This article has been accepted for publication in IEEE Transactions on Industrial Informatics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TII.2023.3252409
1
A Novel Fault Identification and Localization Scheme
for Bipolar DC Microgrid
G. Kesava Rao, Student Member, IEEE, and Premalata Jena, Senior Member, IEEE
Abstract— Traditional fault detection and location schemes
become ineffective for detecting and locating the fault in DC
microgrid systems due to integrating various types of power
electronic-based DC loads and generators. To resolve this issue,
advanced, intelligent, specialized fault detection and location
schemes are necessary. This article suggests a novel fault detection
scheme based on the difference in the teager energy available in
the DC current wave at sending and receiving ends of lines. After
the detection of the fault, the location is calculated by estimating
the resistance of the cable up to the fault point as well as the total
resistance of the cable, with the help of least square technique. The
proposed scheme decides fault is internal if the estimated fault
location is less than 1 p.u. Otherwise, the proposed scheme decides
fault is external. A DC microgrid with different types of generating
units and loads is simulated using MATLAB/SIMULINK to
evaluate the developed algorithm. Internal and external faults,
pole-to-ground and pole-to-pole faults with changing fault
resistance and fault location are some of the fault scenarios that
have been simulated. The obtained simulation results prove that
the suggested algorithm can discriminate between internal and
external faults and locate the fault. The proposed technique is also
examined on a DC microgrid hardware testbed and results show
the efficiency of the suggested approach.
Index Terms— Communication channel, DC microgrid, Fault
location, High resistance fault, Self-protection, Teager energy.
I. INTRODUCTION
D
ue to the evolution in power electronic technology and
power converter topologies, DC microgrids are an
attractive option over AC microgrids [1]. Moreover, the
majority of distributed energy resources (DERs), like
photovoltaic (PV), fuel cells and energy storage systems
(battery) generate power in DC and hence can be easily
interfaced with the DC microgrid. Also, most of the loads,
electric vehicles (EVs), home appliances, and light-emitting
diode (LED) lights operate on DC power [2]. DC microgrids
improve the system's overall efficiency, decrease conversion
stages, easy to control, more stable, maintain the power quality
requirements and reduce conductor weight and size [3] [4]. So
far, DC microgrids have been successfully implemented for
industrial plants, ships, EV fast charging stations [5], data
centers and renewable energy generators such as solar farms
[6].
Despite the many benefits of DC microgrids, it remains
challenging to implement effective protection schemes for these
networks. In DC microgrid, unconventional fault current causes
two paramount protection issues. The first one is the discharge
This work was supported by the Science and Engineering Research
Board, through the Department of Science and Technology, Government
of India, under Projects SER-1801-EED and SER-1851-EED.
of DC bus capacitor in case of a short circuit, which causes a
rapid rise in the fault current and damages any electronic
equipment in the fault path if the fault segment is not separated
by fast DC protection system [7]. The second issue is that the
DC fault current lacks zero crossings, which makes fault current
interruption more difficult. It insists on utilizing a special circuit
breaker (Solid State Circuit Breaker (SSCB) and distinct
converter) to remove the fault [8]. After removing the fault
segment, it is essential to restore the fault line to avoid a power
outage for end users. The restoration of the faulty segment
demands an accurate fault localization algorithm. As a result,
fault identification and localization in a DC microgrid are
becoming a hotspot for research.
Some research articles have exclusively focused on the
methods for DC microgrid fault detection [9]–[18]. In case of
fault, the rate of rising of current is very high in DC microgrid.
Therefore, overcurrent and current derivative protection
schemes are more efficient in detecting the DC system's low
impedance faults [9], [10]. However, the overcurrent relay
coordination is complex because of the DC microgrid low
resistance and small cable length. In [11], authors developed a
fast protection method for the low voltage DC (LVDC)
microgrid depending on the magnitude of current difference of
the line segment. However, the magnitude of the current
difference is very small when there is a high resistance fault. To
minimize synchronization issues in the existing current
differential protection scheme, the sign of the fault current is
equated at the sending and receiving ends of the cable segment
[12]. Nevertheless, the polarity of the current is not changed at
the line terminal during a high resistance fault. In [13], [14], the
faults in the DC microgrid are detected based on the estimated
line parameter sign. In [15], authors suggested a fault
identification technique based on the active resistance
estimation of DC microgrid up to the fault point. However, this
method could fail during a close-in fault. In [16], authors
suggest a machine learning-based fault detection and
classification scheme for the LVDC microgrid. However, the
machine learning method accuracy depends on the dataset
quality. Based on the transients monitoring function, a novel
fault detection method for DC microgrid is developed in [17].
However, the suggested approach is unable to differentiate
internal and external faults. In [18], a novel unit protection
scheme is developed based on the adaptive Fano Factor tool
based technique. However, the method has not discussed the
fault classification in the DC microgrid system.
Some research articles have exclusively concentrated on DC
microgrid fault location methods [19]-[23]. In [19], a new fault
location technique is suggested for the LVDC by connecting the
external circuit at both ends of the line. Yet, the technique has
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2
a limitation on the voltage rating of the DC microgrid.
Traveling wave-based fault location algorithm is proposed in
[20], [21]. This technique estimates the fault location by using
traveling wave reflection laws. Even so, the traveling wave
method demands a high sampling frequency transducer. A
signal injection-based fault location method is developed in
[22]. This method calculates the fault location by introducing a
known signal into the faulted DC line. However, the technique
necessitates using an external source to introduce an external
signal. In [23], a parameter estimation based fault location
method is proposed for the mesh type DC microgrid system.
However, the fault resistance value affects the estimated fault
location accuracy. As discussed above, most of the recent
research articles focus on either fault detection or fault location
in the DC microgrid.
Only a few research articles that addressed fault detection
and localization in the DC microgrid are discussed in [24]–[29].
In [24], authors identify faults in the LVDC microgrid by
employing the voltage of the current limiting inductor and
current flowing through the ground resistor at the relay location.
The fault is localized with the help of an iterative method.
However, the voltage across the current limiting inductor and
current at ground resistance is lower than the threshold during
high resistance fault. Depending on the polarities and wave
shape of the traveling wave, a novel traveling wave protection
technique for the medium-voltage DC (MVDC) microgrid is
suggested in [25]. In [26], the fault is identified based on the
differential current sample cumulative sum (CUSUM) index
and the fault location is estimated using the Moore-Penrose
pseudo inverse scheme. Nevertheless, the CUSUM index rises
slowly in the case of a high impedance fault. Multi-Criterion
System is used to detect a fault in the DC microgrid, and a
neural network is employed to estimate the fault distance [27].
Yet, the fault detection time is very high. In [28], the fault in the
LVDC microgrid system is detected by using overcurrent
protection and the fault location is estimated with the help of
probe power unit. Because of the high rate of rising of fault
current, coordination of the overcurrent relay is challenging,
and the use of probe power unit raises the cost of the protection
system. In [29], the fault in the DC microgrid is detected by
comparing the transient current signal with the steady state
current signal. After that, fault location is estimated by
similarity analysis of sampled current and estimated currents.
However, the method’s fault detection and location accuracy
are altered in case of high resistance faults.
To address the issues with the existing fault identification and
localization scheme [24]–[29], this article develops a novel unit
fault detection and location algorithm for bipolar DC microgrid
systems. This article identifies the fault in the DC microgrid by
comparing the teager energy available in the current waves at
sending and receiving ends of the line. In the case of an external
fault, the difference in teager energy at the ends of line segment
is close to zero. However, the teager energy difference is not
zero at the time of the internal fault. To improve the ruggedness
of the suggested fault detection technique, the fault location is
also estimated online by using voltage and current available at
the ends of the cable segment. If the calculated fault location is
less than one per unit, then the fault is internal else fault is
external. A radial DC microgrid system is used to validate the
proposed scheme.
The suggested fault detection and localization technique
makes significant contributions. First, it takes less computation
time to identify the fault and compute fault location as
compared to other existing methods. It also detects the fault up
to 50 Ω fault resistance. Second, the technique can estimate the
fault location without using an external circuit. Moreover, the
proposed fault location technique can estimate the cable fault
location even though the cable parameters (resistance and
inductance) are unknown. Third, the proposed approach is
immune to the noise in the input signal. Finally, the efficiency
of the suggested technique is unaffected by the intermittent and
variable output of distributed generators.
The rest of the paper is articulated as follows: Section II
presents the system configuration. Section III contains a
description of the proposed algorithm and its flowchart. Section
IV presents simulation results and hardware validation, while
Section V summarizes the conclusions.
II. DESCRIPTION OF THE LVDC SYSTEM
A seven-bus bipolar LVDC microgrid system is designed in
MATLAB/SIMULINK to check the effectiveness of the
suggested unit protection scheme, as illustrated in Fig. 1 [22].
The voltage of the LVDC system is 480 V. The buses present
in the LVDC microgrid are associated with the loads (DC fastcharging stations and data centers) and distributed generators
(solar, battery) which are connected via a voltage source
converter (VSC). The ratings of the parameters of seven bus DC
microgrid are provided in Table-IV. Bipolar cables (positive
pole, negative pole and neutral line) are used to connect the
different types of DERs and loads available at buses in the DC
microgrid system. The utility grid is also connected to the
LVDC microgrid at bus L via a VSC, LC filter, and transformer.
The VSC at the grid and the bidirectional converter at battery
are working in grid forming mode. The boost converter at solar
panels is set to track the maximum power point. The DC fast
chargers are coupled to the LVDC microgrid with the
Islanding
DC Bus-L
protector AC
Utility
grid
Bus-R
DC
IEDRQ
EVcharging
station
IEDLM
0.5 Km
IEDQR
1 Km
F2
DC
DC
Bus-Q
IEDQN
0.5 Km
IEDML
IEDNQ
IEDMN 1 Km IEDNM
Bus-M
Bus-N
IEDMO
F1
IEDNP
Solar Panel
DC
1 Km
1 Km
IEDOM
IEDPN
Bus-O
Power line
Communication line
DC
DC
Data center
DC
DC
EVcharging
station
Battery
DC
DC
Bus-P
DC
Load
DC
DC
EVcharging
station
Fig. 1. DC microgrid with different type of DG’s and EV charging stations.
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3
unidirectional isolated DC-DC converters [30]. Initially, the
mode of charging for the unidirectional, isolated DC-DC
converters is constant current, which later shifts to the constant
voltage charging mode. The voltage and current at the ends of
the line segment are measured using the LV20-P-718331
voltage sensor and the LA25-P-13022 current sensor. Due to
unequal load distribution at the positive and negative poles in
the bipolar DC microgrid system, voltage unbalance occurs in
DC microgrid system. To resolve the voltage unbalance
between the positive and negative poles of the bipolar network,
a voltage balancer circuit is utilized [31]. The IEDs with the
SSCB are mounted at every end of the line segment to protect
the line segment from different types of faults [32]. The
connection circuit of the digital protection IED used in this
article is shown in Fig. 2 [32]. The protection IED receives the
voltage and current information through the voltage and current
sensor connected at one end of the line segment. It also receives
the voltage and current information from the other end of the
line through sophisticated communication infrastructure. After
receiving the data, IEDs analyze the data and send the
corresponding command signal to the SSCB. The precision
time protocol (PTP) is considered to synchronize the data in the
DC microgrid [33].
A. Communication Latency
The development of the smart grid concept can lead to a
reduction in the cost of unit protection techniques because the
smart grid's communication infrastructure can be employed in
such protection schemes [34]. Therefore, this article suggested
a novel unit protection technique for LVDC microgrid systems.
Using the IEC TR 61850-90-5:2012 communication protocol,
the data is transmitted between the two IEDs. In general, the
following factors influence the communication latency between
the two IEDs. They are (i) processing delay, (ii) propagation
delay or queuing delay (iii) transmission delay. The processing
delay is influenced by the magnitude of the data packets
transmitted and the communication channel bandwidth. For the
same data packets, the delay in the high bandwidth channel
would be lower. Thus, for data packets of 64 bytes and a
channel capacity of 1.5 Mbps, the delay is 0.3 milliseconds, and
for a channel capacity of 100 Mbps, it is reduced to 5
microseconds [12]. In DC microgrid systems, the distance
between two IEDs is typically small. This will cause a minor
propagation delay (1–5 miles, delay ranging from 8.2 to 41
DC BUS
SSCB
DC line
+
-
Trip
+
-
Current sensor
R1
I
IED
R2
Vn
+
GND Voltage sensor
V
Communication
from other IED
microseconds) [35]. Queuing and transmission delays would be
negligible for enhanced communication networks, like fiber
optics and high bandwidth communication channels (the
latency is about 0.1 millisecond of bandwidth 100–1000 Mbps).
As a result, communication latency is low enough to allow the
existing unit protection techniques to operate in less than one
millisecond. Therefore, the proposed unit protection technique
is subjected to a communication latency of up to 1 ms in the
relaying operation, as suggested in [12][18].
III. PROPOSED FAULT DETECTION AND LOCATION TECHNIQUE
A. Teager Energy Representation of Continuous and Discrete
Signal.
The teager energy operator has been defined for both
continuous (real and complex) and discrete signals. In [36],
Kaiser used the following differential equation to understand
the teager energy operator.
d 2
dt
2

