

f)t(x)f,(S 2

22π−⎟

⎟⎠

⎞⎜⎜⎝

⎛

α−τ−

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

πα=τ ∫

∞

∞− (1)

Here f is the frequency, t is the time and τ is a parameter that controls the position of the gaussian window on the t -axis.

The factor ‘ α ’ controls the time and frequency resolution of the transform and lower ‘ α ’ means higher time resolution. The converse is true if higher value of ‘ α ’ is chosen for the analysis. A suitable value of ‘α’, however, lies between 12.0 ≤α≤ . A value of 0.7 gave the best result for during analysis.

Also )f(Xd)f,(S∫∞

∞−=ττ (2)

where ‘ )f(X ’ is the Fourier transform of ‘ )t(x ’.

The discrete version of the continuous S-transform is obtained as

( )mj2iexp.n

m2exp.)nm(X)n,j(S2

2

221N

0mπ⎟

⎟

⎠

⎞

⎜⎜

⎝

⎛ απ−+= ∑−

= (3)

and j =1……..N-1, n =0, 1…N-1.Here j and n indicate the time samples and frequency step, respectively and

)nk2iexp(.)k(xN1)n(X

1N

0kπ−= ∑

−

= (4)

where n=0, 1………, N-1 Computation of )nm(X + is done in a straight forward

manner from (4). The Fourier spectrum of the gaussian window at a specific n (frequency) is called a voice gaussian and for a frequency )n(f 11 , the voice is obtained as

)n,j(jexp().n,j(A)n,j(S 111 φ= (5)

The spectral energy content E of the signal is obtained from modified S-transform as

{ }2))n,j(S(absE = (9) The signal energy obtained from the S-transform can be

used to detect the fault patterns in the transmission line for effective relaying.

In the present application x(k) is the time domain sampled current signals retrieved at both ends. The differential energy of the current signals is obtained using modified S-transform to detect the fault patterns in the microgrid.

III. SYSTEM STUDIED Fig. 1 Microgrid with multiple DG interface.

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 20 km with distribution lines of pi-sections. The details of the generator, DGs, transformers, distribution lines and loads are mentioned in Appendix-I.

The model is simulated using SIMULINK (MATLAB) package. The sampling frequency is 1.2 kHz at 60 Hz base frequency (20 samples per cycle). Fault currents at sending and receiving ends of respective feeders are retrieved and processed through modified S-transform to compute the differential energy for registering the fault patterns. Different types of shunt faults such as line-ground (L-G), line-line-ground (LL-G), line-line-ground (L-L), line-line-line-ground (LLL-G) and High Impedance Faults (HIF) are created on the distribution line at different locations. The faults are created in both grid-connected and dis-connected mode. The CB_MAIN is activated (off) for islanded mode and CB_LOOP is activated (on) for loop network of microgrid.

B-5

B-2

PCC B-1

CB_LOOP

DG-3

L-5

L-3

TR-3

R-3

L-2

R-6 R-5 R-4

R-1 R-2 TR-1

DL-6 DL-5

DL-2

DL-4

DL-1

DG-2 DG-1

L-4

TR-5 TR-4

TR-2

CB_MAIN

~

Gen

DG-4

DL-3

L-1

B-3

B-6

B4

B-7

3

IV. SIMULATION RESULTS

A. Microgrid protection in Grid-connected mode In the proposed technique, the fault current signals are

retrieved at respective sending and receiving end of the respective feeder (considering the primary and back-up protection), are processed through modified S-transform to produce the time-frequency contours. For primary protection at R-1 (for a fault on the feeder between B-1 and B-2), the fault currents are retrieved at B-1 and B-2 and differential energy is calculated. For back-up protection at R-1(for fault on feeder between B-2 and B-3), the fault currents at B-1 and B-3 are considered for computing the differential energy. Similarly for primary protection at R-2 (fault on feeder between B-2 and B-3), differential energy is computed considering the currents at B-2 and B-3 respectively. For back-up protection at R-2, differential energy is computed considering the fault currents at B-2 and B-4. This approach is considered for primary and back-up protections for all the feeders to compute the differential energy.

The spectral energy content of the fault currents retrieved at bus ‘S’ and ‘T’ are calculated as given in equation (10) and (11), which are derived from equation (9). The corresponding differential energy is computed as the difference between the two and given in equation (12). For primary and back-up protections, the busses are selected as mentioned earlier.

