Received: 22 May 2023
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Revised: 23 August 2023
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Accepted: 28 September 2023
DOI: 10.1002/cae.22691
RESEARCH ARTICLE
Introducing AI applications in engineering education
(PBL): An implementation of power generation at
minimum wind velocity and turbine faults classification
using AI
Talha Ahmed Khan1,2
| Muhammad Alam1,3 | Safdar Ali Rizvi2 |
Zeeshan Shahid4 | M. S. Mazliham1
1
Multimedia University‐MMU,
CyberJaya, Selangor, Malaysia
2
School of Engineering and Applied
Sciences, Bahria University Karachi
Campus, Karachi, Pakistan
3
Faculty of Computing, Riphah
International University, Islamabad,
Pakistan
4
Faculty Of Electrical Engineering,
Nazeer Hussain University, Karimabad,
Karachi, Pakistan
Correspondence
Talha Ahmed Khan, Multimedia
University‐MMU, 63100 CyberJaya,
Selangor, Malaysia.
Email: talha_khann@hotmail.com and
ahmedkhan.talha01@mmu.edu.my
Funding information
Multi‐media University
Abstract
This article explores the integration of artificial intelligence (AI) applications
into project‐based learning (PBL) education as a means to enhance students'
education. Specifically, the implementation of AI in the context of power
generation is addressed, focusing on achieving power generation at minimum
wind velocity and classifying turbine faults using AI techniques. The
researchers have proposed a novel generating unit which is going to generate
1 KW of electric power at a specific flow rate of air and the generated power
will be stored in the battery bank through the charge controller and then the
load is driven from the battery through an inverter. Iron or core losses
(Hysteresis, Eddy Current losses) can be acknowledged as one of the major
reasons for the inefficiency of conventional generators, therefore anovel
coreless model generator was proposed which also improved efficiency and
reduces drag. Wind Turbine prototype was fabricated and deployed for the
testing and validation of the proposed novel design. The design produced
outstanding power ratings and electrical generation characteristics compared
with other existing strategies at minimal air flow. Results proved that the
proposed coreless axial flux generator has the capability to produce a better
power rating compared with the existing wind turbine generators. Proposed
Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a
wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the
proposed generator while the conventional generator produced around
300 kwh. The proposed generator design performed 35% better in terms of
production efficiency under load and no load conditions. Moreover, faults in
turbines are very common due to the various temperatures, therefore the faults
have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of
space vector modulation (SVM) and Naive Bayes classifiers was performed in
the study to classify wind turbine faults. It was found that both classifiers
performed well in achieving high accuracy. SVM achieved a slightly higher
Comput Appl Eng Educ. 2024;32:e22691.
https://doi.org/10.1002/cae.22691
wileyonlinelibrary.com/journal/cae
© 2023 Wiley Periodicals LLC.
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accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of
0.967. Based on the results, it can be inferred that SVM may be a more suitable
classifier for wind turbine fault classification. The case study results
demonstrated the potential of AI applications in PBL education, offering
students a multidisciplinary learning experience that enhances their technical
knowledge, problem‐solving skills, and teamwork abilities.
KEYWORDS
axial flux generator, coreless, machine learning, project‐based learning, power rating,
wind turbines
1 |
INTRODUCTION
Lack of development in power generation as well as in
the power dispatch have been observed for many decades
in Pakistan; having a geographical advantage of being
beneficial in terms of natural or renewable resources.
From these factors, electricity can be generated in bulk
and makes the environment pollutant‐free too. In spite
of this, Pakistan has been facing a crisis in the power
development sector for the last 15 years. This leads to the
critical issue of electricity breaking down and not using
natural resources efficiently. “Wind power” whenever
this term arises there is a basic thing which comes to
peoples' mind is the generation of power through the air
flow. Now living in today's century, the method and
implementation of generating power through the air are
through many methods, the most common and efficient
means is by building a horizontal wind turbine. When a
wind turbine is manufactured, there is a perception of a
huge infrastructure that requires a lot of manpower in
construction that takes years for completion. Whereas on
the other hand, the benefit of all such time can be
acknowledged as fruitful too. Wind energy can produce a
bulk amount of power on a daily basis [3]. The most
recent wind power generation capacity was 1237 MW in
Pakistan which is more than 6% of the total production of
Pakistan but Pakistan has tremendous wind energy
potential. The total extractable wind power capacity is
120 GW. Urban cities like Karachi, Hyderabad, and
Quetta have an average wind speed of 3–4.5 m/s at
10‐m height, which is best suitable for small‐scale wind
turbines. In Sindh, the Gharo‐Jhimpir wind power plant
is expanded on 9700 km2 and that plant is producing
50–55 MW power. Except this, there are more than five
wind power plants which are under construction in
Pakistan named as Metro Power Company, Master wind
energy, United energy Pakistan, Nordex Pakistan, Yunus
Energy, Tenaga Generasi which would produce more
than 400 MW [41]. The design idea can be elaborated as
when there is minimum airflow, the blades of the turbine
will start to produce more power compared with
conventional design. The generator (Coreless Axial Flux
Generator) converts mechanical energy into electrical
energy. The generation is dependent on the flow of air
and the minimum amount of wind is required to run the
system. Furthermore, a transformer is being used to step
up the voltages which are sent to the grid [10]. In the
plain areas such as in Punjab and Sindh, the flow of air is
maximum in flat areas or in plain areas which is a
positive point in the production of more amount of
electrical power throughout mechanical input in a way
so it can be rotated smoothly without any interruption.
This thing can only happen when the minimum wind is
coming at a height. Around 10–15 feet is required
normally to make the turbine rotate freely. Once this
thing is done while the power generated is transported to
remote distances where electricity is required [15].
Fundamental power generation transformation is presented in Figure 1.
Figure 1 shows the fundamental power generation
diagram. The best thing about generating power through
renewable energy which is normally water, air, and
sunlight that it is the cleanest way of generation. For
example, there is no type of harmful gases and pollutants
involved through such generation. But in this proposed
research, air is used as renewable energy to produce
electrical power through mechanical input, and the
reason why air was preferred here instead of water and
sunlight are not available everywhere but the air is
available everywhere. Also, the air is the main source
which is one of the variables that cannot be completely
removed. Hence, it can be controlled which is done
throughout the wind power generation [14]. As conventional turbines are comparatively costly and take a lot
of time to be built, the researchers have proposed a
turbine that can generate electricity through‐flow of air
through coreless axial flux permanent magnet generator
(AFPM) that has been proposed for the first time in
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FIGURE 1
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Fundamental power generation diagram.
Pakistan by the research team. The minimum flow of the
wind is the prime consideration of the research which
would be having a minimal flow that is enough to rotate
a turbine. A turbine constructed by the researchers can
be rotated with minimum airflow at a low height. The
construction of the turbine is simply completed depending on the flow of air, so as it strikes the blades of the
turbine it may rotate efficiently to produce electricity.
The blades of the turbine are connected to the rotor of a
generator which can generate power up to 700 W. Once
the generation is completed, the voltages are then sent to
the bridge rectifier which converts alternating current
(AC) voltages into direct current (DC). DC–DC controller
known as pulse width modulation (PWM) is used which
transmits maximum voltage into the battery of 12 V.
Once the voltages are passed through the controller, it is
being stored in the battery which again depends on
personal demand. The battery bank has a voltage rating
of 12 V, 38AH. The final section of this research is the
addition of an inverter that can convert pure DC to pure
AC. Furthermore, the AC supply is now ready to use for
appliances [26]. The flow of the wind turbines is
electromechanical energy converted into electrical energy that extracts kinetic energy from the flowing of air to
produce electricity. Wind turbines provide a wholly
renewable energy solution which is cost‐saving in other
cases too. Such technology is scalable and standardized
according to many researchers [25]. The flow of the wind
turbines produces a maximum amount of power through
the kinetic energy of airflow. Large infrastructure
“turbines” are not necessary for an operation that
directly saves investment and time too. Due to the fact
that only flow is required from the wind which varies
from time to time and area to area from a mathematical
aspect. In wind turbines, high altitude is required for
maximum power output and long wings. It is found that
the energy flux of water is proportional to the cross‐
sectional area, density, and cube velocity. According to
the wind energy formula [44].