K
 0
M
(1)
The object of the mass M is suspended to the spring constant
K, as explained by the preceding second order differential
equation. The total energy in the object can be calculated as
follows
1
1 2
2
E  K   Mv
2
2
(2)
where v=dχ/dt and χ=Acos(ωt+ϕ), the energy that exists in the
objective can be represented as
1
E  M 2 A2
(3)
2
As per the preceding equation, the energy is a function of the
oscillation amplitude (A) and frequency (ω). According to [36],
the amount of teager energy in the continuous signal χ(t) is
expressed as
(4)
 c (  (t ))  (  '(t )) 2   (t ). ''(t )
where,  ' (t ) 
d ( t )
dt
Using equation (4), the amount of teager energy present in
the continuous signal χ(t)=Acos(ωt) is given by
 c (  (t ))  A2 2
(5)
From (5), the amount of the teager energy present in the
continuous signal (χ(t)=Acos(ωt)) is proportional to the square
of the oscillation amplitude (A) and frequency (ω). By
observing equations (3) and (5), the energy available in the
suspended object has the frequency oscillation of Acos(ωt+ϕ)
and the teager energy present in signal χ(t)=Acos(ωt) are
similar. According to [36], the teager energy available in the
discrete signal can be expressed as
 d   [ n]   [ n]   [ n  1]. [ n  1]
2
(6)
where n is the sample number. For calculating the teager
energy, three consecutive samples are required. The magnitude
of teager energy present in the discrete signal χ[n]=Acos[Ωn+φ]
is calculated using the equation (6) and written as
Fig. 2. IED connection circuit in DC microgrid.
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4
 d  A cos[n   ]  A 
2
2
(7)
The relation between the continuous teager energy ψc and
discrete teager energy ψd can be calculated using the backward
finite approximation. The sampling of the continuous signals
generates the discrete signals with the sampling frequency of fs.
The discreet signal can represent as
(8)
[n]   (nt )
1
fs
Therefore, by replacing
y[n]    [n]   [n  1] / t
where, t 
 '(t )
and
and
 "(t )
in (4) with
 y[n]  y[n  1] / t
2
 v (0) 
2
 (i f (t ))   c  e( z1  z2 )t  iL (0) * z1 z2e( z1  z2 )t
L
 cf 
 v (0) 
( z  z )t
  c  * iL (0) * e 1 2  z2  z1 
 Lcf 
Now, the z1 and z2 parameters present in (15) are expressed
in terms of the line parameters up to the fault point. Further, the
teager energy can be written as
2
 v (0)  ( R / L )t
2
1
( R / L )t
ψ(i f (t ))   c  e cf cf  iL (0) *
e cf cf
Lcf Ccf
 Lcf 
respectively, (4) can be written as
 c (  (t )) 
1
(Δt)
2
 [n  1]
2
  [ n  2]. [ n]