{ }2BB ))n,j(S(absE

SS= (11)

{ }2BB ))n,j(S(absE

TT= (12)

TS BB EEenergyalDifferenti −= (13)

Fig. 2(a) and 2(b) show the fault currents and generated time-frequency contours versus samples (time) for the fault currents at B-1 and B-2 for the L-G fault created on feeder between B-1 and B-2. It is observed that the time-frequency contours effectively localize the fault patterns immediately after the fault occurrence. The spectral energy content of frequency contours of the fault currents at B-1 and B-2, and the corresponding differential energy (computed by subtracting the spectral energy content of the current signal at B-2 from spectral energy content of the current signal at B-1) are shown in Fig 2(c). The differential energy and the threshold setting for L-G fault on the feeder between B1 and B-2 (primary protection at R-1 with radial network) is shown in Fig. 2(d). It is observed that with a threshold setting of ‘1’, the differential energy based technique takes 3.5 cycles (70 samples) from the fault inception for issuing the tripping signal. Similar observations are made for LL-G fault on the feeder between B-3 and B-4: primary protection at R-3 (loop network), where it takes 3.6 cycles after fault inception to issue the tripping signal as shown in Fig. 3. Thus a threshold setting is confirmed after testing on various cases of fault situations across different sections of the microgrid.

The proposed technique is also tested for registering fault patterns in back-up zone. Fig. 4 shows the differential energy and the threshold setting for L-G fault on the feeder between

B2 and B-3: back-up protection at R-1 (radial network). In this case, the tripping signal can be issued after 3.75 cycles from fault inception. In back-up protection, the threshold is set at 0.5 by similar logic (testing on different fault situations in back-up zone) for issuing the tripping signal.

50 100 150 200 250 300 350 400 450 500 550-2

-1

0

1

2

3

4

Am

plitu

de(p

u)

samples

time-

frequ

ency

con

tour

s

50 100 150 200 250 300 350 400 450 500 550

20

40

60

80

100

Fig. 2(a) Fault current and time-frequency contours of phase-a for L-G fault on the feeder between B-1 and B-2, retrieved at B-1.

50 100 150 200 250 300 350 400 450 500 550-1

-0.5

0

0.5

Am

plitu

de(p

u)

samples

time-

frequ

ency

con

tour

s

50 100 150 200 250 300 350 400 450 500 550

20

40

60

80

100

Fig. 2(b) Fault current and time-frequency contours of phase-a for L-G fault on the feeder between B-1 and B-2 , retrieved at B-2.

50 100 150 200 250 300 350 400 450 500 5500

2

4

6

ener

gy(B

-1)

50 100 150 200 250 300 350 400 450 500 5500

1

2

ener

gy(B

-2)

50 100 150 200 250 300 350 400 450 500 5500

2

4

6

samples

Diff

eren

tial e

nerg

y

Fig. 2(c) Spectral energy of the fault current at B-1, B-2 and differential energy for the for L-G fault on the feeder between B-1 and B-2: primary protection at R-1 (radial network).

4

50 100 150 200 250 300 350 400 450 500 5500

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

samples

Diff

eren

tial e

nerg

y

Fault inception

70 samples(3.5 cycles)

Threshold

Fig. 2(d) Differential energy and the threshold setting for L-G fault on the feeder between B-1 and B-2: primary protection at R-1 (radial network).

50 100 150 200 250 300 350 400 450 500 5500

0.5

1

1.5

2

2.5

3

3.5

samples

diffe

rent

ial e

nerg

y

Threshold

Fault inception

72 samples(3.6 cycles)

Fig. 3 Differential energy and the threshold setting for LL-G fault on the feeder between B-3 and B-4: primary protection at R-3 (loop network).

50 100 150 200 250 300 350 400 450 500 5500.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

samples

diffe

rent

ial e

nerg

y

Threshold

75 samples(3.75 cycles)

Fault inception

Fig. 4 Differential energy and the threshold setting for L-G fault on the feeder between B-2 and B-3: back-up protection at R-1 (radial network). One of the important events in protection measure is the

fault pattern recognition. This is the process of identifying the faulty phase (phases) involved during the shunt fault process. The differential energy for a, b and c-phase for L-G fault (a-g) on feeder between B-1 and B-2 is shown in Fig. 5. It is observed that the differential energy for a-phase grows

substantially to higher value where as the differential energy for b and c-phase stays at base zero value and the response time is 3.5 cycles from fault inception. Similar observation is made for LL-G fault (ab-g) on feeder between B2 and B-3 and the differential energy for a, b-phase is growing while of c-phase is not growing as shown in Fig. 6. Thus the fault pattern recognition is reliably achieved using the differential energy approach in microgrid protection.