P = 1/2ῤAV 2,
The above equation states that (P) is power in Watts,
whereas (ρ) is the air density measured in kg/m. (A) is
the area which is equal to the π×r, where r is the radius
of the blade. Lastly, (V) is referred to as the velocity of air
in cubes and is measured in m/s.
List of nomenclature.
Abbreviations/Symbols
Full forms
RPM
Revolution per minute
PWM
Pulse width modulation
VAWT
Vertical axis wind turbine
HAWT
Horizontal axis wind turbine
KWH
Kilowatt per hour
ρ
Air density
MPPT
Maximum power point tracker
φ
Weber flux
Kp
Pitch factor
1.1 | Types of turbines
There are two types of wind turbines which are horizontal
wind turbines and vertical wind turbines. The most
common to produce electrical power between them are
horizontal wind turbines. The horizontal wind turbines
consist of a rotational axis on a rotor which is subjected
parallel to the blades of wind turbine. Horizontal wind
turbines are shown in the given Figure 2.
Figure 2 illustrates the basic horizontal wind turbines
as shown that are usually deployed [29].
1.1.1 | Vertical axis wind turbine (VAWT)
VAWT is illustrated in the following Figure 3. According to
the literature review, HAWT works better than the VAWT.
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FIGURE 2
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Horizontal axis wind turbine [29].
FIGURE 4
ET AL.
Inner parts of horizontal wind turbine.
the generator to produce output power. For the change in
the direction of air, the yaw drive rotates the whole
turbine along the direction of air. The low‐speed shaft is
connected to the wings of turbine and the high‐speed
shaft is connected to the generator. If the speed of wind is
so high, the mechanical or electrical brakes will be
applied [8].
1.1.3 | Savonius wind turbine
FIGURE 3
Vertical axis wind turbine [29].
Figure 3 demonstrates that in the vertical wind
turbine, a rotor has a rotational axis parallel to the flow of
the air. Figure 3 illustrating the basic vertical wind
turbine is shown. It is found by in the exhaustive
literature review that horizontal turbine is comparatively
better than the vertical wind turbine because of the
difference in production [32].
Like a horizontal wind turbine, the Savonius wind
turbine is one of its kind used for applications having a
minimum wind speed shown in Figure 5.
Such turbines can be very efficient, around 87%–90%
efficiency is attained through this turbine. The design of
this turbine is so efficient which is aerodynamic as
compared with horizontal wind turbine. When the
direction of air is variable or change, so the effect of
this changing of air is not affected by the power
generation [7]. The functionality of this turbine is such
that when the minimum air pressure is applied to the
turbine, as a result of this rotation the turbine runner
spins and imparts energy to the shaft of the turbine.
1.1.4 | Darreius wind turbine
1.1.2 | Horizontal axis wind
turbine (HAWT)
The main rotor of this turbine is connected to the shaft
which is directed along the flow of wind to convert
mechanical energy into electrical energy as shown in
Figure 4. When the wings of a turbine receive mechanical pressure through the flow of air, so the shaft that is
connected to the gear box starts rotating, which makes
This turbine is operated by minimal force of air which is
low torque and produces maximum power. The Darreius
wind turbine is shown in Figure 6.
Figure 6 elaborated on the Darrieus wind turbine
which is also known as eggbeater turbine because of its
super structure design. This turbine is a VAWT but not
the same as the savonius wind turbine. But the main
drawback is that researchers have to require external
force to move it in initial condition and it has high
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renewable energy attains less running cost and gives the
desired output. However, the structure and style of
the turbine are being changed to irrespective ways, but
the prime motive of generating power remained constant
[14, 29]. Wind‐power plants are installed in areas where
there is a demand for electricity having different loads.
So, their fabrication and style are suggested to the place
where it needs to be placed [11, 26].
1.2 | Wind energy management
FIGURE 5
FIGURE 6
Savonius wind turbine [7].
Darreius wind Turbine [31].
efficiency but this is not consistent. There are two blades
that are used in this machine that are connected parallel
to the shaft and rotate perpendicularly to the shaft [31].
Normally, the HAWT is better than others because of its
efficiency parameter. The HAWT is capable to convert
40%−50% kinetic energy into electrical energy whereas
Darrieus wind turbine produced 30%−40% of input
power. Savonius wind turbine is capable of converting
kinetic energy up to 10%−17% into electrical energy. So,
the HAWT is better than other wind turbines and vertical
wind turbine [1].
1.1.5 | Proposed axial flux wind turbine
There has been wide research and theories about axial
flux wind turbines for many years [12]. This is because
Power plants generate most of the world's electricity by
burning fossil fuels, which are inadequate in availability
and have harmful impacts on the environment [23].
Current research reveals that if the utilization rate stays
stable, the fossil fuels reserve of the world will last only
50−60 years. Moreover, the Paris Climate Agreement and
United Nations Sustainable Development have set targets
to decrease the pollution of CO2 to the environment [17].
Balancing energy production and utilization is known as
energy management which can have major influences on
the journey of electric energy from production to
utilization. Energy management in power distribution
systems considers various traditional energy sources like
energy storage systems, renewable energy sources (RES),
critical loads, and energy management system operations
and functions. The scholars are curious about the energy
management system because of numerous reasons,
which include:
• Reduction of losses in the distribution systems by the
service companies to decrease operational costs,
ultimately facilitating the customers by paying fewer
electricity bills.
• Cost reduction by precisely monitoring and observing
the loads and energy resources.
• Decreasing greenhouse gas discharges that affect the
society by power and electric companies.
In this scenario, urgency for a more effective and
efficient way to produce and utilize energy is exhibited. It
also facilitates giving power to the consumers of critical
load in the power lines during scheduled load shedding
[40]. Various research papers have been published on the
energy management of smart grids for reducing operational costs, and system losses, such as the authors in
Divshali et al. [13] presented a model in his paper that
shows the reschedules and controls of the generators
based on diesel and units of battery storage to reduce the
cost of the system. They presented an energy management system based on multiagent transactive in his paper
that regulates the supply in the existence of high levels of
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RES and electric vehicles [37]. The author [43], in their
paper, presents a model of the optimal dispatch of
microgrid distributed generation for system sustainability. Researchers discussed control of the distributed
generators to reduce the costs of the system by increasing
the use of RES. The authors in Atwa et al. [6] presented a
model to accommodate the output of the generation
according to the network's reasonable scenario. A model
in Arefifar et al. [5] presents that it considers the distinct
size and increases the allowable distributed generation of
the systems. In addition, numerous papers focused on
controlling distributed generations for different aspects
of smart grid energy management systems [16, 35].
2 |
PROBLEM STATEMENT
Based on the exhaustive literature review, it was revealed
that existing approaches have deficiencies on the power
generation side and on the dispatch side. It was also studied
that traditional turbines are comparatively costly and take a
lot of time to be designed. The researchers have proposed a
turbine that can generate electricity through‐flow of air
through coreless AFPM generator. Moreover, the integration of Artificial Intelligence (AI) was also needed for the
concrete turbine faults classification so that students may
also learn the engineering applications of AI as a project‐
based learning (PBL) approach.
3 | RESEARCH C ON TR IBU TIO N
FOR ENGINEERING
EDUC ATION ( PBL)
The research presented in this article makes a significant contribution to engineering education, specifically
in the context of PBL, by incorporating AI for turbine
fault diagnosis in a turbine generator project. By
integrating AI techniques into the project, students
are exposed to cutting‐edge technologies and gain
hands‐on experience in utilizing AI for real‐world
problem‐solving. This not only enhances their technical
skills but also prepares them for the evolving demands
of the engineering industry. The introduction of AI for
turbine fault diagnosis in the turbine generator project
adds a new dimension to PBL education. Students are
challenged to analyze sensor data, develop AI models,
and classify various turbine faults accurately. This
fosters critical thinking, data analysis, and decision‐
making skills, which are essential for future engineers.