(15)
(16)
 v (0) 
Rcf ( R / L )t
  c  * iL (0) *
* e cf cf
Lcf
 Lcf 
(9)
(10)
On observing the above equation (10), if we ignore the onesample shift and the scaling by T-2
s then we can transform
ψc(χ(t)) into ψd(χ[n]). For the simple representation of the teager
energy in the subsequent section, the subscripts c and d from ψ
are ignored.
In the case of a transient, the DC signal oscillates with the
damped resonant frequency (ωd). As a result, the teager energy
available in the DC current wave is not equal to zero. From (16),
it has been observed that the size of the teager energy (ψ(if(t))
is dependent on the prefault voltage of the DC capacitor,
prefault current through inductance, and the fault current path
parameters.
B. Calculation of Teager Energy in DC Signal During Steady
State and Transient (fault) Scenarios.
In a DC microgrid system, the steady-state current can be
represented as
(11)
i(t )  A * u(t )
where A is the magnitude of the signal. With the help of
equations (4) and (11), during the steady-state, the teager
energy available in the DC current wave i(t) can be written as
 (i(t ))  0  i(t )  0  0
t  0 (12)
In the steady state condition, frequency (ω) of DC current
wave is zero. As a result, the teager energy available in the DC
current waveform is also zero. When a fault occurs in a DC
microgrid, the fault current [9] expression in the frequency
domain is written as
vc (0)
 iL (0)
Lcf
i f (s) 
(13)
Rcf
1
2
s 
s
Lcf
Lcf Ccf
C. Faulty Line Identification and Fault Location Estimation
in LVDC Microgrid
1) Faulty Line Identification in DC Microgrid
In this article, the difference in teager energy available in
current waves at both ends of the line segment has been used to
distinguish the internal and external faults in the DC microgrid.
The teager energy difference is calculated as
 [ n ]   1 [ n ]  2 [ n ]
(17)
 c (  (t ))   d ( [n  1]) / (Δt)2
where, vc(0) and iL(0) are the prefault voltage and current
across the capacitor and inductor, respectively. Rcf, Lcf, and Ccf
signifies the resistance, inductance, and capacitance present in
the fault current path. Expression for the fault current [9] in the
time domain is expressed as
vc (0)
i (0)
i f (t ) 
[e  z1t  e  z2t ]  L
[ z1e  z1t  z2 e  z2t ] (14)
Lcf ( z2  z1 )
( z2  z1 )
Here, z1 =α+ω d , z2 =α-ω d and α denotes the damping factor
and is equal to α=Rcf/2Lcf. ωd is the damped resonant frequency
and is equivalent to 𝜔𝑑 = √𝛼 2 − 𝜔02 . ω0 is the natural
frequency and is equal to ω0 =1/√Lcf Ccf . Hence, during the
transient, the teager energy available in the current wave (if(t))
given in (14) is determined using (4) and expressed as
where ψ1 and ψ2 are the teager energies of the current waves
flowing through both ends of the line segment. They can be
estimated using (6) as follows
 1[n]  ( I1[n])2  { I1[n  1]* I1[n  1]}
(18)
 2[n]  ( I2[n])  { I2[n  1]* I2[n  1]}
(19)
2
where I1 and I2 denote the line segments sending and receiving
currents, respectively. In the case of an internal fault (F1), the
equivalent DC microgrid circuit is depicted in Fig. 3 [16].
Hence, expressions of I1 and I2 [18] of the line segment can be
written as
vc1 (0)
i (0)
I1 (t ) 
[e  z1t  e  z2t ]  L1
[ z1e  z1t  z2 e  z2t ] (20)
Leq1 ( z2  z1 )
( z2  z1 )
I 2 (t ) 
vc 2 (0)
i (0)
[e  z3t  e  z4t ]  L 2
[ z3 e  z3t  z4 e  z4t ] (21)
Leq 2 ( z4  z3 )
( z 4  z3 )
where,
z1 =α1 +ω d1 , z2 =α1 -ω d1 here
ωd1 =√α21 -ω201 , ω01 =
ω d2 here α2 =
Req2
Leq2
1
√Leq1 C
α1 =
Req1
Leq1
;
; z3 =α2 +ω d2 , z4 =α2 eq1
; ωd2 =√α22 -ω202 , ω02 =
1
√Leq2 C
eq2
The resulting teager energy present in the sending end current
signal I1 and receiving end current signal I2 can be derived as
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5
Leq1/2
Req1/2
Req2/2
Leq2/2
I2 2Ceq2
2Ceq1 I1
VSC-1 vc1(0)
vc2(0)
Rf
Req1/2
Leq2/2
2
( Req 1 / Leq 1 )t
(22)
 v (0) 
Req1 ( R / L )t
  c1  * iL1 (0) *
* e eq1 eq1
L
L
 eq1 
eq1
2
 v (0)  ( R / L )t
2
1
 2   ( I2(t ))   c2  e eq 2 eq 2  iL2(0) *
Leq2Ceq2
 Leq2 
e
( Req 2 / Leq 2 )t
VNpp
(23)
 v (0) 
Req2 ( R / L )t
  c2  * iL2 (0) *
* e eq 2 eq2
L
L
 eq2 
eq2
By observing (22) and (23) in case of an internal fault, the
teager energy at sending end and receiving end of the line
segment is different. Therefore, the difference in teager energy
(γ) is greater than the threshold (ξ). However, in the case of
external fault, magnitude of current at the sending end and
receiving is same I1=I2. Due to this, the teager energy in both
current signals I1 and I2 is the same ψ1=ψ2. Therefore, teager
energy difference (γ) is less than the ξ.
In the bipolar DC microgrid system, the difference of the
teager energy present in the positive pole is given as
 p [n]  1 p [n]  2 p [n]
(24)
Similarly, the difference of the teager energy present in the
negative pole is given as
 n [ n ]   1 n [ n ]  2n [ n ]
(25)
2) Type of Fault Identification
In the DC microgrid cables or lines, during the pole to ground
fault, the teager energy difference (γ) of the faulted pole is
greater than the threshold (ξ). On the other hand, the teager
energy difference at the non faulted pole is less than the
threshold. By using this concept, the faulted pole in the DC
microgrid is identified during the pole to ground fault. In the
case of a pole to pole fault, teager energy difference at the
positive pole (γp) and negative pole (γn) are greater than the
threshold (ξ). As a result, DC microgrid pole to pole and pole
to ground faults are easily differentiated using above discussed
concept.
3) Fault Location Estimation in LVDC Microgrid:
In the suggested fault location scheme, the fault distance is
calculated using data at both ends of the line. The distance up
to the fault point is calculated by estimating the line resistance
up to the fault point and the overall line resistance. As a result,
even when the cable parameters are unknown, the suggested
method can estimate the fault location. A two-bus equivalent
system is considered to understand the proposed fault location
algorithm in the case of line to ground fault, as illustrated in Fig.
4. It has two voltage sources, VMpg (bus M pole to ground
L
R
-
Req2/2
 v (0)  ( R / L )t
2
1
 1   ( I1 (t ))   c1  e eq1 eq1  iL1 (0) *
Leq1Ceq1
 Leq1 
+
I2 VNpg
RF
VMpp
Fig. 3. Equivalent DC microgrid circuit during internal fault in line LM.
e
(1-m)R (1-m)L
mL
VMpg I1
VSC-2
2Ceq2
2Ceq1
Leq1/2
mR
+
-
Fig. 4. Two bus system during pole to ground fault.
voltage) and VNpg (bus N pole to ground voltage). Both voltage
sources supply the fault current of I1 and I2, respectively. The
voltage at bus M is written in terms of the currents at both ends
of the cable MN and written as
dI
dI
VMpg  mRI1  mL 1  (1  m)RI2  (1  m)L 2  VNpg (26)
dt
dt
where, m is the fault distance from the bus M, R and L are the
DC line segment resistance and inductance. The voltage drop in
the line MN can be written as
 dI dI 
dI
VMpg - VNpg  mR  I1  I2   mL  1  2   RI2  L 2 (27)
 dt dt 
dt


