Novel Method of Low and High Impedance Fault Detection in ... · intelligent approach based on...
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11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
Novel Method of Low and High Impedance Fault
Detection in LVDC Microgrids
Ali Abdali, Kazem Mazlumi, Reza Noroozian Faculty of Electrical and Computer Engineering
University of Zanjan
Zanjan, Iran
Abstract— Despite AC classical systems, protection of DC
system is a real challenging task. The method of high and low fault
detection for LVDC microgrids using fuzzy interface systems is
presented in this paper. Based on specific rules and conditions,
fuzzy inference systems try to make the most appropriate decision
for each system state as quickly as possible. The aim of this paper
is the fast detection of low and high impedance faults in LVDC
microgrids, regardless of the type and amplitude of fault current
and the power supply capacity, by instantaneous current
monitoring. So, that the entire system would not experience an
outage, while the faulted segment is isolated. To do so, an LVDC
ring-bus microgrid is used that utilizes solid-state bidirectional
switches along with master and slave controllers.
Keywords— Fault detection; Fuzzy controller; LVDC
microgrids; Circuit breaker (CB).
I. Introduction
Many numbers of studies have been conducted recently to
develop and arrive at favorable conditions of utilizing
renewable energy resources such as the wind and solar energy
in electrical energy distribution networks. Additionally,
distributed generation systems have advantages over traditional
power generation procedures at centralized power plants due to
their high reliability, environmental compatibility, easier
controllability and higher performance [1], [2]. Microgrid
systems are small-scale power grids that consist of renewable
energy sources and loads, and are capable of instant integration
with renewable energy resources whenever required [3]– [5].
Microgrids may generally be categorized into DC and AC
systems. The advantage of AC microgrids is the possibility of
directly using distributed generation sources that are based on
AC voltages; however, synchronization, reactive power
control, and voltage stability are among their disadvantages.
However, DC microgrids are considered to be a feasible
solution, since they are local small grids with fewer
transmission losses. In addition, they lack the defects of AC
systems, and the size of AC-DC-AC converters used may be
significantly reduced [5]. Fig. 1 shows a conceptual schematic
of a DC ring bus microgrid.
Fig. 1. Conceptual schematic of an LVDC ring bus microgrid.
For small scale systems, LVDC microgrids have many
advantages over traditional AC microgrids. Both AC and DC
microgrids require power electronic converters and are used to
connect loads and sources to a common bus. Therefore,
employing DC systems requires fewer converters [6], [8], [9].
In addition, DC system can transmit √2 times that of an AC
system with the same Do cables. Also, DC system is not
affected by the skin effect and can use the entire cable thus
reducing transmission losses [9], [10].
Despite their significant advantages, the protection of DC
microgrids poses many challenges and, in addition, no written
standards, solutions, or experience exist in regard to this topic
[6]. In a distribution system, the capability of precise fault
detection provides us with advantages such as quick repair,
maintenance, and restoration, leading to reduced duration of
power interruption [7], [8]. Various fault protection solutions
have been proposed for LVDC distributed systems including
overcurrent protection [6], [9], [10], derivatives of current [6],
under- voltage and directional protection [9]. However, the
dynamics of voltage and current were not considered, and this
method causes an unnecessary outage of sources and loads in
DC microgrids. A differential method of fault detection and
isolation LVDC bus microgrids was proposed in [11]. The
disadvantage of this method is the use of a specific threshold
This work was supported by the University of Zanjan.
The authors are with the Department of Electrical and Computer Engineering,
University of Zanjan, Zanjan 45371-38791, Iran (e-mails:
[email protected], [email protected] and [email protected]).
11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
for fault detection, meaning that the fault occurs when line
current exceeds the threshold. The threshold value is
determined based on the operator’s experience, which is a
defect. The present research aims at correcting the
aforementioned defect of the [11] so that human actions and
considerations would not affect fault detection. An expert,
intelligent approach based on fuzzy inference system (FIS) for
fault detection is presented for LVDC microgrids, where after
the fault is cleared, the whole network remains online.
II. Fault Detection Based on Differential Method
In this section type of faults, available protection equipment
for DC systems, differential fault detection method, and its
controller are mentioned.
A. Possible faults in DC microgrids
Two type of faults may occur in DC microgrids [11]. Line
to Line (LL) fault, and Line to Ground (LG) fault. An LL fault
is one where short-circuit occurs between positive and negative
lines in a system, while an LG fault is one where short-circuit
occurs between one line of the system, either positive or
negative and the earth. This is the most common type of fault
in industrial distribution systems [12].