This turbine is special due to its coreless axial flux‐
based novel design and it produces a maximum amount
of power at minimum input. The axial flux turbine can
ET AL.
be deployed easily over residential buildings; small
buildings whose height is more than 50 m and it can
also be deployed in plane areas at 15 feet in height. The
researchers have proposed a novel generating unit
which is going to generate 1 KW of electric power at a
specific flow rate of air and the generated power is
stored in the battery bank through the charge controller
and then the load is driven from the battery through the
inverter. Iron or core losses (Hysteresis, Eddy Current
losses) can be acknowledged as one of the major
reasons for the inefficiency of conventional generators,
therefore a novel coreless model generator was proposed which improved efficiency and reduces drag also.
The turbine faults have also been analyzed and
classified using the AI‐based classifiers so that remedies
can be applied while building an axial flux generator
design. Furthermore, the research contributes to the
field of engineering education by bridging the gap
between theoretical knowledge and practical application. By working on a tangible project and utilizing AI
for turbine fault diagnosis, students develop a deeper
understanding of the complexities involved in turbine
generator systems and gain insights into the importance
of efficient maintenance strategies. The incorporation of
AI in the turbine fault diagnosis process also enhances
the overall efficiency and reliability of turbine generator
projects. Students learn how AI can streamline fault
detection, improve maintenance practices, and optimize
power generation. These insights contribute to the
advancement of engineering practices in the renewable
energy sector. Overall, the research presented in this
article offers a valuable contribution to engineering
education through the integration of AI for turbine fault
diagnosis in a turbine generator project. By leveraging
AI technologies, students gain practical skills, address
real‐world challenges, and contribute to the development of sustainable and efficient energy systems.
4 | M E T HO D O L O GY
Figure 7 presented a run‐off wind turbine basic
fundamental diagram that was developed by the
researchers which was specifically designed in a
manner that can be generated through the flow of
air. A horizontal vortex turbine subjected at height has
a minimum flow of air to give maximum output
results. The size and shape of the blades of the turbine
are constructed with proper calculation and research
so that it can easily rotate when subjected to the
direction of air. The selection of material (Aluminum)
is done because Aluminum makes the weight of the
turbine comparatively lower than other metallic
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• Reducing the electricity crisis in Pakistan by creating
an alternate source for lighting up homes, shops,
recreational points, and much more.
• Conversion of constant DC voltage to 220 AC voltage
through an inverter.
4.1 | Mathematical analysis for axial
flux generator
Axial flux generator is the core part of the proposed
turbine, it is being selected for this final year research
project after doing proper research and calculations. The
generator is used for the generation of power and is in
between the three turbine blades. The generator is
designed in a way that there are two rotors which are
the rotating part, whereas the single stator is held
stationary in between the rotating plates.
• Calculation of poles per phase
Speed = 600 rpm,
Frequency = 50Hz,
Seconds to minutes conversion = 60,
(1)
Poles per phase = 60 × f / n,
(2)
Poles per phase = (60 × 50)/600,
(3)
Poles per phase = 5 pair of poles per phase.
FIGURE 7
Basic block diagram.
• Winding of generator
materials. Further specifications of the turbine are
shown below:
•
•
•
•
•
•
•
•
•
•
The height of the turbine is 12 ft.
The material used is Aluminum and iron.
The diameter of the blade is 5 ft.
The pitch of the blade is 25 mm.
Weight of axial flux generator is 8 kg and the total
weight of the turbine is 40 kg.
The following objectives were set to achieve the best
optimal wind power generation results with a minimum amount of airflow.
To design a free‐moving turbine that can be easily
rotated on minimal flow of air.
Generation of around 750 W is desired that can be
utilized for electrical appliances.
To build a rigid body of aluminum and stainless steel
to handle wear and tear with oxidation.
Deployment of turbine on places where the flow of air
is present in a decent amount, but has not yet been
utilized for generation.
L = 750 mm,
W = 20 mm,
Ns = 120 × f/p,
(4)
300 = 120 × 50/ p,
(5)
p = 20 poles,
m = no. of slots/poles/phase,
m = 15 × 3/10,
(6)
m = 5,
B = 180 ÷ No. of slots ÷ poles,
(7)
B = 180 ÷ 15 ÷ 10B = 120.
(8)
Kd = (sinmp/2)/(msin B /2),
(9)
Kd = (sin 5 × 20/2)/(5 sin 120/2),
(10)
Therefore,
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Kd = 0.9597.
(11)
Pitch factor, kp = 1 for full pitch winding.
• Calculation of the number of turns
W is the number of turns in coil per pole and phase,
U is the phase voltage, a is the number of parallel
branches, φ is the magnetic Flux of 1 pole pair (wb), f
is the nominal frequency and Kd is the Winding factor.
Formula,
0.22 Uaf
,
φ fKd
(12)
0.22 × 465.67 × 1
,
0.00427 × 50 × 0.9597
(13)
W′ =
W′ =
W′ is the 500 number of turns in coil.
So
S′u is the number of turns per slot, W′ is the number
of turns in coil per pole and phase, and Z is the slots.
Formula,
S′u =
6 × W′
,
Z
(14)
S′u =
6 × 500
,
15
(15)
S′u is the 200 turns per slot.
• Cross Section of the wire
q is the cross‐section of the wire, S is the turn in
slot = 200 turn, Q is the Surface slot, and F is the Filling
Factor = 0.34.
Formula
q=
Q×F
,
s
q = 0.13328 mm2,
Diameter = 2(q/ π ),
• The
main
axial
flux
generator
diameter
400 mm × 80 mm.
• Generation of 100 V having 700 W of power are
recorded at 400 rpm.
• The axial flux generator consists of 15 nodes and 20
poles.
• Three‐phase AC winding with H‐bridge diode 40 A.
The following design comprising of poles and nodes
was also tested with the proper measurements in
Figure 8.
Figure 8 demonstrated that the internal structure of
the nodes and poles was designed on Autodesk software.
The winding structures were placed on different
placements as can be seen in Figure 9.
Figure 9 shows the entire design process. Most
of the process was performed in the workshop to
make it an accurate and robust design for the better
results.
Practically it was very complex to design the most
accurate and robust design without any deficiency or
defects but the precision was achieved in the final design
as shown in Figure 10.
Figure 10 represents the final processed stator
winding design. The accuracy and precision of the
design were mandatory for better results as it was
to be deployed in the generator. The design was
initially checked on Autodesk software for the better
results.
5 | PBL IMPLEMENTATION
5.1 | Multilevel inverter (MLI)
Therefore,
0.784 × 0.34
,
200
turbine is subjected at some height so for that the turbine
starts running with flow of air, thereby preventing from
any damages so the stator of the generator is kept held
with the stand strongly. Further specifications of the
generator are shown below;
(16)
Surface slot Q = 0.784,
q=
ET AL.
(17)
(18)
Diameter = 0.6215 mm.
Therefore, according to the above diameter, 22
standard wire gauge is available in the market. The
In a wind turbine system, the turbine generates AC
power which is then converted to DC power by a
rectifier. The DC power is then fed into an multi‐lever
inverter (MLI), which converts the DC power into high‐
quality AC power suitable for use in the electrical grid.
The MLI uses multiple levels of voltage to create a
waveform that closely resembles a sine wave. This results
in a high‐quality output waveform with low harmonic
distortion. The use of an MLI also allows for improved
efficiency and reduced stress on the turbine and other
components. Overall, the use of an MLI in a wind turbine
system can help to improve the overall performance and
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FIGURE 8
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Poles and nodes internal structure design.
reliability of the system, even when only a single turbine
is used. Rectification is the process, which is a very
mandatory process in this proposed research as it
converts AC into DC. The output which comes from
the turbine is AC therefore to convert the current flow in
one direction, a rectifier is used. Also, DC can be easily
controlled as compared with AC. MLIs were used to
enhance and stabilize the voltage levels and harmonics
for the current. Power semiconductors and thyristors are
usually connected to improve and obtain the proper sine
wave using the switching frequency. An inverter is a
power electronic device that can convert DC power into
AC power. The term “inverter” was coined by David
Prince in 1925 when he published an article with the
same name. In the past, inverters were primarily used to
power lighting loads when the grid was unavailable.