During pole-to-pole fault, the corresponding two-bus system
model is depicted in Fig. 5. In the occurrence of a pole-to-pole
fault, the voltage drop across the line segment (MN) can be
expressed as
 dI
dI 
VMpp - VNpp  2mR( I1  I2 )  2mL  1  2   2RI2
 dt
dt 

dI2
 2L
dt
(28)
The line voltages in equation (28) are expressed in terms of
the line-to-ground voltage and written as
 dI
dI 
dI
(VMpg - VNpg )  mR( I1  I2 )  mL  1  2   RI2  L 2 (29)
 dt

dt 
dt

By using (27) and (29), the generalized equation for both
fault cases is expressed as
 dI
dI 
dI
(VMpg - VNpg )  mR( I1  I2 )  mL  1  2   RI2  L 2 (30)
 dt
dt 
dt

The parameter presented in equation (30) is obtained using
Least Square (LS) technique [37] and written as

[mR mL R L]T  H T H
where,

H   I1 (ti )  I2(ti )

mR

-1
H T V1 (ti )- V2(ti )
 dI1(ti ) dI 2 (ti ) 
+

  I 2 ( ti )
dt 
 dt

N×1
(31)
dI2 (ti ) 

dt 
N 5
(1-m)R (1-m)L
mL
+
+
VNpg
I2
VMpg I1
RF
VMpp
mR
mL
VNpp
(1-m)R (1-m)L
-
I1
I2
Fig. 5. Two bus system during pole to pole fault.
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6
where the value of N is considered to be 20. mR represents cable
resistance up to the fault point. The fault distance m is
calculated as
m
mR

