Computers, Materials & Continua Tech Science Press

DOI:10.32604/cmc.2021.014786
Article

An Efficient Fuzzy Logic Fault Detection and Identification

Method of Photovoltaic Inverters
Mokhtar Aly1,2 and Hegazy Rezk3,4, *

1
Department of Electrical Engineering, Aswan University, Aswan, 81542, Egypt
2
Electronics Engineering Department, Universidad Tecnica Federico Santa Maria, Valparaiso, 2390123, Chile
3
College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University,
Wadi Addawaser, 11991, Saudi Arabia
4
Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt
*
Corresponding Author: Hegazy Rezk. Email: hr.hussien@psau.edu.sa
Received: 16 October 2020; Accepted: 17 November 2020

Abstract: Fuzzy logic control (FLC) systems have found wide utilization in
several industrial applications. This paper proposes a fuzzy logic-based fault
detection and identification method for open-circuit switch fault in grid-tied
photovoltaic (PV) inverters. Large installations and ambitious plans have been
recently achieved for PV systems as clean and renewable power generation
sources due to their improved environmental impacts and availability every-
where. Power converters represent the main parts for the grid integration of
PV systems. However, PV power converters contain several power switches
that construct their circuits. The power switches in PV systems are highly sub-
jected to high stresses due to the continuously varying operating conditions.
Moreover, the grid-tied systems represent nonlinear systems and the system
model parameters are changing continuously. Consequently, the grid-tied PV
systems have a nonlinear factor and the fault detection and identification
(FDI) methods based on using mathematical models become more complex.
The proposed fuzzy logic-based FDI (FL-FDI) method is based on employing
the fuzzy logic concept for detecting and identifying the location of various
switch faults. The proposed FL-FDI method is designed and extracted from
the analysis and comparison of the various measured voltage/current compo-
nents for the control purposes. Therefore, the proposed FL-FDI method does
not require additional components or measurement circuits. Additionally, the
proposed method can detect the faulty condition and also identify the location
of the faulty switch for replacement and maintenance purposes. The proposed
method can detect the faulty condition within only a single fundamental line
period without the need for additional sensors and/or performing complex
calculations or precise models. The proposed FL-FDI method is tested on
the widely used T-type PV inverter system, wherein there are twelve different

This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
2284 CMC, 2021, vol.67, no.2

switches and the FDI process represents a challenging task. The results shows
the superior and accurate performance of the proposed FL-FDI method.

Keywords: Fault detection and identification; fuzzy logic; T-type inverter;

photovoltaic (PV)

1 Introduction
Recently, photovoltaic (PV) generation systems have found wide concerns in electricity gen-
eration due to the reductions in the reserves of fossil fuel sources [1]. Additionally, the negative
impacts on environment and the global warming concerns represent the main issues of the
conventional fossil fuel generations. This in turn has resulted in increasing the installed PV systems
in the utility grids in addition to the ambitious future installation plans. The recent energy reports
show that the electricity generation by PV systems is increasing exponentially [2].
The increased installations and the dependency of future grids on the PV generation system
have increased potential for the reliability, energy efficiency, and cost reduction as major concerns
for industry and research [3,4]. The field based statistics have showed that the power converters
of PV systems are the most vulnerable part amongst the other parts. Power converters are mainly
constructed of electrolytic capacitors, power switches, and passive elements. The power switches
related failures are the most common types of shutdown of PV systems [5–7]. Therefore, detection
of the power switches faults and identify their locations are very important for reliable operation
of PV generation systems.
From another side, multilevel inverters have proven superior performance over the traditional
two-level topologies for wide output power ranges [8]. The main playing factors behind these
advantages are the low dv/dt values, reduced harmonics, and low voltage ratings of components.
Moreover, the output grid filter components are reduced by using multilevel inverter topologies.
This in turn results in reducing volume, weight, and cost of PV systems. There are several
multilevel inverter topologies in the literature for the grid-tied PV installations. Among the var-
ious existing topologies, the T-type three-level inverter topology has been widely presented from
research and industry [9,10]. The main advantages of T-type topology are the elimination of
clamping diodes and flying capacitors, using lower rating switches, and reducing the conduction
losses in the power switches.
The continuous power supply of PV systems requires the fast detection of abnormal con-
ditions due to faults and transients. The accurate and fast detection of faults would reduce the
number and time of maintenance and repair of faulty parts [11]. Mainly, there are two different
types of faults in power switches, including the open circuit fault, and short circuit fault. In
the short circuit faults, the system is required to shutdown to prevent accumulated damage of
the other parts due to the high abnormal generated fault overcurrent [4]. Normally, fuses are
utilized to open the circuit of the power switch at short circuit faulty condition. Whereas, the open
circuit faults introduce distortions in the output of the inverter and proper detection is highly
needed. The open circuit faults in T-type inverters lead to imbalance among the dc-link capacitors,
which accumulates with time and hence higher voltage stresses on the capacitors and switches are
produced [12,13]. Thence, accurate fault detection and identification of faulty switch location is
highly important in order to increase the reliability, and stability of PV generation system supply.
Several methods have been presented in the literature for detecting the open circuit faults of
both the two-level and three-level inverters [14–16]. General review of the existing detection and
CMC, 2021, vol.67, no.2 2285