B. Available protection equipment for DC systems
Fuse and CBs are the available protection equipment for DC
systems [6]. Due to limitations of fuses and AC CBs in DC
systems, a solid-state CB is considered to be an appropriate
option for DC protection. There are different options including
gate turn-off (GTO) thyristor, insulated gate bipolar transistors
(IGBTs) and integrated gate commutated thyristor (IGCT),
among which IGCT is the best choice [13]- [16].
C. The controller
A new protection structure for DC-bus microgrid systems
was proposed in [11]. Instead of a complete system shutdown
or DC-bus current limiting, the fault is first detected and then
isolated from the system and to allow the rest of the system to
keep operating. To this end, a loop-type common DC bus was
proposed to make the microgrid robust in faulted condition. It
is also shown that loop-type systems are more efficient,
particularly when transmission lines are not very long [17]. The
total loop type bus is divided into a series of segments between
subsystems. Each segment includes a section of the bus and a
segment controller. A schematic of the protection method is
depicted in Fig. 2. The proposed protection system consists of
one master controller, two slave controllers and freewheeling
branches between each line and the ground.
D. Fault detection based on differential method
Master controller calculates and monitors the difference
between input and output currents, and slave controllers are
responsible for measurement of these currents:
Idiff = Iin - Iout (1)
Where Iin and Iout are input and output currents of the bus
segment. When the difference value exceeds a threshold, the
master controller identifies it as a fault and gives appropriate
Fig. 2. Schematic of the proposed protection structure (Similar controllers also
exist in segments B and C, but they are not depicted).
commands to the slave controllers. While the faulted segment
has been isolated, the remainder of the system can continue to
operate on the ring-bus. As noted before, the major weakness
of this method is the use of a threshold for detecting faults. This
value is determined based on operator’s experience, and can
obviously affect fault detection and isolation speed.
III. The Proposed Fault Detection Using FIS
In this section, the proposed fault detection based on FIS and
fuzzy rules are presented.
A. Investigation of fault detection algorithm in specific
conditions
A common bus is divided into different segments. Every
segment is continuously monitored and its current is measured
by the two slave controllers. The speed and accuracy of fault
detection of the master controller highly depend on its ability to
analyze the data and the fault detection algorithm. For example,
regarding the method in the II-D, we can see that a fault is
detected when a current difference between the two segments
exceeds a threshold; otherwise, no action is taken. Consider the
two following cases:
1) A high threshold is selected.
2) A threshold lower than the requirement is selected.
In the first case scenario, if a large threshold is set, there is
the possibility that current difference between the two segments
does not exceed the defined threshold. Therefore, the master
controller will be unable to detect faults in this situation. Also,
the fault current amplitude depends on network resistance and
fault current path. If the impedance at fault location is large or
a large resistance exists in the fault current path, the maximum
fault current decreases. Hence, the master controller will be
unable to detect large impedance faults.
On the other hand, the fault detection threshold value may
be reduced to overcome these problems. The decrease of
threshold means the higher sensitivity of the protection system,
which possibly causes the master controller to make a wrong
decision and trip the system due to power swings or
measurement noise, while in fact, no fault has occurred. In such
circumstances, a low threshold value is set, and attempts for
faster fault detection has resulted in lower detection accuracy.
11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
Fig. 3. Current flow direction in faulted segA.
According to the above explanations, the performance and
accuracy of differential method significantly depend on the
fault detection threshold value. Therefore, it is suggested that
another criterion as expert system be added to the decision-
making unit.
Based on specific rules and if p, then q conditions, FIS
attempt to make the most appropriate decision for each system
state as quickly as possible. Further, another fault detection
criterion for LVDC microgrids is proposed and defined in the
next section. Then an intelligent fuzzy controller is used as a
replacement for the previous controller, where can detect faults
as quickly as possible.
B. Definition rate of change of current difference and current
direction as criterion for low impedance fault detection
For a better understanding of the new criterion, assume the
low impedance fault occurs in segment A (segA), which is to
be investigated. The input current to segA is calculated as
follows:
Input current to segA (Iin) = Iload + Ifault1
Where Ifault1 is the fault current entering the segA. The
output current from the second end is determined as follows:
output current from segA (Iout) = Iload - Ifault2
Where Ifault2 is the fault current exiting the segA. Fig. 3.
demonstrates these currents.
It can be concluded that in the low impedance fault
condition in segA, the current on the source side increases,
since fault current is added to the load current. However, the
current on the load side decreases. It is easily understood that if
the fault current on the load side is greater than the load current,
the current direction on the load side is reversed. Hence, the
following two modes can be concluded (Table I).