R
(32)
where ℓ denotes the line segment overall length. The estimated
percentage error of fault location is expressed as
m  mact
 100
% Error = cal
(33)
mact
When teager energy difference exceeds the threshold (ξ) and
the computed fault distance (m) lies in the range 0< m<1, then
the proposed scheme detects that the fault is internal and sends
a trip signal to the SSCB.
Fig. 6 depicts the proposed algorithm flow chart. Initially,
IEDs at the ends of line segments receive voltage and current
measurements from both ends of the line through measurement
sensors and communication links. After that, the IEDs estimate
the teager energy available in the current wave using equations
(18) and (19). Further, the teager energy difference present in
each line segment of positive and negative poles is calculated
with the help of equations (24) and (25). When the difference
of the teager energy at any pole (γp and γn) is more than the
threshold, the proposed technique detects the fault and initiates
the fault classification and fault location techniques. If the
estimated teager energy difference at both the poles is more than
the threshold and the suggested algorithm decides the fault is
pole to pole fault. Or else it decides the fault is pole to ground
Fault detection
Start
Measure the value of V1pg, I1p, V2pg,
I2p,V1ng, I1n, V2ng and I2n of the line MN
Calculate the difference of the teager
present in the current signals
γp >ξ
(or)
γn >ξ
No
Faultclassification
classification
Fault
Yes
γp >ξ
(and)
γn >ξ
Yes
No
Pole to ground
fault
Pole to pole
fault
Fault localization
Yes
Estimate the parameter of mR and R
using the equation (30)
fault. After fault classification, the suggested method initiates
the fault location algorithm. In the fault location technique, the
IEDs estimate the line resistance (R) and resistance of the line
up to the fault point (mR) using equation (30). Further, the fault
distance (m) is estimated using (32). If the estimated fault
distance lies in the range of 0<m<1, then the proposed method
decides fault is internal or else the fault is external. After
detecting the internal fault, the IEDs generate the trip signal to
SSCB to isolate the fault section.
D. Threshold (ξ) Selection
The threshold (ξ) is dependent on the teager energy
difference. From equation (17), the teager energy difference in
the current signal during internal faults depends on teager
energy available at sending and receiving end of the line
segment. However, the teager energy in the current signal
depends on the iL(0), fault distance and fault resistance.
Therefore, to decide the threshold (ξ), a high resistance fault F1
is created with a fault resistance of 50 Ω by varying fault
distances and percentage of loading on DC microgrid. During
high resistance faults, the teager energy in the current signal is
low. Therefore, a high resistance fault is considered to decide
the threshold. For all scenarios, the obtained teager energy
difference is shown in Fig. 7. By examining Fig. 7, the
minimum teager energy difference of 1298 is obtained during a
high resistance fault at fault location 0.9 p.u and 50 % loading.
Therefore, for detecting an internal fault in the DC microgrid,
the threshold magnitude must be less than 1298.
In an ideal scenario, the teager energy difference equals zero
for external faults and is greater than the threshold during
internal faults. However, in the practical scenarios, teager
energy difference is not equal to zero in case of external fault
and load changes. Generally, in the differential protection
scheme, the threshold selection depends on the [38] (i)
Measuring error and noise in the signal, (ii) Cable charging
current, (iii) Communication delay, (iv) CT saturation.
Therefore, to decide the threshold, these factors are also
considered. According to the IEC 61869-2 and IEC 61869-3
standards, the measurement error for the voltage and current
sensors should be less than 3% [39]. Therefore 3%
measurement error in the current signal is considered while
deciding the threshold. Also, 35 dB signal-to-noise ratio (SNR)
in the current signal and cable charging current during double
pole to ground fault is also considered to decide the threshold.
Among these, communication delay and CT saturation effects
are taken into account for deciding the threshold. Thus,
considering the above-mentioned factors, the reliable threshold
(ξ=1000) value for the proposed protection technique is
decided. In any case, the suggested method initiates the fault
Estimate the fault location (m) using
mR, and R
External
fault
No
0<m<1
Yes
Generate trip signal
Fig. 6. Flow chart of suggested fault detection and fault location scheme.
Fig. 7. Teager energy difference during high resistance fault.
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7
× 104
200
150
100
1
0.5
0
VMpg
γ
100
0
2
VNpg
(a)
I1
I2
3.5
Time(s)
3.55
(c)
1
γp
γn
20
10
R(mΩ )
0
× 104
ψ1
4
2
ψ2
0
-2
3.45
(d)
20
10
0
(b)
R
mR
(e)
0.5
d(pu )
ψ
ξ
0
0
3.55
3.5
3.45
3.55
3.5
(f)
Time(s)
Time(s)
(c)
Fig. 9. Pole to Pole fault. (a) Voltages of line MN. (b) Currents of line MN.
(c) Teager energy. (d) Difference of teager energy. (e) Estimated
resistance. (f) Fault distance.
B. During Pole to Pole Fault
In this section, the performance of suggested technique is
verified in the case of pole to pole fault. A pole to pole fault is
established at the midpoint in the line segment MN with a fault
resistance of 0.1 Ω. The simulation results obtained by the
suggested technique at the IEDMN and IEDNM are provided in
Fig. 9. Fig. 9(c) demonstrates the teager energy available in
current wave at the IEDMN is (ψ1) and IEDNM is (ψ2). The
magnitude of the teager energy during the pole to pole fault is
very high compared to the pole to ground fault. Fig. 9(d)
demonstrates the difference in the teager energy of the positive
pole and negative pole. By observing Fig. 9(d), it is clear that
the difference in the teager energy of the positive and negative
poles is more than the threshold. As a result, the suggested
technique decides that the fault is line to line. Fig. 9(e) shows
the estimated line resistance (R) and resistance of the line up to
the fault point (mR). Fig. 9(f) shows that the estimated fault
distance during line to line fault is less than 1 p.u. Subsequently,
the proposed method classifies the fault as internal and the trip
signal is sent to the respective SSCB by the IEDs present at the
ends of the line MN. The proposed method dependability is also
evaluated during the line to ground (positive pole to ground
fault) and line to line faults (F1) by varying the line LM length.
The results obtained by the proposed algorithm are provided in
Table-I. According to Table-I, during the positive pole to
ground fault, the estimated teager energy difference at the
positive pole (γP) exceeds the threshold and the fault location
(m) is less than 1 p.u for all scenarios. Similarly, during the line
to line fault the estimated teager energy difference at the
positive pole (γP) and negative pole (γn) are greater than the
threshold and the estimated fault location (m) is less than 1 p.u
TABLE-I
TESTING OF PROPOSED METHOD BY VARYING LINE LENGTH
Varying line LM length (km)
(d)
R
mR
Parameter
3.5
Time(s)
3.55
(f)
Fig. 8. Line to ground fault. (a) Voltages of line MN (b) Currents of line
MN. (c) Teager energy. (d) Difference of teager energy. (e) Estimated
resistance. (f) Fault distance.
LG fault
LL-Fault
2 (km)
5 (km)
10(km)
2 (km)
5 (km)
10(km)
γP
13817
12942
11715
52706
46544
40739
γn
110.98
63.99
390
52706
46544
40739