However, with technological advancements, inverters
have expanded their range of applications. Initially, only
two‐level inverters were used, which produced an output
with two different voltage levels. However, these inverters had high switching losses and harmonic voltage,
which caused the flow of harmonic current in the circuit
and resulted in losses. To overcome these disadvantages,
an MLI topology was developed. This topology can
produce a pure sinusoidal waveform at the output
voltage, suppress harmonics in the output, reduce the
percentage of losses, and increase the number of voltage
levels beyond two. The MLI has three different types:
the diode‐clamped MLI, the flying capacitor MLI, and the
cascaded H‐bridge inverter. These types of inverters offer
several advantages over the traditional two‐level inverter
[4]. Research on cascaded H bridge (CHB) inverters has
been increasingly focused on in recent years due to their
modular circuit design, balanced DC voltage, and ability
to produce a high number of output voltage levels with
fewer components without requiring high device ratings.
However, one downside of this inverter is that it requires
a larger number of individual DC power sources
compared with other topologies. CHB inverters can be
classified into two basic types based on certain criteria.
CHB inverters can be categorized into two types based on
the DC sources used: symmetrical and asymmetrical
inverters. Symmetrical inverters use DC power sources of
equal magnitude, while asymmetrical inverters use
sources that are not equal. Asymmetrical inverters have
several configurations, with binary and trinary being the
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FIGURE 9
F I G U R E 10
Stator winding design.
most popular. In binary configuration, DC sources are
selected as 20, 21, 22, and so on, while in trinary
configuration, voltage sources are selected as 30, 31, 32,
and so on. The source configuration is typically selected
based on the desired strategy and the number of output
levels required in the inverter output. Symmetrical
systems are used to create fault‐tolerant systems, while
binary and trinary configurations are used to achieve
more levels in the output voltage with fewer switches.
This article discusses several CHB topologies based on
the number of switches [30]. Typically, inverters operate
by switching electronic switches that are controlled by
PWM techniques. The initial development of inverters
focused on the two‐level inverter design, but the need for
ET AL.
Coreless axial‐based design.
higher power, voltage stress on switching devices, and
power quality (PQ) issues exposed limitations of the two‐
level inverter, leading to further research. In DC–DC
power electronic converters, a technique that is commonly used to reduce voltage stress on devices is to
connect capacitors in series. By connecting two capacitors in series across the input DC source, the voltage drop
across each capacitor becomes half [38]. MLIs are widely
used for integrating RES into the grid and for high‐power
industrial applications. MLI has many advantages,
including high‐level output voltage with minimal distortion of harmonics, and meets power efficiency requirements. However, general MLI topologies have complex
circuits with many components, resulting in high total
cost and a large installation area. Past studies aim to
provide a synthesis of knowledge about various MLI
topologies and identify knowledge gaps. The study
defines a conceptual diagram with a switching sequence
table and mathematical equations for the topology,
covering most newly formed MLIs and providing useful
information. The current need is for a large number of
power semiconductor switches and control circuit isolation, less electronic power switches, and a DC source
[36]. When controlling a traction motor, it is necessary
to have high‐frequency transitions in the inverter.
However, these fast transitions, combined with parasitic
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ET AL.
inductances and capacitances, can cause significant
electromagnetic interference (EMI) issues both in conducted and radiated emissions. The size and frequency of
the inverter mean that noise is mostly emitted through
the cables and lines. At the inverter output, voltage
spikes may occur, which can damage the motor's
windings, insulation, bearing balls, or rollers, and high
dv/dt can increase eddy current and skin effect losses in
the cores and windings [18, 19, 21]. The challenges
associated with high‐frequency transitions inside an
inverter are exacerbated in modern inverters that utilize
new insulated gate bi‐polar junction transistors (IGBTs)
and metal oxide field effect transistors with faster
transitions. The resulting EMI issues, which include
both conducted and transmitted emissions, can have
detrimental effects on the performance and lifetime of
the inverter and the motor it drives. For instance, voltage
spikes that occur at the inverter output can cause damage
to the motor windings, insulation, bearing balls, or
rollers. Furthermore, the high dv/dt associated with these
transitions can increase eddy current and skin effect
losses in the cores and the windings, respectively. In
response to these issues, International Electrotechnical
Commission and the National Electrical Manufacturers
Association have recommended that the maximum dv/dt
in typical motor drive applications should be limited to
500 V/µs [19]. An MLI can be used in a wind turbine
system with just one turbine. The inverter helps to create
high‐quality AC power for the electrical grid. This type of
inverter uses multiple voltage levels to produce a
waveform that looks like a sine wave, which results in
a better‐quality output. The inverter can also improve
efficiency and reduce stress on the turbine and other
components. The wind turbine generates AC power,
which is then converted to DC power by a rectifier. The
DC power is fed into the MLI, which converts it back into
AC power suitable for use in the electrical grid. The
system can use various techniques to control the voltage
and frequency of the AC power. The MLI can also
improve the performance and reliability of the wind
turbine system, even with just one turbine. It can reduce
harmonic distortion, which is caused by the nonsinusoidal waveform of the AC power. This can help to extend
the lifespan of the system and improve its efficiency.
Overall, the use of an MLI in a wind turbine system can
help to create high‐quality AC power, reduce stress on
the components, and improve the system's overall
performance and reliability [9]. When voltage levels are
increased in an MLI, then waveform quality and
harmonic distortion are also improved. This is because
there are more voltage levels available in the inverter.
However, there are also some drawbacks, such as
increased complexity in control and a higher number of
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power semiconductor devices required. In general, the
use of MLI is more common in applications with higher
power requirements, where the benefits of improved
waveform quality outweigh the drawbacks [9, 42]. A
study presents a comparative review of three different
three‐phase inverter topologies, namely the PWM
Inverter, 180 Conduction Inverter, and the MLI. The
primary objective of this comparison is to evaluate the
percentage of total harmonic distortion (THD) present in
the output voltage for each inverter operating at a 100 V
DC input. The study was conducted using MATLAB/
SIMULINK simulation environment. The results indicate
that the 180 Conduction Inverter has the highest THD,
approximately 28.94%, followed by the multilevel topology with 9.93%, and the PWM inverter with the lowest
THD of 2.03%. A detailed analysis of the different
topologies reveals that the perfect results can be achieved
for a fixed application by selecting the appropriate
inverter topology [42]. PWM‐based inverters are among
the most crucial power‐electronic circuits used in
practical applications. These inverters are capable of
generating AC voltages of variable magnitudes and
frequencies. This is particularly useful for achieving a
wide range of speeds in drives, where it is necessary to
change the frequency of the functional AC voltage over a
broad span. The PWM inverter is particularly suited for
this task as it can easily achieve this desire and vary the
applied voltage linearly with the frequency variation.
The operating principle of the PWM inverter remains the
same for both single‐phase and three‐phase inverters.
There are different PWM techniques that can be used to
implement a PWM‐based inverter, and the methods of
implementation can vary depending on the specific
technique being used. Some common PWM techniques
include:
1. Carrier‐based PWM.
2. Sinusoidal PWM.
3. Space Vector PWM.
The specific method of implementation for each
technique can also vary depending on factors such as the
number of output phases, the type of load being driven,
and the required output voltage and frequency range
[27, 45].
Figure 11 displays the waveforms of the carrier
voltage (Vtri) and control signals (Vcontrol). The
inverter's line‐to‐line voltages and output or phase
voltage are indicated by (VA0, VB0, VC0) and (VAB,
VBC, VCA), respectively [27, 42, 45]. For the past two
decades, neutral point clamped (NPC) MLI have been
dominant in the power electronics industry. While
multicarrier pulse width modulation (MCPWM) is a
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F I G U R E 11
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ET AL.
