Novel Method of Low and High Impedance Fault Detection in ... · intelligent approach based on fuzzy inference system (FIS) for fault detection is presented for LVDC microgrids, where

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]).

mailto:[email protected]




in Power System



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.


in Power System



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 (


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


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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


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


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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).


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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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Documents

Novel Method of Low and High Impedance Fault Detection in ... · intelligent approach based on fuzzy inference system (FIS) for fault detection is presented for LVDC microgrids, where