identification methods for faults in the cascaded H-bridge (CHB) inverters has been presented
in [17]. In [18], fast open circuited switch fault has been proposed for fault tolerant two-level
inverters in active power filter applications. The detection of the fault is achieved through com-
paring the voltage difference in magnitude and time between the measured and calculated output
voltages. However, additional sensors and accurate model of the system are required in this type
of fault detection. Another similar fault detection method for two-level and three-level NPC
topology has been proposed in [18,19], however it fails at identifying the location of faults switch.
Concordia transform based fault detection method has been presented in [20] using the measured
output current of the inverter within the line period. Unfortunately, it require an additional
fundamental period for inductive current injection to identify the place of the faulty switch.
Additional methods based on ac-current slope and the zero voltage switching state have been
presented in [21]. Although, this method represents a slow fault detection solution due to requiring
three fundamental line periods to detect and identify the faulty switch.
In [22], historical average voltage values based fault detection method has been presented for
the CHB topology. The method is capable of locating the faulty switch pair without determining
the faulty one. A modified method has been presented in [23] with adding another voltage sensors
to locate exactly the faulty switch. A method based on the state estimators and the current residual
has been proposed in [24]. Although, this method neglects the dynamic behavior of multilevel
inverters. From another side, the neural network has been proposed for fault detection in CHB
multilevel inverter topology [25]. A Real-time switch fault detection method based on 1-D con-
volutional neural networks (CNN) has been presented in [26]. These methods can properly detect
the faulty switch and its location, however, complex computation and learning data are needed in
this type. Additional methods have been introduced using the simplified Fourier transform [27],
observer based [28], CNN [29], and deep CNN [30],
Principal component analysis (PCA) based fault detection method has been proposed in [31].
However, this method requires additional measurements, which increase the cost of the system.
Application of artificial neural network and fuzzy logic methods for detection of faulty switches
have been proposed in the literature [32–34]. However, the existing methods fail at exactly local-
izing the faulty switch. Additionally, the model predictive control (MPC) methods have been
employed for detecting faults in power converters [35,36]. These methods employ the already
estimated states against the actual measured ones to detect the faulty condition. Fuzzy logic based
method for detecting the faults in two-level based motor drive applications using the measured
currents has been proposed in [34].
It can be seen that the existing fault detection methods focus on the two-level inverters and
motor drive applications. Whereas, the grid-tied applications are subjected to continuously varying
operating conditions and parameters variations. Thence, the behavior of the grid-tied PV systems
is difficult to be predicted by mathematical modelling in addition to continuous variations in the
grid side impedance. Thence, nonlinearity would appear in case of open circuit faults occurrence.
Therefore, this paper proposes an efficient fault detection and identification method for open
circuit faults for grid-tied T-type PV inverter applications. The proposed method is based on
employing the fuzzy logic for detecting and relocating the various faults. The proposed method
represents a simple method with considering nonlinearities and parameter variations of grid-tied
PV systems. Moreover, the proposed method eliminates the need for the additional measurement
circuits and/or complex mathematical modelling.
The remaining of the paper is organized as following: the operating principle and power
circuit of grid-tied T-type PV inverter is presented in Section 2. The post-fault analysis of the
2286 CMC, 2021, vol.67, no.2

PV inverter is explored in Section 3. The proposed fault detection method is detailed in Section 4
with the design considerations of the proposed algorithm. The results of the selected case study
and performance criteria of the proposed method are given in Section 5. Section 6 presents the
conclusion of the paper.