Three phase wave forms for pulse width modulation (PWM) [42].
common PWM technique used in NPC‐MLI applications, it has limitations such as poor DC‐link voltage
balancing, common mode voltage (CMV) issues, and
total harmonics distortion (THD) limitations. Selective
harmonic elimination (SHE) technique can reduce
THD but has the drawback of narrow modulation
index range. Recently, space vector modulation (SVM)
technique is widely used in NPC‐MLI as it offers better
DC‐link voltage balancing, self‐neutral point balancing, near‐zero CMV reduction, better‐quality harmonics profile, and switching loss minimization, making
it a preferred solution for electrical conversion
applications such as electric traction, high power
industrial drives, renewable power generation, and
grid‐connected inverters. This article comprehensively
reviews the SVM for NPC‐MLI, discussing the state of
the art for two‐level SVM, extending it to three‐level
(3 L) SVM, comparing the 3 L SVM performance with
other MCPWM techniques, and various modified MLI
SVM techniques, including their implementations, DC‐
link capacitor balancing, and reduction of CMV. The
review also includes open‐end winding Inverters and
multiphase MLIs and ends with a discussion on future
trends and research directions on MLI SVM techniques
and its applications [22]. MLIs are widely used in
industrial drives, HVDC, and UPS applications. They
are also commonly used in Stand‐alone and Grid‐
connected Systems. However, conventional two‐level
inverters face limitations when operating at high
frequencies due to switching losses and device rating
constraints. To address this problem, diode‐clamped
MLIs are used, which operate at lower switching
frequencies, provide high voltages, and have improved
THD without requiring a filter. This article explains
the operation principle, analysis, and Simulink implementation of three‐phase five‐level and seven‐level
Diode‐clamped MLI and compares them based on THD
and output voltage [34]. A novel three‐level common‐
mode voltage eliminated inverter is proposed, which
utilizes an inverter structure consisting of a H‐Bridge
and a three‐level flying capacitor inverter cascaded
together. The article discusses the three‐phase space
vector polygon formed by this configuration, as well as
the polygon formed by the common‐mode eliminated
states. The entire system is simulated using Simulink
and the results are verified experimentally. This
inverter has the advantage that if one of the devices
in the H‐Bridge fails, the system can still operate as a
normal three‐level inverter at full power. Additionally,
the use of a single DC‐supply makes it suitable for
back‐to‐back grid‐tied converter applications, and it
offers improved reliability [33].
5.2 | Charge controller
For the selection of controller an extensive literature
survey and market research was completed for the better
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ET AL.
results and efficiency of the design. According to the
research, two controllers were found appropriate.
• Maximum power point tracker (MPPT).
• Pulse width modulation (PWM).
Figure 12 shows PWM as a charge‐controlling device
to charge the power bank through axial flux generator.
Further specifications of PWM is given as below:
• 100 V, 600 W when connected with a battery of 12 V.
• 100 V, 1200 W when connected with a battery of 24 V.
Once generation and control are done, now the main
part of the proposed research was to store the desired
output preferably. For this, a battery bank was used of
rating12V, 38AH (20HR) at a certain voltage and amp‐
hour capacity. The battery bank was designed so that the
user can pair it up with an off‐grid system. The rating
was chosen for the battery bank after undergoing proper
calculations. The battery bank is very necessary for
research because users can use external power which the
turbine is generating.
5.3
| Lab testing
Before testing the turbine at height, it was made sure that
the generator which is being placed at the top of the
turbine is generating enough voltages at desired rpm. For
that, the private lab was used to couple our generator
with three‐phase induction motor. The induction motor
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used was of 1500 pm (2 hp) and related to variable
frequency drive (VFD) to set the frequency and get the
desired rpm accordingly. The rpm is calculated through
the formula.
Number of poles of 30 Induction motor = 4,
Ns = 200 rpm,
Frequency = ?,
Calculation,
Ns= 120 × frequency poles
= 6.66 Hz.
Frequency
(19)
Figure 13 illustrates the basic flow diagram which
shows that VFD is controlling the three‐phase induction
motor. The induction motor is coupled by the belt with
the rotor of our generator through pulley and from there,
a bridge rectifier is used to convert AC voltages into DC.
Digital multimeter is used to record the voltages observed
in the lab, and it is found that the turbine is generating
eminent voltage at the desired rpm.
Figure 14 graphically represents that as soon as the rpm
is increased from VFD, it is found that the generator is
generating more voltage by increasing the rpm of the
generator. Through this observation, it can be said that speed
has direct linear relation with voltage. PWM, battery and
inverter are not used in lab testing because the focus is to
check whether the turbine is generating voltages at low or
high rpm. Hence, it was found that the turbine is suitable to
be tested in the flow of air for which it has been made.
5.4 | Installation testing at 50 m height
F I G U R E 12
Pulse width modulation (PWM) charge controller.
F I G U R E 13
Fundamental block diagram.
It was found in lab testing that the proposed research design
is suitable enough to be tested at any height. For this, the
team headed toward some height which is the roof top of
the house to implement the design and analyze throughput.
Once reaching there, it is found that the normal air flow
which is suitable for the rotation of turbine. Before placing
the turbine directly onto the roof, the speed of wind flow at
that roof top was recorded by the authentic researcher for
weather forecast. Wind velocity was measured day by day
and the turbine produced rated voltages.
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F I G U R E 14
Voltage versus speed graphical representation.
F I G U R E 15
Weather forecast for wind velocity.
Figure 15 displays the weather forecast and the main
parameter which was recorded is speed of air and it
showed the variations in the velocity of wind in every 5 s.
Power generation through normal turbine is totally based
on the speed of air, greater speed of air which means
greater output and minimum speed of air which means
less output. The proposed turbine design, which is based
ET AL.
on a novel axial flux generator, rated voltages or rated
throughput which varies from 400 W to 1000 W depending on the wind velocity were achieved. As the figure
shows signs of the speed of air flow on land and sea with
quantified values with knots units.
After placing the turbine at a minimum or some
height, the output of turbine is connected with the bridge
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ET AL.
rectifier. The work of the bridge rectifier is to convert
three‐phase AC voltages into DC. Rectifier is needed for
this research because it converts AC voltages into DC.
Whereas DC voltages are easy to store, it can be used for
backup and DC voltage is efficient. Within our rectifier,
there were six diodes connected which is creating a
bridge for the working of circuitry.
Figure 16 represents the rectification process. The DC
output of the bridge rectifier is then connected to PWM.
The use of PWM is to control the charging of battery. The
things observed on the screen of the PWM is turbine
voltages, turbine watts, charging current, battery voltages
and the mode of PWM charge controller. Each parameter
is shown when the turbine is rotating. The PWM is in
working condition and as it is a DC–DC controller that
has an input from bridge rectifier and output to battery
connected. The PWM has a maximum voltage rating of
100 V with 600 W of power when l2V battery is being
connected. The wattage of PWM can be converted into
1200 W when a battery bank of 24 V relates to its output.
In the storage section, a 12 V (38 Amp‐hour) battery
is used for the storage of power which is being generated
from the turbine. The battery is discharged first by
connecting to the load bank of 500 watts. It is observed in
F I G U R E 16
TABLE 1
Bridge rectifier circuit diagram.
15 of 28
PWM that the voltage rating of battery is shown to be
12.4 V first and by connecting it with load bank of 500 W
the voltages dropped down to 10 V.
To make sure that the generation of turbine is
charging the battery from 10 V until it is fully charged.
After discharging, the battery related to the output of
PWM and from there the actual voltages of battery is
shown which were 10 V. Once these parameters are set a
connection is made from positive and negative terminal
of battery to the inverter. Hence, it is found that the
PWM is working perfectly when the battery is operating
with a load and is getting charged when connected to the
turbine.
Table 1 shows the power rating of wind turbines under
no load conditions. It demonstrated wind speed, rotational
speed, voltage, current, and power. The power has been
estimated by the product of the voltage and current.
Figure 17 highlighted the graphical illustration
between rotational speed, voltages, and current. The
rotational speed (rpm) is represented by y‐axis and
voltages (v) is shown by the x‐axis. The relationship
analysis of current and voltages related to rotational
speed can be analyzed and investigated by this graphical
illustration which elaborated that under no load conditions it produced voltages. The blue line represents the
rotational speed achieved by the wind velocity, voltages
are represented by orange color and current (I) is shown
by light gray color. Moreover, the time domain is
presented at x‐axis while the magnitude to measure the
peak has been shown at y‐axis.