TABLE I. CONDITIONS OF INPUT AND OUTPUT CURRENTS IN
EACH SEGMENT
Without Fault Fault Occurrence
Iin =ILoad Iin > ILoad
Iout =ILaod Iout < ILoad
During normal operating conditions when no fault has occurred,
the transmission line current flowing through segA is identical
at both input and output of segA. While a low impedance fault
occurs in a segment like segA, either the input or output current
rapidly rises, meaning that its rate of change has become
positive. Meanwhile, the rate of change of current on the other
segment side turns negative. The categorization is represented
as rules, which will be fed to the FIS so that decisions at every
moment would be made according to these rules.
Rule 1. IF Iin and Iout are identical, THEN no fault has
occurred.
Rule 2. IF Iin and Iout are decreasing, THEN no fault
has occurred.
Rule 3. IF Iin and Iout are increasing, THEN no fault
has occurred.
Rule 4. IF Iin is increasing and Iout is decreasing,
( 𝑑𝐼𝑖𝑛
𝑑𝑡> 0) and (
𝑑𝐼𝑜𝑢𝑡
𝑑𝑡< 0), THEN a fault has
occurred in segA.
Rule 5. IF Iin is decreasing and Iout is increasing,
( 𝑑𝐼𝑖𝑛
𝑑𝑡< 0) and (
𝑑𝐼𝑜𝑢𝑡
𝑑𝑡> 0).
Rule 6. IF Iin and Iout are entering into segA, THEN a
fault has occurred, even if no another condition is true.
These 6 rules help the FIS monitor currents in each segment
to make the most suitable, accurate decision based on the rate
of change of current and current direction. The 6 rules as
criterion based on the rate of change of current and the criterion
based on current direction are included in Table II and Table III
for defining the proposed FIS algorithm. The current direction
criterion has priority over the rate of change criterion, and if a
fault is detected based on the current direction, the output of the
rate of change of current needs not be calculated and detection
of a fault can immediately be announced.
TABLE II. THE CURRENT DIRECTIONS IN EACH SEGMENT IN
LOW IMPEDANCE FAULT
Iin Direction Iout Direction Fault Occurrence
Entering Exiting ×
Exiting Entering ×
Entering Entering
TABLE III. THE RATE OF CHANGE OF CURRENT IN EACH
SEGMENT IN LOW IMPEDANCE FAULT
Iin Iout Fault Occurrence
0 0 ×
Decreasing Decreasing ×
Increasing Increasing ×
Decreasing Increasing
Increasing Decreasing
11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
The symbols in the Table II and Table III represent more
important outputs, meaning that fault occurrence is certain. The
proposed fuzzy controller is the main controller in the
protection of LVDC microgrid, and the differential method acts
as a backup controller supervising this intelligent system.
C. Definition current direction as criterion for high
impedance fault detection
In the high impedance fault condition, it is not easy to fault
detection based on the rate of change of current difference.
Because rate of change of current difference in source side
becomes positive (𝑑𝐼𝑖𝑛
𝑑𝑡> 0), but rate of change of current
difference in load side is not specified, and it is depended on
fault impedance. According to the fault impedance rate of
change of current difference in load side is likely to be positive
or negative ( 𝑑𝐼𝑜𝑢𝑡
𝑑𝑡< 0 or
𝑑𝐼𝑜𝑢𝑡
𝑑𝑡> 0). So, for this reason decision
based on rate of change of current difference as criterion is not
authentic. As a result, in high impedance fault, fault detection
should be done based on current direction as criterion. In such
circumstances, current direction in source in load side the is
entering. According to this rule, high impedance faults could be
detected in ring-bus LVDC microgrids.
IV. Simulation Results
Simulations are conducted on a microgrid model consisting
of a source, load, and energy storage. A case study microgrid is
depicted in Fig. 4. The microgrid includes 3 buses and 3
segments. Different segments are named as segA, segB, and
segC. The voltage of DC supply source is assumed to be 240V,
and each segment of the DC bus is a 0.2km cable and
parameters of the network are presented in table IV. The
snubber circuit is also connected in parallel with each switch to
suppress voltage overshoots due to the line inductance effect.
The snubbers employed are of RCD type [18].
TABLE IV. SIMULATION PARAMETERS
DC BUS Bus voltage 240V
Cable cross-section area 241.9mm2
Unit resistance Ru 121mΩ/km
Unit inductance Lu 0.97mH/km
Unit capacitance Cu 12.1nF/km
Segment length l 200m
Fault location d 100m
Ground resistance RG 0.5Ω
Freewheeling resistance Rfw 1Ω
Snubber resistance RS 10Ω
Snubber capacitance CS 10µF
A. Low impedance fault detection
The simulation conditions are similar to compare with the
differential fault detection method. Hence, the LG low
impedance fault is applied to in the middle of segA at t=1ms.