50 100 150 200 250 300 350 400 450 500 550-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

samplesdi

ffere

ntia

l ene

rgy

a-phaseb-phasec-phase

Threshold

Fault inception70 samples(3.5 cycles)

Fig. 5 Differential energy of a, b and c-phase for L-G fault (a-g) on feeder between B-1 and B-2: primary protection at R-1 (Fault pattern recognition: radial network).

50 100 150 200 250 300 350 400 450 500 550-1

0

1

2

3

4

5

samples

diffe

rent

ial e

nerg

y

a-phaseb-phasec-phase

Fault inception

71 samples (3.55 cycles)

Threshold

Fig. 6 Differential energy of a, b and c-phase for LL-G fault (ab-g) on feeder between B-2 and B-3: primary protection at R-2 (Fault pattern recognition: radial network).

B. Microgrid protection in islanded mode The fault currents in microgrid with grid-connected mode

and islanded mode are significantly different and thus protection measures face difficulty to provide a unique solution. The differential energy based scheme has been tested for different fault situation in microgrid in islanded mode. Fig. 7 shows the differential energy and the threshold setting for L-G fault on the feeder between B1 and B-2: primary protection at R-1 (radial network). It is observed that with a threshold setting of ‘-0.3’, the response time achieved is 3.8 cycles (76 samples) after fault inception. Fig. 8 shows the Differential energy and the threshold setting for LL-G fault on the feeder

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between B2 and B-3: back-up protection at R-1 (radial network). In case of back-up protection, the threshold is set at ‘-0.3’ and the tripping signal can be issued within 4 cycles from fault inception.

50 100 150 200 250 300 350 400 450 500 550-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

samples

diffe

rent

ial e

nerg

y

72 samples(3.6 cycles)

Fault inception

Threshold

Fig. 7 Differential energy and the threshold setting for L-G fault on the feeder between B-1 and B-2: primary protection at R-1 (radial network).

50 100 150 200 250 300 350 400 450 500-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

samples

diffe

rent

ial e

nerg

y

Threshold

Fault inception

76 samples(3.8 cycles)

Fig. 8 Differential energy and the threshold setting for LL-G fault on the feeder between B-2 and B-3: back-up protection at R-1 (radial network).

50 100 150 200 250 300 350 400 450 500 550-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

samples

diffe

rent

ial e

nerg

y

a-phaseb-phasec-phase

Threshold

Fault inception

68 samples(3.4 cycles

Fig. 9 Differential energy of a, b and c-phase for L-G fault (a-g) on feeder between B-2 and B-3: primary protection at R-2 (Fault pattern recognition: radial network).

50 100 150 200 250 300 350 400 450 500 550-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

samples

diffe

rent

ial e

nerg

y

a-phaseb-phasec-phase

Fault inception

70 samples(3.5 cycles

Threshold

Fig. 10 Differential energy of a, b and c-phase for LL-G fault (ab-g) on feeder between B-3 and B-4: primary protection at R-3 (Fault pattern recognition: (radial network).

For microgrid faults in islanded mode, the fault pattern

recognition is also achieved effectively with the proposed differential energy based approach. Fig. 9 shows the fault pattern recognition for L-G fault (a-g) on feeder between B-2 and B-3. Thus the differential energy for a-phase grows in negative direction while of phase-b and c is steady at base. Similar observations are made for LL-G fault (ab-g) on the feeder between B-2 and B-3 (primary protection at R-3), as shown in Fig. 10. It is observed that the differential energy changes substantially in faulted phase compared to unfaulted one, and thus identifying the phase involved in the fault process.

C. High Impedance Fault Detection (HIF) in microgrid.

High impedance faults are difficult to model as they are non-linearly deterministic in nature. The HIF model [11] is developed using anti-parallel diodes with non-linear resistance and DC source connected together for each phase as shown in Fig.11. Typical HIF current for the High-impedance fault created on the feeder between B-1 and B-2, is shown in Fig. 12. The High Impedance Faults (HIFs) are created on the distribution feeders and differential energy is computed to

Vn VP

Rn (Non-linear Resistance)

Rp (Non-linear Resistance)

Fig. 11 High-impedance fault model

6

register the fault. Fig. 13 shows the differential energy and the threshold setting for High Impedance Fault (HIF) on the feeder between B1 and B-2: primary protection at R-1 (radial network). In case of HIF faults, the threshold is set at ‘0.1’ and the tripping signal can be issued within 3.3 cycles after fault inception.