Table 2 displayed that the difference in throughput of
axial flux turbine under no load and load condition and after
applying direct load on axial flux turbine the speed of
rotation is affected according to the type of load and these
results is taken under low wind share (dead wind flow).
Figure 18 graphically demonstrated that the graphical
representation between voltage rotational speed (rpm)
Power rating under no load conditions.
Time
Wind speed
rpm
Voltages (V)
Current (I)
Power (P = VI)
2:00
12 Km/h
60 rpm
14 V
0.61 A
8.42 W
2:05
12 Km/h
62 rpm
14 V
0.63 A
8.82 W
2:10
12 Km/h
71 rpm
14.5 V
0.71 A
10.29 W
2:15
13 Km/h
79 rpm
16 V
0.81 A
12.96 W
2:20
13 Km/h
80 rpm
16.3 V
0.82 A
13.37 W
2:25
12 Km/h
73 rpm
14.9 V
0.72 A
10.73 W
2:30
12 Km/h
54 rpm
13.7 V
0.58 A
7.95 W
2:35
11 Km/h
50 rpm
13.5 V
0.57 A
7.69 W
2:40
11 Km/h
56 rpm
13.9 V
0.6 A
8.34 W
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F I G U R E 17 Graphical analysis between voltages and wind
speed under no load condition.
TABLE 2
ET AL.
F I G U R E 18 Graphical analysis between voltages and wind
speed under load condition.
Power rating under load conditions.
Time
Windspeed
rpm
Voltage
Current
Power
10:00
13 Km/h
38 rpm
12.1 V
0.51 A
6.17 W
10:05
13 Km/h
40 rpm
12.9 V
0.54 A
6.97 W
10:10
13 Km/h
39 rpm
12.5 V
0.49 A
6.13 W
10:15
13 Km/h
48 rpm
13.8 V
0.56 A
7.73 W
10:02
14 Km/h
49 rpm
14.2 V
0.58 A
8.24 W
10:25
13 Km/h
41 rpm
12.8 V
0.48 A
6.43 W
10:03
13 Km/h
40 rpm
13.4 V
0.52 A
6.97 W
10:35
14 Km/h
49 rpm
14.7 V
0.57 A
8.38 W
10:04
13 Km/h
35 rpm
12.1 V
0.44 A
5.32 W
10:45
13 Km/h
40 rpm
12.6 V
0.45 A
5.67 W
10:50
14 Km/h
41 rpm
13.3 V
0.48 A
6.38 W
10:55
13 Km/h
49 rpm
12.1 V
0.41 A
4.96 W
11:00
14 Km/h
50 rpm
13.7 V
0.53 A
7.26 W
11:06
14 Km/h
41 rpm
12.3 V
0.54 A
6.64 W
11:10
13 Km/h
40 rpm
12.5 V
0.55 A
6.86 W
11:15
14 Km/h
32 rpm
15.1 V
0.61 A
9.21 W
11:20
13 Km/h
31 rpm
14.8 V
0.58 A
8.58 W
11:25
13 Km/h
30 rpm
15.3 V
0.63 A
9.64 W
11:30
14 Km/h
48 rpm
12.1 V
0.52 A
6.29 W
11:35
12 Km/h
50 rpm
11.9 V
0.51 A
6.07 W
11:40
11 Km/h
51 rpm
10.9 V
0.48 A
5.23 W
11:45
11 Km/h
41 rpm
10.8 V
0.45 A
4.86 W
11:50
12 Km/h
63 rpm
11.7 V
0.55 A
6.43 W
11:55
11 Km/h
65 rpm
12.5 V
0.61 A
7.63 W
12:00
12 Km/h
64 rpm
13.8 V
0.52 A
7.18 W
12:05
13 Km/h
51 rpm
14.1 V
0.48 A
6.77 W
12:10
13 Km/h
50 rpm
13.7 V
0.49 A
6.71 W
12:15
12 Km/h
39 rpm
13.9 V
0.45 A
6.25 W
F I G U R E 19
Axial flux generator output rating.
under load and no‐load condition. Whereas, on y‐axis,
the quantitative magnitude of voltages, current and rpm
were mentioned with respect to time along on x‐axis. The
blue line represents the rotational speed achieved by the
wind velocity, voltages are represented by orange color
and current (I) is shown by light gray color.
Figure 19 exhibited the liquid crystal display of MPPT
which shows voltages and current readings of axial flux
generator.
Figure 20 represented the load which was connected
to the inverter. The load was driven through the output
of the inverter.
Figure 21 indicated the mechanical works which was
completed in Industrial Research Laboratory located at
Karachi, Pakistan. Mechanical parts were fabricated and
developed in the workshop.
Figure 22 represented the load which was connected
to the inverter. The load was driven through the output
of the inverter. The load was smoothly running using the
generated power which was stored in the battery.
6 | C O M P A R A T IV E A N A L Y S I S
The comparative analysis between axial flux and radial
flux wind turbine is given as below by their own
properties and specification. The best way to analysis
output, team members have been compared the output
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ET AL.
F I G U R E 20
Driven load through inverter.
F I G U R E 21
Mechanical work at workshop lab.
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turbine given at low wind share whiles the output of radial
flux turbine given at high wind share. The proposed axial
flux turbine was deployed at roof top for testing the results. It
was recorded that as the wind velocity increased the rpm
increased and the turbine produced more power. Power was
estimate by the product of the voltages and current.
Figure 23 depicted conventional generator's relation
between rotational speeds of the turbine with power
generated per unit hour for the conventional radial flux
generator turbine.
Figure 24 elaborates the comparison of the output
curves between wind speeds with power generated in
kwh of conventional radial flux and the proposed axial
flux generator. Whereas this graph shows the relationship of turbine speed with power of axial flux generator.
Figure 25 elaborates the graphical illustration for the
comparison of conventional radial flux generator and
proposed axial flux generator. The generated power in
KW/hr has been mentioned on vertical y‐axis and wind
speed was expressed on x‐axis. The red line showed the
proposed axial flux based power generation while blue
colored line represented the conventional radial flux
generator. The graph illustrated that the proposed axial flux
generator is capable of generating power more than the
existing radial flux generator at minimum wind velocity.
Figure 26 portrayed that the proposed axial flux
generator based turbine was deployed over 50 m height
and it produced power in wattages throughout the air
that can be utilized to run the electrical appliances or to
store. This proposed system can be deployed at multiple
places where there is a continuous flow irrespective of its
speed. Residential areas of Pakistan are still facing
electrical issues and their usage of utility is quite massive
because a lot of industries, companies and other things
which consume electrical power. So, this can play a
valuable role for such citizens of Pakistan.
7 | FAULT CLASSIFICATION
USING A I BASED CLASSIFIERS
F I G U R E 22
Driven load through inverter.
and all parameters of axial flux turbine with radial flux
turbine.
Table 3 mentioned the generated output of axial
flux turbine is more efficient as compared with radial flux
turbine. The output voltages, current, power of axial flux
It has been revealed from extensive literature review that
without the fault analysis experimentation of turbines it
would not be feasible to be deployed anywhere. Five
types of faults have been observed during the experimentation. These faults are named as generator heating
fault (gf), mains failure fault (mf), feeding fault (ff), air
cooling fault (af), and excitation fault (ef). The significance of temperature while investigation and predicting
the turbine faults cannot be ignored and neglected.
Temperature sensors were installed to measure the
blades, rotor, stator, and other temperatures. It was
observed that air cooling fault and mains failure fault
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KHAN
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TABLE 3
KHAN
ET AL.
Comparative analysis of radial flux generator with axial flux generator.