The fuzzy rules for the protection algorithm are defined using
“AND”, “OR” operators. Assessment of the results indicated
that using the protection system based on differential method
take 250µs to detect a low impedance fault and send an isolation
Fig. 4. Simulation circuit for the LG fault in the microgrid system
11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
Fig. 5. Source side current in segA(top) and Load side current in segA(bottom).
Fig. 6. Load voltage in the presence of fuzzy protection(top) and fault current
flowing through the freewheeling diode path(bottom).
command, while this value is 30µs for a fuzzy decision-making
controller. Fast performance together with sufficient accuracy
is the definite advantage of fuzzy protection over the current
differential protection. Fuzzy system carefully monitored the
currents and their variations at both ends and will trip few
moments after the occurrence of the fault when the currents and
their rate of change on both sides steeply increased and started
to deviate.
Fig. 5 shows input and output currents of segA. As can be
seen, in the first moment's differential protection and fuzzy
systems experience similar current variations. However, the
fuzzy controller quickly predicts the exceeding from the
threshold value and sends an isolation command. While the
differential protection system waits for the current difference to
reach the threshold value, resulting in a 220µs delay in sending
the command. By using the intelligent fuzzy protection system,
fault currents experience much smaller maximum values,
causing less damage to the microgrid equipment.
Fig. 6(top) compares load voltages in both controllers. As
can be seen, fuzzy protection could manage to restore the
voltage and maintain normal operational conditions by quickly
detecting the fault and isolating the respective segment. A
comparison of fault current flow in the freewheeling diode
branch path is also demonstrated in Fig. 6(bottom). By faster
fault detection and prevention of large increases in fault current,
fuzzy protection resulted in smaller current in the freewheeling
path compared to differential protection mode. This means
lower protection costs and the feasibility of using simpler
smaller diodes.
Fig. 7. Voltages across the switch when segA is isolated.
Fig. 8. The voltage of segC due to fault occurrence in segA(top) input and
output currents in segC(bottom).
Fig. 7 shows switch voltage stress. Taking advantage of
fuzzy protection resulted in decreasing maximum voltage stress
on the isolated switches, which reduces their installation costs
in addition to allowing the use of switches with lower rated
insulation voltages.
The voltage of segC is given in Fig. 8(top). Similar to the
load voltage, voltages in different segments of the common bus
are also restored quickly. In addition to fewer voltage drops in
the event of a fault, fewer overshoots are also recorded during
voltage restoration by fuzzy protection. Simultaneous graphs of
input and output currents in segC are depicted in Fig. 8(bottom).
These currents demonstrate similar behaviors and rates of
change. Thus, the results show that the FIS correctly see no
fault. Currents overshoots experience a significant reduction,
and with segA isolated, segC take the responsibility of load
supply.
B. High impedance fault detection
In this section, the high impedance fault detection is
discussed. Fig. 9 and Fig. 10 indicate source and load side
current in segA for LG and LL for high impedance fault
respectively. As can be seen, it’s obvious that in this condition
differential protection was unable to detect the fault occurrence,
but fuzzy protection quickly predicts the fault occurrence in
source side and sends an isolation command.
Fig. 11 compares load voltages in both controllers. As can
be seen, fuzzy protection could manage to restore the voltage
and maintain normal operational conditions by quickly
detecting the high impedance fault and isolating the respective
segment, whereas the differential controller was unable to
detect the fault and make the isolation decision within the same
time interval. As can be seen, by differential protection for high
impedance fault, load voltage could not have reached to the
nominal value (120V).
11th International Conference on Protection & Automation
in Power System
January 18 and 19 2017,
Iran University of Science & Technology, Tehran, Iran
Fig. 9. Source side current in segA (top) and Load side current in segA(bottom)
for LG high impedance fault.
Fig. 10. Source side current in segA (top) and Load side current in
segA(bottom) for LL high impedance fault.
Fig. 11. Load voltage for high impedance fault
V. Conclusion
This paper is proposed a novel high and low fault detection
method for LVDC microgrids. The proposed method is based
on fuzzy inference system. The proposed protection method
included expert controllers which are able to detect high and
low faults more quickly than the other existing methods. Then
the faulted segment is isolated to prevent overall system
shutdown. Fast fault detection is the advantage of the proposed
method, which reduces system protection costs and enables to
use equipment with lower insulation withstand. The proposed
approach may be implemented in different DC systems, e.g.
green buildings with sustainable energy resources.
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