100 150 200 250 300 350-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

samples

ampl

itude

(pu)

HIF inception

Fig. 12 Typical HIF current for the High-impedance fault created on the feeder between B1 and B-2.

100 150 200 250 300 350 400 450 500-0.1

-0.05

0

0.05

0.1

0.15

0.2

samples

diffe

rent

ial e

nerg

y

Fault inception

Threshold

66 samples(3.3 cycles)

Fig. 13 Differential energy and the threshold setting for High Impedance Fault (HIF) on the feeder between B-1 and B-2: primary protection at R-1 (radial network).

V. DISCUSSION In the proposed technique, the differential energy of the

fault currents at respective bus ends (depending upon primary and back-up protection), are used to register the fault patterns for issuing the tripping signal for reliable microgrid protection. It is observed that the differential energy grows in positive direction for faults in grid-connected mode and becomes negative in islanded mode. This occurs as the fault currents at respective bus ends are significantly different in both situations. This clearly distinguishes the faults in microgrid, indicating whether the fault exists in grid-connected or islanded mode.

The proposed technique is also tested on similar fault situations in radial as well as loop network providing reliable protection measure. The most important issue in the proposed technique is that it uses time-frequency contours of the faulted currents to derive the differential energy, thus involving both

time and frequency information instead of either in conventional relaying for decision making process. The thresholds generated and the results obtained are for the adopted microgrid in the proposed study. However, the thresholds may be suitable for other microgrid structures as the differential energy is derived on per unit system. The adaptive threshold setting is being considered as a future study to generate thresholds on differential energy for different structures of the microgrids.

50 100 150 200 250 300 3500.2

0.4

0.6

0.8

1

1.2

1.4

1.6

samplesdi

ffere

ntia

l ene

rgy Fault inception

74 samples(3.7 cycles)

Fig. 14. Differential energy with sampling time difference of 5 ms between two ends (a-phase for L-G fault (a-g) on feeder between B-2 and B-3).

50 100 150 200 250 300 3500

0.5

1

1.5

2

2.5

3

3.5

4

Fault inception

90 samples(4.5 cycles)

Fig. 16 Differential energy (providing delayed response) with sampling time difference of 10 ms between two ends (b-phase for LL-G fault (b-g) on feeder between B-2 and B-3).

It is observed that the differential energy is less sensitive to

synchronization error compared to time-domain data difference such as differential currents. The differential energy works fine with a time sampling difference of 5ms (quarter cycle) as shown in Fig. 15, with 3.7 cycle response time as usual. As the time difference between two end signals increases to 10 ms (Fig. 16) , then the response time increases to 4.5 cycles (which is delayed), as time difference is more. Thus differential energy can suitably work for sampling time difference up to 5 ms between two end signals without affecting the performance. Thus the energy variable is less sensitive to time error: for instance, the energy of one cycle sine and cosine is the same, even though they are time-shifted by a quarter of cycle.

7

VI. CONCLUSIONS A new approach for the microgrid protection using time-

frequency transform technique is presented. Spectral energy content of the fault current signals retrieved at both ends of the feeders is found using time-frequency transform and differential energy is computed to register the fault patterns. A threshold can be set on the differential energy to issue the tripping signal for different faulty situations in microgrid with grid-connected and islanded mode, and including both radial and loop network. The results indicate that the proposed scheme can reliably protect the microgrid against shunt faults and HIF, and thus can be extended for large power distribution network with multiple

APPENDIX-I • Generator: rated short-circuit MVA=1000, f=60 Hz,

rated kV =120, Vbase = 120 kV. • Distributed Generations (DGs): Wind farm (9 MW)

consisting of six 1.5-MW wind turbines is connected to a 25-kV distribution system exports power to a 120-kV grid through a 30-km 25-kV feeder. The Doubly-fed Induction Generator (DFIG) has been considered for the proposed study.

• Transformer T1: rated MVA = 50, f = 60 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, T5: rated MVA = 10, f = 60 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, DL-4, DL-5, DL-6: PI-Section, 20 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: L-1=10 MW, 3.5 MVAR L-2, L-3, L-4, L-5 = 6 MW, 2.5 MVAR.

REFERENCES [1] T. C. Green and J. D. McDonald, “Modeling and analysis of fault

behavior of inverter microgrids to aid future fault detection”, IEEE International Conference on System of Systems Engineering, 2007, pp-1-6.

[2] E. Sortomme, G. J. Mapes, B. A. Foster, and S. S. Venkata, “Fault analysis and protection of a microgrid”, 40th North Amer. Power Symposium, 2008, pp. 1–6.