Comparative analysis
Conventional radial flux generator turbine
Proposed axial flux generator turbine
Wind
speed
(m/s)
Rotational
speed
Power
Power
unit
In kwh
3
164 rpm
0.05 W
75 kwh
3.5
218 rpm
0.11 W
4
273 rpm
4.5
Wind
speed
(m/s)
Rotational
speed
Power
Power
unit
In kwh
3
218 rpm
0.11 W
100 kwh
100 kwh
3.5
273 rpm
0.21 W
120 kwh
0.21 W
120 kwh
4
328 rpm
0.35 W
150 K2H
328 rpm
0.35 W
150 kwh
4.5
382 rpm
0.62 W
168 kwh
5
382 rpm
0.62 W
168 kwh
5
437 rpm
0.78 W
180 kwh
5.5
432 rpm
0.78 W
180 kwh
5.5
491 rpm
1.17 W
198 kwh
6
491 rpm
1.17 W
198 kwh
6
546 rpm
1.21 W
210 kwh
6.5
546 rpm
1.21 W
210 kwh
6.5
627 rpm
1.28 W
220 kwh
7
627 rpm
1.28 W
220 kwh
7
673 rpm
1.43 W
235 kwh
7.5
673 rpm
1.43 W
235 kwh
7.5
727 rpm
1.55 W
240 kwh
8
727 rpm
1.55 W
240 kwh
8
751 rpm
1.69 W
250 kwh
8.5
751 rpm
1.69 W
250 kwh
8.5
774 rpm
1.78 W
270 kwh
9
774 rpm
1.78 W
270 kwh
9
787 rpm
1.82 W
285 kwh
9.5
787 rpm
1.82 W
285 kwh
9.5
803 rpm
1.76 W
300 kwh
10
803 rpm
1.76 W
300 kwh
10
850 rpm
1.68 W
315 kwh
10.5
850 rpm
1.68 W
315 kwh
10.5
870 rpm
1.6 W
305 kwh
F I G U R E 23 Graphical analysis between power and RPM
(conventional radial fux generator).
have poor amount of production. Mostly in all faults the
blade angle was found higher usually in feeding fault.
Lowest temperature was measured in generator heating
fault. The Power generation statistical analysis for the
faults have been demonstrated in the following figure.
The Fault statistical analysis have been performed
and demonstrated in the given Table 4.
Table 5 portrayed that the fault data contains five
distinct types of faults, or fault modes, which are
F I G U R E 24 Graphical analysis between wind speed (m/s) and
power generated.
identified as follows: “gf” denotes a generator heating
fault. “mf” denotes a mains failure fault. “ff” denotes a
feeding fault. “af” denotes an air cooling fault. “ef”
denotes an excitation fault. Based on the averages
presented above, anomalous behavior can be identified
in the following Fault Modes: WF exhibits lower average,
minimum, and maximum active and reactive power than
No Fault (NF). EF exhibits higher average, minimum,
and maximum active and reactive power than NF, GF
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exhibits zero average, minimum, and maximum active
and reactive power, FF and MF exhibit higher nacelle
cable twisting than NF. AF and GF exhibit negative
nacelle cable twisting, AF and MF exhibit lower
production. All fault modes exhibit a higher blade angle,
with FF having the highest, GF exhibiting the lowest
temperature of all components (cabinet temp, T spinner,
T front bearing, T transformer, etc.). On the other hand,
other fault modes such as FF, AF, MF, and EF exhibit
higher temperatures. EF exhibits the highest temperature
in the cabinet, pitch, rotor, stator, ambient, control,
tower, and transformer. AF exhibits the highest temperature in the spinner, front bearing, rare bearing, nacelle,
main carrier, rectifier, yaw, and fan inverter.
7.1 | Support vector machine (SVM)
faults classification
Linear SVM is a commonly used algorithm in machine
learning for classification and regression analysis. It involves
F I G U R E 25 Comparative analysis between conventional radial
flux generator and proposed axial flux generator.
F I G U R E 26
Power generation with wind statistical analysis.
TABLE 4
19 of 28
Faults statistics with time.
Date and time
Fault
5/14/2020 14:39
GF
5/14/2020 14:50
GF
5/14/2020 14:58
GF
5/14/2020 15:09
GF
5/14/2020 15:20
GF
5/14/2020 15:30
GF
6/4/2020 8:09
MF
6/4/2020 8:20
MF
6/4/2020 17:00
MF
6/5/2020 15:50
MF
6/5/2020 17:41
FF
6/8/2020 23:50
AF
6/9/2020 0:00
AF
6/9/2020 0:00
MF
6/9/2020 0:09
AF
6/9/2020 0:09
MF
6/9/2020 23:50
AF
6/10/2020 0:00
AF
6/10/2020 0:00
MF
6/10/2020 0:09
AF
6/10/2020 0:09
MF
6/10/2020 23:50
AF
6/11/2020 0:00
AF
6/11/2020 0:00
MF
6/11/2020 0:09
AF
Abbreviations: AF, Air cooling fault; FF, feeding fault; GF, generator heating
fault; MF, mains failure fault.
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KHAN
66.071
66.077
34.14
52.3
52.517
53.897
53
55.565
54.971
53.154
34.14
34.14
34.14
34.14
34.14
34.14
55.565
55.565
55.565
55.565
66.071
66.071
66.071
52.3
165.99
166.512
166.05
165.64
166.587
166.013
166.017
166.019
166.032
166.512
166.512
166.512
166.512
166.512
166.512
166.032
166.032
166.032
166.032
166.236
166.236
166.236
166.05
100.37
55.565
52.5
65.925
65.925
65.825
55.677
55.677
55.677
55.677
34.86
34.86
34.86
34.86
34.86
34.86
55.677
55.677
55.677
55.677
55.874
52.614
52.5
34.86
66.915
65.925
99.92
55.677
Rotor
temperature 2
66.35
70.425
70.425
70.425
68.903
68.903
68.903
68.903
42.837
42.837
42.837
42.837
42.837
42.837
68.903
68.903
68.903
68.903
68.574
60.702
66.35
42.837
69.437
70.525
101.59
68.903
Stator
temperature 1
65.85
69.72
69.72
69.72
68.323
68.323
68.323
68.323
42.465
42.465
42.465
42.465
42.465
42.465
68.323
68.323
68.323
68.323
68.174
60.204
65.85
42.465
70.71
69.72
100.454
68.323
Stator
temperature 2
14.8
11.016
11.016
11.016
15.629
15.629
15.629
15.629
11.93
11.93
11.93
11.93
11.93
11.93
15.629
15.629
15.629
15.629
15.358
12.515
14.8
11.93
10.017
11.016
14.5
15.629
Axial flux
temperature
16.75
11.378
11.378
11.378
16.758
16.758
16.758
16.758
12.581
12.581
12.581
12.581
12.581
12.581
16.758
16.758
16.758
16.758
16.747
13.386
16.75
12.511
11.374
11.378
14.937
16.758
Ambient
temperature
45.25
51.063
51.063
51.063
46.903
46.903
46.903
46.903
30.233
30.233
30.233
30.233
30.233
30.233
46.903
46.903
46.903
46.903
47.903
43.933
45.25
30.233
50.071
51.063
64.121
46.903
Transformer
temperature
MF
FF
FF
FF
AF
AF
AF
AF
GF
GF
GF
GF
GF
GF
AF
AF
AF
AF
AF
NF
MF
GF
FF
FF
EF
AF
Turbine
faults
|
KHAN
ET AL.
10990542, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/cae.22691 by Gazi University, Wiley Online Library on [16/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
166.236
165.925
166.032
Rotor
temperature 1
Faults analysis data.
Blade
temperature
TABLE 5
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21 of 28
finding a linear boundary, called the hyperplane, that
separates different classes of data in the feature space while
maximizing the margin between the hyperplane and
the data points. The decision boundary is determined by
the support vectors, which are the closest data points to the
hyperplane. A research paper titled “A Practical Guide to
Support Vector Classification” by Chih‐Wei Hsu, Chih‐
Chung Chang, and Chih‐Jen Lin provides a comprehensive
overview of linear SVM and its practical implementation for
classification tasks. It explains the mathematical formulation
of linear SVM, including the optimization problem that
needs to be solved to find the hyperplane and the support
vectors. The article also provides guidance on selecting
appropriate hyperparameters of the SVM, such as the
regularization parameter and the kernel function [20]. The
decision boundary is called the hyperplane and is represented by a linear equation in the form:
Abbreviations: AF, Air cooling fault; EF, excitation fault; FF, feeding fault; GF, generator heating fault; MF, mains failure fault; NF, No Fault.