2 Operation and Structure of Grid-Tied T-type PV Inverter

The power circuit of the three-phase grid-tied three-level T-type PV inverter topology is
described in Fig. 1. The topology has twelve power switches with four switches in each phase.
The input dc-link voltage is equally divided among the two dc-link capacitors at steady state
(vdc1 = vdc2 = vpv /2). The dc-link capacitors are responsible for generating the different voltage
levels in the inverter. The incorporation of different twelve power switches results in making the
fault detection and identification process more complex for T-type inverters. The operation of the
topology can be explained as follows: each phase leg in the topology can generate one of three
states (P, O, or N) according to the applied gating pulses. The associated output voltages for the
three states are vdc1 , 0, and −vdc2 , respectively. The controller has to maintain balanced voltages
over the dc-link capacitors in order to avoid the voltage overstresses on the different components
in the inverter system. The switches pulses for each phase leg is shown in Tab. 1.

Vdc1 Sa1
Rf Lf
C
O B
A `
Sa2 Sa3
Grid
Vdc2
Filter
Sa4
N

PV modules String Inverter

Figure 1: Grid-connected three-level T-type inverter

Table 1: The switching states and switch status of the T-type topology
Switching state Switching status (x = a, b, or c) Output voltage
Sx1 Sx2 Sx3 Sx4 vxO
P 1 1 0 0 Vdc1
O 0 1 1 0 0
N 0 0 1 1 −vdc2

Fig. 2 shows the control system for the three-phase grid-tied T-type topology for the PV
systems. The phase locked loop (PLL) is firstly employed to provide the grid synchronization
CMC, 2021, vol.67, no.2 2287

function for the inverter. The maximum power point tracking (MPPT) block maximizes the
extracted power from the PV systems at the various operating solar irradiance and ambient
temperature. The MPPT controller generates the reference dc-link voltage for the inverter, which is
responsible for generating the d-axis reference current. The d-q reference frame is utilized in this
paper for performing the inverter control. The q-axis reference current is determined according
to the required reactive power injection by the inverter. Two differ proportional-integral (PI)
decoupled controllers are used for performing the d-q-axis current control.

Figure 2: The control structure of the grid-tied PV inverter

3 Post-Fault Analysis of T-Type Inverter

In case of open circuit fault condition, some switching states will be impossible and free-
wheeling diodes results in different states at the output depending on the current direction and the
faulty switch location. Tab. 2 shows the healthy and faulty output states of the inverter at different
fault locations. It can be seen that when Sx1 is open circuited, the output voltage state will remain
the same at negative output grid current due to the freewheeling diode path. Whereas, at positive
output current and faulty switch, the current direction will go through other freewheeling path in
the neutral point switches. The same analysis can be made for the other faulty conditions. The
change of the switching states at faulty condition results in the distortion of the output pole
voltages, output grid currents, and dc-link capacitor voltages. The distortion is dependent on the
2288 CMC, 2021, vol.67, no.2

state of the pole voltage and the direction of the grid current. In the following subsections, the
analysis of the effects of different switches faults on the behavior of grid currents and the dc-link
capacitor voltages are analyzed in more details for designing the proposed fault detection method.

Table 2: The effect of different faults and current direction on the states of output pole voltages
Faulty switch Output pole voltage (x = a, b, or c)
Healthy Faulty (ix < 0) Faulty (ix > 0)
Sx1 P P O
Sx2 O O N
Sx3 O O N
Sx4 N N O

3.1 Analysis of the Output Grid Currents at Faulty Conditions

The inverter is controlled to generate a sinusoidal output currents in the normal case. At the
faulty case, the path is determined by the location of faulty switch, the output current polarity,
and the gating pulse. The inverter output states at faulty switch Sx1 is shown in Fig. 3a at positive
polarity of output current, and in Fig. 3b for negative output current polarity. At negative output
current, the path is determined by the freewheeling diode Dx1 of the switch at both the healthy
and faulty cases and the output voltage will remain at P state without changes. Whereas, at
positive current polarity, the output current will pass through the switch Sx2 and diode Dx3 and
the pole voltage will change to state O. Similar analysis can be made for the other faulty switch
cases. In case of switch Sx2 open circuit fault, the negative grid current will pass through diode
Dx2 . If the grid current is positive, the new path will be through diode Dx4 and the pole voltage
will take the state N. In case of switch Sx3 open circuit fault, the negative grid current will pass
through diode Dx3 . If the grid current is positive, the new path will be through diode Dx1 and
the pole voltage will take the state P. In case of switch Sx4 open circuit fault, the negative grid
current will pass through diode Dx4 . If the grid current is positive, the new path will be through
switch Sx3 and diode Dx2 and the pole voltage will take the state O. The performance of the
output current of the inverter at the various faulty switches is shown in Fig. 4. Tab. 2 summarizes
the output state changes at different healthy and faulty conditions.