R (Ω)
0.0403
0.1006
0.2010
0.0403
0.1005
0.2013
mR (Ω)
0.0204
0.0506
0.0988
0.0199
0.05
0.1005
%Error
1.240
0.59
-1.691
-0.765
-0.497
-0.149
m (p.u)
0.5062
0.5029
0.4915
0.4962
0.4975
0.4992
(e)
0.5
0
3.45
I1
I2
γp
γn
ξ
0
(b)
ψ
× 103
5
0
ψ1
-5
ψ2
-10
3.45
4
×10
0
R(mΩ )
200
d(pu )
Current(kA) Voltage(V)
A. During Pole to Ground Fault
To check the selectivity of the suggested protection
technique during pole to ground fault, a positive pole to ground
fault (F1) is initiated at the midpoint of the line MN with the
fault resistance of 0.1 Ω as depicted in Fig. 1. The line to ground
voltage and line current variation at the IEDMN and IEDNM are
illustrated in Fig. 8. The teager energy of ψ1 and ψ2 present in
the current waves at IEDMNp and IEDNMp is provided in Fig.
8(c). By observing Fig. 8(c), the teager available in the current
wave during the steady state is zero. During the fault, its
magnitude is not equal to zero. The difference in teager energy
(γ) available in the current waves of the positive and negative
poles of the line MN is illustrated in Fig. 8(d). The difference
in the teager energy present in the current wave of the positive
pole (γp) is more than the threshold (ξ), as depicted in Fig. 8(d).
Therefore, the suggested technique initiated the fault location
algorithm at the positive pole IEDs of line MN. After that, by
using equation (30), the total line resistance (R) and resistance
of the line up to the fault point (mR) are estimated as depicted
in Fig. 8(e). The calculated fault distance is less than 1 p.u, as
depicted in Fig. 8(f). As a result, the suggested method
determines that the fault is internal and sends a trip command
to the line segment MN's SSCB.
VNpg
(a)
2
1
IV. SIMULATION AND EXPERIMENTAL RESULTS
A radial LVDC microgrid system is considered to assess the
effectiveness of the suggested fault detection and fault
localization algorithm, as illustrated in Fig. 1. The LVDC
microgrid system is modeled in MATLAB SIMULINK with a
250 µs step size. The suggested fault detection and fault
location technique is tested in various fault scenarios, including
pole to ground faults, pole to pole faults, noise environments,
load switching, high resistance faults, and internal faults by
varying fault resistances and fault distances.
5
VMpg
γ
Current(kA) Voltage(V)
location algorithm during the external fault due to the difference
of the teager energy being more than the threshold (ξ). In that
case, the estimated fault location is more than 1 p.u. Then the
suggested fault detection algorithm decides that the fault is
external. Therefore, the low value of threshold ξ=1000 is
considered to trigger the fault location algorithm for all types of
internal faults up to the fault resistance of 50 Ω.
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500
3.55
(c)
3.5
Time(s)
0
3.45
3.55
(f)
3.5
Time(s)
Fig. 10. During external fault. (a) Voltages of line MN, (b) Currents of line
MN. (c) Teager energy. (d) Difference of teager energy. (e) Estimated
resistance. (f) Fault distance.
for all scenarios. As a result, the proposed method does not
compromise dependability in line to ground and line to line
faults by varying line length.
C. During Pole to Ground External Fault
To check the security of the suggested fault detection and
location algorithm in case of external fault. An external fault
(F2) is initiated in the line LM with a fault impedance of 0.1 Ω
and fault location of 0.5 p.u from bus L. The corresponding
voltage and current at the IEDMN and IEDNM are depicted in Fig.
10(a) and Fig. 10(b). The teager energy available in the current
waves at IEDMN and IEDNM is illustrated in Fig. 10(c). It is
noticed from Fig. 10(c) that the teager energy available in the
current wave at both IEDs is very high during external fault.
But the teager energy difference is much lower than the
threshold, as depicted in Fig. 10(d). As a result, the suggested
method identifies the fault as external. The fault distance is also
estimated to check the fault location technique's robustness in
this external fault case, as depicted in Fig. 10(f). The estimated
fault distance is more than the 1 p.u. As a result, the suggested
algorithm retains its security in the case of an external fault.
1
0.5
0
VNpg
× 104
5
0
ψ1
-5
ψ2
-10
3.5
3.45
Time(s)
0
(b)
3.55
(c)
γp
γn
ξ
(d)
20
10
VMpg
230
1
R
mR
(a)
I2
I1
0
-1
× 103
0
-2
-4
3.45
3.55
(f)
Fig. 11. During internal fault in case of noise environment. (a) Voltages of
line MN. (b) Currents of line MN. (c) Teager energy. (d) Difference of
teager energy. (e) Estimated resistance. (f) Fault distance.
R
mR
(e)
500
ψ1
ψ2
3.5
Time(s)
0
3.45
3.55
(c)
3.5
Time(s)
3.55
(f)
Fig. 12. During EV charger switching. (a) Voltages of line MN. (b)
Currents of line MN. (c) Teager energy. (d) Difference of teager energy.
(e) Estimated resistance. (f) Fault distance.
5
80
200
γn
γp
0
10
(a)
I1
I2
0
(b)
(e)
3.5
Time(s)
γp
γn
(d)
0
-5
-10
-15
(b)
-200
0.5
0
3.45
0.5
0
VNpg
140
0
(a)
I1
I2
1
× 103
1
250
Ipv(A)
γ
VMpg
F. During Variable Generation in DC Microgrid
In order to evaluate the effectiveness of the suggested
algorithm in the scenario of variable generation, the irradiation
50
0
-50
ψ1
-100
3.45
ψ
ψ
100
0
4
2 × 10
R(mΩ )
200
d(pu )
Current(kA) Voltage(V)
D. In Noise Environment
This section evaluates the performance of the suggested
protection technique in a noise environment. With a fault
location of 0.5 p.u from bus M, a pole to ground fault is initiated
in cable MN. After that, a White Gaussian noise is introduced
into the voltage and current waves present at the IED MN and
IEDNM with signal-to-noise ratio (SNR) of 35 dB, as depicted
in Fig. 11. With the help of the suggested technique, the teager
E. During EV Charge Switching
An EV charger is switched in the DC microgrid bus N to test
the suggested algorithm security. The EV's initial state of
charge is considered to be 20%. The corresponding voltage and
current at the IEDMN and IEDNM are depicted in Fig. 12(a) and
Fig. 12(b). The initial current drawn by the EV charger is high,
as shown in Fig. 12(b), so overcurrent-based protection
schemes are becoming less reliable in detecting the fault in
LVDC microgrid systems. However, the proposed method
maintains its dependability during the EV charger switching.
The difference of the teager is less than the threshold, as
depicted in Fig. 12(d). Moreover, the estimated fault distance is
more than the 1 p.u, as depicted in Fig. 12(f). Hence, the
proposed technique maintains its security when EV charge
switching occurs in a LVDC microgrid.
γ
(e)
R(Ω )
(b)
d(pu )
ψ
× 103
4
ψ1
2
ψ2
0
3.45
R
mR
d(pu )
-0.2
γ
0
(d)
10
0
-10
-20
I1
I2
R(Ω )
0.2
ξ
ψ2
3.5
Time(s)
5
(d)
R
mR
0
(e)
500
d(pu )
(a)
γp
γn
Current(kA) Voltage(V)
VNpg
energy difference in positive pole (γp) and negative pole (γn) are
estimated as depicted in Fig. 11(d). By observing Fig. 11(d), the
values of γp and γn are before the fault is not equal to zero due
to noise in the current signals. However, the values of γp and γn
are less than the threshold during the steady state and the value
of the γp is greater than the threshold during the fault scenario.
Moreover, the estimated fault distance (m) during the fault is
less than the 1 pu, as shown in Fig. 11(f). By observing Fig. 11,
it is clear that the proposed algorithm does not lose its security
due to the measured signal consisting of the noise.
ψ
γ
VMpg
100
0
× 103
Current(A)
200
1.5
1
0.5
0
R(Ω )
Current(kA) Voltage(V)
8
3.55
(c)
0
3.45
3.5
Time(s)
3.55
(f)
Fig. 13. During variable generation at bus O. (a) PV output current (b)