[3] H. Nikkhajoei and R. H. Lasseter, “Microgrid protection,” IEEE Power Engineering Society General Meeting, Jun. 2007, pp. 1–6.

[4] M. A. Redfern and H. Al-Nasseri, “Protection of microgrids dominated by distributed generation using solid state converters”, IET 9th international conference on Develop. Power Syst. Protection, 2008, pp. 670–674.

[5] H. H. Zeineldin, E. F. El-Saadany, and M. M. A. Salama, “Distributed generation microgrid operation: Control and protection”, Power Systems Conference: Advanced Metering, Protection, Control, Communication, and Distributed Resources, 2006 , pp. 105–111.

[6] Eric Sortomme, , S. S. Venkata, , and Joydeep Mitra, “Microgrid Protection Using Communication-Assisted Digital Relays”, IEEE Trans. on Power Delivery, (Early access 2010)

[7] Hong-Tzer Yang, Chiung-Chou Liao, ‘A de-noising scheme for enhancing Wavelet-based power quality monitoring system”, IEEE. Transaction on Power Delivery, Vol.16, No.3, pp. 353-360, July 2001.

[8] Mansinha, L., Stockwell. R. G. and Lowe, R.P., “Pattern analysis with two dimensional spectral localization: Application of two dimensional S-transforms”, Physica A, 239, pp.286-295, 1997.

[9] Pinnegar, C.R and Mansinha, Lalu., “The S-transform with windows of arbitrary and varying window", Geophysics, vol-68, pp.381-385, 2003.

[10] Stockwell, R.G., Mansinha, L. and Lowe, R.P., “Localization of complex Spectrum: The S-transform”, Jour. Assoc. Expl. Geophysics, vol. XVII, No-3, pp. 99-114, July-1996.

[11] S.R. Samantaray, B.K. Panigrahi and P.K. Dash, “High impedance fault detection in power distribution networks using time–frequency transform and probabilistic neural network”, IET Generation, Transmission & Distribution, 2008, 2, (2), pp. 261–270, 2008.

BIOGRAPHIES S. R. Samantaray (M’08, SM’ 10) received B.Tech in Electrical Engineering from UCE Burla, India, in 1999 and Ph.D. in Power System Engineering from the Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, India, in 2007.

Dr. Samantaray holds the position of Associate Professor in the School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, Orissa, India.

He visited department of Electrical and Computer Engineering, McGill University, Montreal, Canada as Post Doctoral Research Fellow in 2008 and 2009-10. His major research interests include Intelligent Protection, Digital Signal Processing, Soft Computing, FACTs, Distributed Generation and Dynamic Security Assessment.

He is the recipient of the Orissa Bigyana Academy Young Scientists Award-2007, Indian National Academy of Engineering Best Ph.D. Thesis Award-2008 and IEI (Institute of Engineers, India) Young Engineers Award-2009, Samanta Chandra Sekhar Award, Orissa Bigyana Academy, Department of Science and Technology, Govt. of Orissa-2010.

Geza Joós (M’82, SM’89, F’06) graduated from McGill University, Montreal, Canada, with an M.Eng. and Ph.D. He has been a Professor with McGill University since 2001, and holds a Canada Research Chair in Power Electronics applied to Power Systems. He is involved in fundamental and applied research related to the application of high-power electronics to power conversion, including distributed generation and wind energy, and to power systems. He was previously with ABB, the Ecole de

technologie supérieure and Concordia University. He is involved on a regular basis in consulting activities in power electronics and power systems. He is active in a number of IEEE Industry Applications Society committees and in IEEE Power Engineering Society working groups, and in CIGRE working groups. He is a Fellow of the Canadian Academy of Engineering and of the Engineering Institute of Canada.

Innocent Kamwa (S’83–M’88–SM’98–F’05) received his Ph.D. degree in Electrical Engineering from Laval University, Laval, QC, Canada, in 1988. Since then, he has been with the Hydro-Québec Research Institute (IREQ), Power System Analysis, Operation and Control, Varennes, QC, where he is currently a Principal Researcher in bulk system dynamic performance. He has been an Associate Professor of Electrical Engineering at Laval University since 1990.

Dr. Kamwa has been active for the last 13 years on the IEEE Electric Machinery Committee where, as Working Group Chair and Secretary, he contributed to the latest standards 115 and 1110. A member of CIGRÉ and a registered professional engineer, Dr. Kamwa is a recipient of the 1998 and 2003 IEEE Power Engineering Society Prize Paper Awards and is currently serving on the Adcom of the IEEE System Dynamic Performance Committee.