3.2 Analysis of the dc-Link Capacitor Voltages at Faulty Conditions

The two capacitor voltages are highly influenced by the faulty switch and the applied switching
states of phases a, b, and c. Fig. 5 shows the effect of different switching states on the dc-link
capacitor voltages. The space vectors of the inverter output are categorized according to their
magnitude into zero voltage, small voltage, medium voltage, and large voltage vectors. The neutral
point (NP) is left unconnected in the large and zero voltage vectors, so they do not affect the
NP voltage. On the other hand, medium-voltage vectors has a connection to NP, so they affect
NP voltage. The NP voltage will increase or decrease depending on the current direction of its
connected phase. In small voltage vectors, one or two of the three phases are connected to the
NP, so they affect the NP voltage. The P-type small-voltage vectors helps to discharge the upper
capacitor, and hence the NP voltage is increased. Conversely, the N-type small-voltage vectors
CMC, 2021, vol.67, no.2 2289

discharges the lower capacitor that results in decreasing the NP voltage. More details about the
NP voltage analysis with space vectors can be found in [15,37].

vdc1 C1 vdc1 C1
Sa1 Sa1
vpv Da1 vpv Da1
Da2 Da3 Faulty Da2 Da3 Normal
ix ix
PV O PV O
ix ix
Normal Faulty
Sa2 Sa3 Sa2 Sa3
vdc2 vdc2

C2 C2
Sa4 Da4 Sa4 Da4
N N

(a) (b)

Figure 3: Current paths at normal and faulty conditions in the power switch Sa1 . (a) Positive grid
current (b) Negative grid current

(a) (b)

(c) (d)

Figure 4: The behavior of grid current in different faulty modes. (a) Sa1 fault (b) Sa2 fault (c) Sa3
fault (d) Sa4 fault
2290 CMC, 2021, vol.67, no.2

The two dc-link capacitors are expected to have equal voltages at healthy condition. When
open circuit fault occurs, a change will occur in the output pole voltage state and hence the output
space vectors, as described before in Tab. 3. In case switch Sx1 fault, the P state will be replaced
with O. Consequently, some regions of the discharging of the upper capacitor will be replaced
with lower capacitor discharging, and hence the upper capacitor will be higher than the lower
capacitor voltage. A similar analysis can be made for other types of faults. Fig. 6 shows the two
dc-link capacitor voltages at different types of faults.

a P a
P
C1 C1

b b

Grid

Grid
vpv vpv
O O

C2 c C2 c
N N
(a) (b)
P a P a

C1 C1

b b
Grid

Grid
vpv vpv
O O

C2 c C2 c
N N
(c) (d)

Figure 5: The effect of different switching space vectors on dc-link capacitors voltages. (a) PPN
large voltage vector (b) PON medium voltage vector (c) POO P-type small voltage vector (d) ONN
N-type small voltage vector

4 The Proposed Fault Detection Method

The operation of grid-tied PV inverter systems is subjected to several varying parameters. The
output power/voltage/current are highly fluctuating with the operating solar irradiance and ambi-
ent temperature. In addition, the electrical networks are weak grids, and the grid side impedance
is changing with the different operating points. Therefore, the behavior of the grid-tied PV systems
is difficult to be expected by mathematical modelling due to the continuously varying grid side.
This in turn imposes several challenges to the fault detection as the consequences of open circuit
faults are nonlinear. Fig. 7 shows the nonlinear behavior of the grid connected systems at faulty
condition with uncertainty in the grid-side impedance. The aforementioned analysis of the inverter
system performance under different fault types is employed for designing the proposed fuzzy logic
detection technique. The grid current and dc-link capacitor voltages are the selected parameters
to extract the fuzzy rules. This is advantageous due to using the already available measurements
for the control purposes without requiring additional measurements.
CMC, 2021, vol.67, no.2 2291