Currents of line OM. (c) Teager energy. (d) Difference of teager energy.
(e) Estimated resistance. (f) Fault distance.
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9
of the PV panel connected to bus O is increased by 20% at 3.5
seconds. Due to the increase in irradiation, the current supplied
by the PV panel is increased at 3.5 sec, as shown in Fig. 13(a).
The current measured at both ends of the line segment OM is
shown in Fig. 13(b). With the help of the proposed algorithm,
the teager energies (ψ1 and ψ2) present in the current signals are
estimated at both ends of line segment OM as shown in Fig.
13(c). After that teager energy differences, γ p and γn are
calculated as shown in Fig. 13(d). By observing Fig. 13(d), the
teager energy difference is small and less than the threshold
(ξ=1000). Also, the estimated fault location is greater than 1
p.u, as shown in Fig. 13(f). Therefore, the proposed method
does not lose security in the case of variable generation in the
DC microgrid.
G. Accuracy in Fault Location
To check the accuracy of the suggested fault location
technique, a pole to ground fault F1 is initiated in the line
segment MN for various values of fault distance (m) and fault
resistance (Rf). The fault distance is estimated at the IEDMN and
IEDNM using the proposed fault location technique for all the
above cases. The corresponding estimated error present in fault
location is also calculated and provided in Table-II. Table-II
shows that the maximum error obtained by the suggested fault
distance estimation technique is 3.59 percent.
DC
DC
AC
O
P
IEDPO IEDPQ
IEDOP
N
Q
IEDOP
IEDQP
DC
load
IEDQL
IEDNM
F3
IEDMN IEDML
IEDLM
IEDLQ
L
M
DC
DC
DC
0
1
0
-1
-2
1
0.5
0
10
5
0
DC
(b)
1
γ
DC
load
VMpg
VNpg
200
100
0
R(mΩ )
IEDNO
d(pu )
DC
Bidirectional Islanding
converter protector
ψ
Utility grid
DC
load
Current(kA)Voltage(V)
H. Change in Topology and Cable Parameters
The proposed scheme is validated for the different DC
microgrid topologies by considering a meshed bipolar DC
microgrid, as shown in Fig. 14(a). The ratings of the sources
and loads presented in the meshed DC microgrid are same as
the rating of the sources and loads in the radial DC microgrid,
as shown in Fig.1. The cables in the meshed DC microgrid have
a resistance of 0.12 Ω/km, an inductance of 300 μH/km, and a
capacitance of 0.5 μF/km, respectively. To test the suggested
method for the meshed DC microgrid a line to ground fault (f3)
is initiated in the line MN has a fault resistance of 0.1 Ω as
I1
I2
× 103
× 104
0.5
0
3.45
Solar Panel
(a)
Battery
(c)
ψ1
ψ2
(d)
γp
γn
(e)
R
mR
(f)
3.5
Time(s)
(g)
3.55
Fig. 14. During a line-to-ground fault in the meshed DC microgrid. (a)
Meshed DC microgrid (b) Voltages of line MN. (c) Currents of line MN.
(d) Teager energy. (e) Difference of teager energy. (f) Estimated
resistance. (g) Fault distance.
Rf
m
0.1 p.u
0.2 p.u
0.3 p.u
0.4 p.u
0.5 p.u
0.6 p.u
0.7 p.u
0.8 p.u
0.9 p.u
TABLE-II
FAULT LOCATION ESTIMATION ERROR
0.01 Ω
1Ω
5Ω
10 Ω
15 Ω
0.0845
0.0136
0.0125
0.0997
0.3488
0.7451
1.2792
1.9415
2.7203
0.2575
0.3067
0.0303
0.6868
1.6788
0.3994
1.3357
2.9167
3.5993
0.0481
0.2193
0.2997
0.2815
0.0357
1.2377
1.0254
1.8405
2.8441
0.15
0.1382
0.3406
0.7846
1.0478
1.3783
1.3699
1.4421
1.4535
0.4523
0.0266
0.4972
0.7994
1.1219
1.3783
1.5807
1.7286
1.8275
20 Ω
0.6565
0.0266
0.3792
0.7872
1.1458
1.4321
1.6542
1.8156
1.9183
shown in Fig. 14(a). The line to ground voltage and line current
variation at the IEDMN and IEDNM are illustrated in Fig. 14. The
difference in the teager energy present in the current wave of
the positive pole (γp), is more than the threshold (ξ), as depicted
in Fig. 14(e). Therefore, the suggested technique initiated the
fault location algorithm at the positive pole IEDs of line MN.
After that, by using equation (30), the total line resistance (R)
and resistance of the line up to the fault point (mR) are estimated
as depicted in Fig. 14(f). The calculated fault distance is less
than 1 p.u, as depicted in Fig. 14(g). As a result, the suggested
method efficiently detects and locates the fault in the meshed
DC microgrid system by varying the cable parameters.
I. Comparison Study with Existing Protection Scheme
The proposed technique is compared to some sophisticated
fault identification and location schemes developed in the DC
microgrid. Table-III compares and evaluates the proposed
approach with other previous techniques [7] [24], [26], [27],
[28] in detail. According to Table-III, the proposed approach
takes less time to identify faults than the technique presented in
[27], [26]. The proposed approach can identify and locate high
resistance faults with a fault resistance of 50 Ω, where existing
protection schemes [7] [24], [26], [28] fail to work. The
TABLE-III
COMPARATIVE ANALYSIS OF THE PROPOSED TECHNIQUE WITH PREVIOUS
PROTECTION METHODS
Parameters
The time it takes for a
fault to be detected
(ms)
The maximum R f is
taken into account.
Mal operation in the
case of temporary
fault
Fault classification
Maximum estimated
percentage error in
fault distance (%)
Fault location
accuracy considering
SNR of 20 dB (%)
For fault detection
and location, an
external circuit is
required.
DC
microgrid
incorporated with the
EV charging station
Cable information is
necessary for fault
location calculations.
Real-time validation
PS
2
[24]
1.25
Various methods
[26]
[28]
100
1
[27]
19
[7]
1.25
50 Ω
25 Ω
2Ω
2Ω
100 Ω
10Ω
No
No
No
Yes
Yes
No
Yes
3.59
Yes
7.1
No
6.42
No
9.75
No
1.6
No
8.7
95.8
94.1
92.6
91.3
1
75.3
No
No
No
Yes
No
No
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
PS denotes the proposed algorithm
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© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
This article has been accepted for publication in IEEE Transactions on Industrial Informatics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TII.2023.3252409
10
(d)
γ
(a)
I1(A)
2
1
0
×103
(b)
(c)
I1+I2(A) I2(A)
(f)
(e)
d(pu ) R(mΩ )
20
10
0
ξ
γp
γn
R
mR
Fuel cell
Boost
converter
Boost
converter
Load
Bus C
Buck converter
Load
A
VC
V
A
IC F2
3 Phase Rectifier
Bidbuck I1-6 PVboostGbuck Fboost Mbuck
24 v DC from
SMPS
Capacitor Bank
(20uf, 230V)
Half Bridge DO
F1
FPGA Board
15V-0
SEIG
Simultaneous A/in
VB
V
Va Vb Vc Ia Ib Ic N Vpv IpvVB IB Vcp
Speed senscor
A IB
A
DC
Motor
Bus B
R-C
Filter
&
Transformer
R B Y N
Aux In
+15/0/-15
Inverter
Load
Bidirectional
converter
ARM
2200uf
190
185
180
175
-173
-174
-175
10
5
0
3.45
Hydrogen
gass
PV
Emulator
1 Phase Rectifier
J. Experimental Validation.
HIL experimentation is also carried out to validate the result
obtained through the MATLAB simulation and test the
feasibility of the suggested algorithm in a practical
environment. The HIL setup consists of four distributed energy
resources solar emulators of the 2 kW, a stack of 8 lead-acid
batteries, each with 12 V, 60 Ah, a fuel cell of 1 kW, and a wind
emulator of 2 kW. All the distributed generators are interfaced
to DC microgrid through the DC to DC converter, as depicted
in Fig. 16. The DC microgrid setup is also connected with the
utility grid with the help of VSC. The VSC at utility grid
maintains DC microgrid voltage of 110 V. VSC plays an
important role in synchronizing DC microgrid with the
P
N
Control signal
field
proposed method produced a maximum of 3.59 percent error in
fault location estimation, which is less than existing techniques.
The proposed algorithm does not require an external circuit to
identify the fault and estimate its location. On the other hand,