Table 3: The rules of the fuzzy logic in the proposed method

Input membership functions Output membership function (FO) Type of fault
Id i ΔVdc
Z or P X X 0 NF
N 1 P 1 Sa1
N 1 N 2 Sa2
N 2 P 3 Sa3
N 2 N 4 Sa4
N 3 P 5 Sb1
N 3 N 6 Sb2
N 4 P 7 Sa3
N 4 N 8 Sb4
N 5 P 9 Sc1
N 5 N 10 Sc2
N 6 P 11 Sc3
N 6 N 12 Sc4

(a) (b)

(c) (d)

Figure 6: The behavior of dc-link capacitor voltages at different faults. (a) Sa1 fault (b) Sa2 fault
(c) Sa3 fault (d) Sa4 fault
2292 CMC, 2021, vol.67, no.2

Figure 7: Nonlinear behavior in the trajectory of the grid currents in α-β plane at Sx1 fault

Firstly, trajectory of the measured grid currents is made in the α-β plane. The resultant
phasor current of the three phase system (Ir ∠) represents the sum of the current trajectory
divided by number of data points. This trajectory is represented by a circle, which is centered
in the origin at healthy case. Hence, the magnitude of the resultant will be zero at healthy
condition. Whereas, the trajectory takes the shape of half-circle at any faulty switch condition
with particular angle, which is dependent on the location of the faulty switch. Consequently,
the resultant phasor current will have a magnitude that determines the occurrence of faulty
condition when its magnitude exceeds some predefined threshold value. From another side, the
angle of the resultant phasor current is dependent on the phase leg location of the faulty switch,
which can give partial information about the location of the faulty switch. Fig. 8 summarizes the
various trajectories of the three-phase output grid currents in the α-β plane for the various fault
conditions. The resultant will be compared with a threshold value Ith and the difference Id will
be employed for distinguishing between healthy and faulty switch conditions.
The various faults result in different performance of the dc-link capacitor voltage related
to the increase/decrease with respect to each others. Therefore, the difference between the two
capacitors voltage (Δvdc ) is used in addition to current trajectory amplitude and angle to design
the fuzzy rules to properly detect the location of the faulty switch. There will be three input
membership functions and one output membership function to define the fault occurrence and
fault switch location. The input and output membership functions of the proposed fuzzy logic
fault detection method are shown in Fig. 9. Tab. 3 summarize the fuzzy bases of input and output
functions to locate the fault. Where, Z denotes to zero, P to positive, N to negative, X to do
not matter, and NF to no fault conditions in Tab. 3. The various rules are defined according
to the response of the current trajectory and the difference between the capacitor voltages. The
Mamdani fuzzy logic set is employed for implementing the proposed method. The defuzzification
is performed by using Max-Min composition and the centroid of area method.
Additionally, the failure of power switches and/or diodes in the neutral point clamped topol-
ogy results in the distortion of the inverter output current and unbalance of dc-link capacitor
voltages. Therefore, the proposed method can be also applied to the neutral point clamped
topology with the same aforementioned analysis and design method. The output signal of the
proposed fault detection method is fed into fault tolerant control systems to take the proper
CMC, 2021, vol.67, no.2 2293

post-fault action. Hence, continuous operation of PV inverter systems can be maintained with the
help of the proposed fault detection method.

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

Figure 8: Trajectory of grid currents in α-β plane in different faulty conditions. (a) Sa1 fault
(b) Sa2 fault (c) Sa3 fault (d) Sa4 fault (e) Sb1 fault (f) Sb2 fault (g) Sb3 fault (h) Sb4 fault (i) Sc1
fault (j) Sc2 fault (k) Sc3 fault (l) Sc4 fault

5 Results and Discussions

To test the feasibility and effectiveness of the proposed fault detection method, a test case
study has been designed and simulated using MATLAB/SIMULINK platform. Tab. 4 summarizes
the PV inverter parameters for the studied system. The open-circuit faulty condition is emulated
by disconnecting the gating pulses from the power switches. Fig. 10 shows the performance of
the output pole voltage at faulty switch of Sa1 . It can be seen that the output pole voltage of
phase a is distorted in the positive half-cycle of the output voltage. The output state P cannot
be generated during this fault and the output state O is generated instead. Whereas, the other
two pole voltages of phase b, and c are generated properly. It can be seen also due to the lag
power factor of the inverter, some part of the positive pole voltage is not affected due to the
freewheeling diode of the switch Sa1 .
The performance of the proposed fault detection method at faulty switch Sa1 is shown in
Fig. 11. The output currents of the PV inverter are distorted at the faulty condition. This is a
direct result from the distortion of the output pole voltage of phase leg a. The positive half-cycle
2294 CMC, 2021, vol.67, no.2

of the current is eliminated due to the fault. In the proposed method, the output currents and
the dc-link capacitor voltages are employed to generate the various membership inputs to the
proposed fuzzy logic based fault detection method. The fuzzy logic output of the proposed
method is shown in the figure. It refers to output 1 faulty condition, which denotes to the faulty
switch Sa1 as described in the design of the proposed method in Tab. 3. Moreover, the output of
the fuzzy logic is maintained at 0 output at no fault condition, which shows the ability of the
proposed method to distinguish between the healthy and faulty conditions.

(a) (b)

(c) (d)

Figure 9: The input and output membership functions of the proposed fuzzy logic. (a) μId (b) μ
(c) μΔvdc (d) μFO

Table 4: The system parameters for the simulated case study

Parameter Value Parameter Value
Grid voltage (rms line-line) 100 V Grid line frequency 60 Hz
dc-link voltage 300 V dc-link capacitance 1000 μF
Grid side filter inductor 3 mH Grid side filter resistance 150 m

The performance of the proposed method at faulty condition of Sa2 is shown in Fig. 12.
It can be seen that the output current is distorted in the positive half-cycle of phase leg a.
To determine the difference among the faulty case of Sa1 and Sa2 , the sensed dc-link capacitor
voltages are employed as inputs for the proposed method. As shown in the analysis in Fig. 6,
the two faults has different effects on the dc-link capacitor voltages. The output of the proposed
detection method is 2 in this case, which belongs to the faulty Sa2 condition. Therefore, the
proposed method can determine the faulty condition with determining the faulty switch location.
Moreover, the proposed method can detect the various faults within only a single line period.
Fig. 13 shows the performance of the proposed detection method at faulty switch of Sa3 . The
inverter operation is affected by this faulty case, and the negative half-cycle of the output current
is eliminated. The current trajectory is estimated in this type of fault, and the amplitude and
CMC, 2021, vol.67, no.2 2295

phase angle are fed into the proposed method. With employing the difference between capacitor
voltages as input to the proposed method, the results shows the effectiveness of the proposed
method to locate the faulty case and the faulty switch location. The output of the proposed fuzzy
logic method denotes to the output of 3, which belongs to the switch Sa3 faulty case. It is clear
the ability of the proposed fault diagnosis algorithm to detect and locate the fault within one
fundamental period at various types and location of faults.

Figure 10: The simulation results of the output pole voltage at fault location in switch Sa1

Figure 11: The simulation results of the proposed method performance at fault location in
switch Sa1
2296 CMC, 2021, vol.67, no.2

Figure 12: The simulation results of the proposed method performance at fault location in
switch Sa2

Figure 13: The simulation results of the proposed method performance at fault location in
switch Sa3

6 Conclusions
An efficient fuzzy logic-based open circuit fault detection and identification method has been
proposed in this paper for grid-tied PV inverters. The proposed method can deal successfully with
the nonlinearities of the grid-tied PV systems and the utility grid impedance uncertainties, espe-
cially during the faults conditions. Additionally, the advantages of the proposed method are the
simple implementation, no need for complex mathematical modelling, no need for large training
CMC, 2021, vol.67, no.2 2297

data, and without requiring additional measurements and/or circuits. The analysis, design, and
implementation of the proposed detection method are presented for the widely-used three-phase
T-type three-level PV inverter topology. The performance of the measured output current and
dc-link capacitor voltages are analyzed at healthy and faulty conditions for designing the fuzzy
bases and for extracting the rules of the proposed fuzzy logic detection method. Furthermore, the
proposed detection algorithm employs the already available measurements for the control purposed
of the PV inverter. The results of the tested case study verify the superiority of the proposed
detection method for detecting the various faulty conditions, and identifying the location of the
faulty switch accurately.

Funding Statement: This project was supported by the Deanship of Scientific Research at Prince
Sattam Bin Abdulaziz University under the research project No. 2020/01/11742.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding
the present study.

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