the methods in [24] require an external circuit for fault
identification and location estimation. The proposed algorithm
does not require cable information to determine the location of
the fault. However, the methods in [7], [24], [26]–[28] need
cable information to estimate fault location. Most of the
existing fault detection and location methods ignore the effect
of EV charging stations in the DC microgrid system when
detecting and locating faults.
A simulation case study is also conducted to compare the
suggested fault detection method with existing differential
protection schemes [11],[12]. In this case, a high impedance
fault of 50 Ω is initiated at the midpoint of line segment MN.
The fault current at the IEDMN and IEDNM are delineated in Fig.
15(a) and Fig. 15(b). The current direction at both IEDs is not
changed, as illustrated in Fig. 15(a) and Fig. 15(b), so the
differential protection technique depends on the sign of current
[12] field to operate in case of a fault with high resistance. The
current differential (I1+I2) is also estimated, and its magnitude
is very small, as delineated in Fig. 15(c). Therefore the
protection technique depends on the magnitude of the current
difference [11] and may fail during the high resistance fault. In
this case, with the help of the suggested algorithm, the teager
energy difference and fault location are calculated as depicted
in Fig. 15(d) and Fig. 15(f). From the figures, the calculated
teager energy difference is greater than the threshold.
Moreover, the calculated fault location is less than 1 p.u, as
depicted in Fig. 15(f). According to the case studies discussed
above, the suggested protection scheme outperforms all
existing protection schemes.
P
N
1Pole Switch
10A fuse
Gate signal 15V-0 10A DC MCB
from FPGA
Motor Buck
converter
2200uf
3 Phase Rectifier
Battery Bank
3 Phase Auto
trans
4 pole MCB
Grid
B Y R
Grid
Fig. 16. Schematic diagram of the DC microgrid hardware setup.
conventional grid. HIL experimentation setup can operate gridconnected mode and islanding mode operation. In the gridconnected mode, VSC at the utility grid operates in the grid
forming mode remaining all converters operating in grid
feeding mode. During islanding condition, bidirectional
converter at the battery operations in the grid forming mode
remaining converters at DGs operates in grid feeding mode. All
the converter control circuits are used in the experimental setup
that can be implemented in the LABVIEW platform and
interfaced through Field Programmable Gate Array (FPGA). A
DC line attached between DC microgrid and the battery bank
has a line resistance of 2 Ω and inductance of 0.5 H, as depicted
in Fig. 16. The voltage and current of the LVDC microgrid are
measured using the LV20-P-718331 voltage sensor and the
LA25-P-13022 current sensor. The measured voltage and
current waves are sent to the d-SPACE 1104 controller to
process further the suggested fault detection and location
algorithm depicted in Fig. 17.
To test the proposed algorithm in HIL environment, internal
fault (line to line and line to ground fault) at F1 location and
external fault (line to ground fault) at F2 location are created
with a fault resistance of 15 Ω as illustrated in Fig. 16. In all
0.5
3.5
Time(s)
3.55
0
3.45
3.55
3.5
Time(s)
Fig. 15. During high resistance fault. (a) Sending end current at line MN.
(b) Receiving end current at line MN. (c) Differential current (d)
Difference of teager energy. (e) Estimated resistance. (f) Fault distance.
Fig. 17. The DERs have been used in the experiment.
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© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
This article has been accepted for publication in IEEE Transactions on Industrial Informatics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TII.2023.3252409
11
IB
2 A/div
2 A/div
2 A/div
2 A/div
5unit/div
γp
0.1 Ω /div
mR
2 A/div
IC
2 A/div
IB
IC
IC
IB
γp
mR
(a)
5unit/div
γp
5 unit/div
0.1 Ω /div
mR
0.1 Ω /div
(c)
(b)
Fig. 18. The HIL results during fault. (a) Internal line to line fault. (b) Internal line to ground fault (c) External line to ground fault.
fault scenarios the current at both ends of the line segments
increases rapidly due to the discharge filter capacitor in the
converters shown in Fig. 18. By observing the Fig. 18, during
internal faults the currents at the line segment increased in same
direction. However, in the case of external fault, the currents
are increased in opposite directions. In all fault scenarios, the
teager energy present in the current waveform at the sending
and receiving ends of the line segment is estimated, and the
teager difference is calculated using the d-SPACE controller.
During internal pole to pole and pole to ground fault, the teager
energy difference in the positive pole is greater than the
threshold, as shown in Fig. 18(a) and Fig. 18(b). Therefore,
proposed initiate the fault location algorithm in the case of an
internal pole to pole and pole to ground fault. However, the
teager energy difference is less than the threshold during an
external fault, as shown in Fig. 18(c). As a result, proposed
method decides fault is external and does not initiate fault
location algorithm. To compute the fault location during
internal faults, the d-SPACE evaluates the line resistance up to
the fault point, as depicted in Fig. 18(a) and Fig. 18(b). The
estimated fault location during the internal pole to pole and pole
to ground faults are 0.4832 p.u and 0.4875 p.u, respectively. As
a result, the proposed scheme identifies the faults are internal
and d-SPACE generates the trip signal. Observing the HIL and
simulation results during an internal fault, the suggested
technique computes the teager energy and fault location in less
than 1 ms. As a result, the proposed algorithm's overall fault
clearing time is 2 ms is considered. Total fault clearing time
includes the relay computation time, the operating time for the
SSCB of 50 μs [8] and a 1 ms communication delay.
V. CONCLUSION
This research work focuses on fault detection and
localization in LVDC microgrid. Using the IEC 61850
communication protocol, the fault in the DC microgrid is
detected by comparing the teager energy available in the current
wave at both ends of the line segment. The teager energy
available in a DC waveform is zero during a steady state. In the
case of a transient, its magnitude is not zero. After the fault has
been detected, the fault location is computed by estimating the
line resistance up to the fault point and the actual line resistance
by using the least square technique. The proposed approach can
assess the fault location even though cable parameters are
unknown. In 2 ms, the proposed algorithm can detect and locate
faults has a fault resistance of up to 50 Ω. The suggested method
is robust to the noise environment and loads switching
conditions. The comparison studies prove that the suggested
technique is superior to the existing fault detection and location
methods.
APPENDIX A
Table III demonstrates the DC microgrid system information in
Fig. 1.
TABLE-IV
TEST SYSTEM PARAMETER
Operating voltage
480V
Ratted power
Grid VSC
solar DC to DC converter
Battery bidirectional converter
PV panel
Battery
DC Load
EV battery capacity
DC bus capacitance
Cable Resistance(R)
Cable Cross-section area
Cable Inductance(L)
Cable Capacitance
3 MW
1 MW
1*0.5 MW
1*0.5 MW
Vmp=54V, Imp=5.58A, Voc=60V
Isc=7.8654A
Voltage=12 V, capacity=6 AS
1*250 kW
85 kWh
25 mF
10 mΩ/km
240 mm2
100 µH/km
0.67 µF/km
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