MF
45.25
52.5
166.05
52.3
66.35
65.85
14.8
16.75
MF
MF
45.25
45.25
16.75
16.75
52.5
52.5
166.05
166.05
52.3
52.3
66.35
66.35
65.85
65.85
14.8
14.8
Transformer
temperature
Blade
temperature
Rotor
temperature 1
Rotor
temperature 2
Stator
temperature 1
Stator
temperature 2
Axial flux
temperature
Ambient
temperature
Turbine
faults
ET AL.
w^Tx + b = 0,
(20)
where w is a weight vector perpendicular to the
hyperplane and b is the bias term. The input vector x is
classified as belonging to one of the two classes based on
which side of the hyperplane it falls on.
The margin is represented by the equation:
2/||w||,
(21)
where ||w|| is the Euclidean norm of the weight vector.
Figure 27 explained that the fault data contains five
distinct types of faults, or fault modes, which are
identified as follows: “gf” denotes a generator heating
fault which is class 0. “mf” denotes a mains failure fault
which is class 1. “ff” denotes a feeding fault which is class
2. “af” denotes an air cooling fault which is class 3 “ef”
denotes an excitation fault which is class 4. NF(no fault)
was mentioned as class 5. The confusion matrix that was
generated by the SVM showed that class 0 has a TPR of
170 and and FPR of 4 as 4 are classified as false classes.
TPR of 280 was achieved while classifying class 1 with
FPR of 10. Class 2 was classified with a TPR of 180 with 2
false positive rates (FPR). Class 3 has 160 TPR with zero
FPR. Class 4 was classified as Linear SVM with 170
positive rate. Class 5 TPR was found to be 240 with 1
false positive rate. Total TPR and FPR were found to be
1200 and 17, respectively. Therefore, accuracy, precision,
recall and F1 can be calculated for SVM algorithms and
are given below:
SVM‐ SVM‐
TPR
FPR
Accuracy
Precision
Recall
F1‐score
1200
0.9861
0.9861
0.24096
0.919
17
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KHAN
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KHAN
ET AL.
F I G U R E 27 Space vector modulation (SVM) confusion matrix
for faults classification.
F I G U R E 28
Naïve Bayes confusion matrix for faults classification.
7.2 | Naïve Bayes faults classification
The Naive Bayes algorithm works by computing the
probability of each class for a new instance based on its
features and then selects the class with the highest
probability as the predicted class. The main assumption
of Naive Bayes is that the features are independent of
each other, given the class label. This assumption allows
the algorithm to calculate the joint probability of the
features given the class by multiplying the individual
probabilities of each feature given the class [39].
The Naive Bayes classifier predicts the most probable
class label C_i for an instance with features (x_1, x_2, …,
x_n) and a set of possible class labels (C_1, C_2, …, C_k)
by calculating the expression:
C_i = argmax[P (C_i) × P (x _1|C_i)
(22)
× P (x _2|C _i ) × … × P (x_n|C_i) ],
Here, P(C_i) is the prior probability of class C_i, and P
(x_j | C_i) is the conditional probability of feature x_j given
class C_i. The function argmax returns the argument (i.e.,
class label) that maximizes the expression in the brackets.
Figure 28 explained that the fault data contains five
distinct types of faults, or fault modes, which are
identified as follows: “gf” denotes a generator heating
fault which is class 0. “mf” denotes a mains failure fault
which is class 1. “ff” denotes a feeding fault which is class
2. “af” denotes an air cooling fault which is class 3 “ef”
denotes an excitation fault which is class 4. NF(no fault)
was mentioned as class 5. The confusion matrix that was
generated by the Naïve Bayes classifier showed that the
class 0 has a TPR of 170 and and FPR of six as six are
classified in false classes. TPR of 280 was achieved while
classifying class 1 with FPR of 11. Class 2 was classified
with true positive rate (TPR) of 180 with four false
positive rate (FPR). Class 3 has 160 TPR with two FPR.
Class 4 was classified with Linear SVM with 170 positive
rate and one FPR. Class 5 TPR was found to be 240 with
three false positive rate. Total TPR and FPR was found to
be 1200 and 27, respectively. Therefore the accuracy,
precision, recall and F1 can be calculated for Naïve Bayes
algorithm are given below:
Naïve
Bayes‐TPR
Naïve
Bayes‐
FPR
Accuracy
Precision
Recall
1200
27
0.967
0.967
0.33097 0.823
F1‐score
Figure 29 demonstrated the SVM and Naïve Bayes
classifiers comparative analysis in terms of their objective
functions. There are various objective functions that can
be used to compare the performance of classification
models. Here are two other objective functions that can
be considered. F1 score: The F1 score is the harmonic
mean of precision and recall, and it provides a balanced
measure of a classifier's performance on positive and
negative cases. Higher values of F1 score indicate better
overall performance.
Classifier
F1 score
SVM
0.919
Naive Bayes
0.823
In this example, SVM has a higher F1 score than
Naive Bayes, which suggests that SVM performs better
overall on positive and negative cases.
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F I G U R E 29
23 of 28
Space vector modulation (SVM) and Naïve Bayes comparison.
Best geomterical values and stream conduct was
achieved by performing numerical simulations to produce more power. Blades were designed optimally to get
the higher tip speed proportion at minimum air flow [2].
Computational and theoretical studies have been completed to find out the appropriate position for the airfoil
horizontally for the turbine blade using Qblade software.
Fifty‐four‐meter blade was designed by using four and
five‐digit NACA airfoils [46]. Predictive analysis accuracy
for mehane‐fueled based combustion engine was
increased from 89.3% to 98.4% by using nonliner model
the robust random sample consensus (RANSAC) [28].
Response surface method (RSM) proved the maximum
bio diesel production upto 94.67%. Process variable eas
found to be reaction time [24].
F I G U R E 30
8 |
Proposed axial flux wind turbine.
RESUL TS A ND CONCL USION
Figure 30 exhibited the front and back view of axial flux
turbine which are being rotated through the flow of air. It
has been concluded that through this research the flow of
air in the residential cities of Pakistan can be utilized for
the generation of power. This proposed system can be
deployed at multiple places where there is a continuous
flow irrespective of its speed. Residential areas of
Pakistan are still facing electrical issues and their usage
of utility is quite massive because a lot of industries,
companies and other things which consume electrical
power. So, this can play a valuable role for such citizens
of Pakistan. This proposed research gives more benefits
in residential areas by installing axial flux turbine on the
plane roof of any house or building whose height is
greater than 50 m. The block diagram is very simple, and
the research is very easy to install. Block diagram consists
of a wind flow, turbine, rectifier, PWM, battery and
inverter. All components are easily available in markets
of Pakistan and are being used for generation through
renewable resources. Counter rotating wind can be an
obstacle for the maximum power generation by axial flux
generator. Counter rotating wind can be minimized by
designing the blades more optimal or it may be a
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KHAN
|
research topic for the next phase. Eddy current losses,
back EMF should be minimized in the next phase.
A 1000‐W turbine was constructed as said in research
proposal and was successful in achieving its results through
the type of flow, which was found on the rooftop whose
height is more than 50 m. Due to minimum flow, only
300 W of power was recorded at 200 rpm which operated the
load bank. Thus, by the increase of flow and unidirectional
wind flow then this turbine can easily generate 800 W on his
maximum rpm of 500–600 rpm. Axial flux design can give
more advantages in power generation at minimum wind
speed. Proposed Axial flux achieved 10.73 watt power at
wind velocity at around 80 rpm. At the wind velocity of
10 m/s and around 800 rpm 313–330 kwh was produced by
the proposed generator while the conventional generator
produced around 300 kwh. Proposed generator design
performed 35% better in terms of production efficiency
under load and no load conditions.
Moreover in the study, SVM and Naive Bayes
classifiers were evaluated for the task of classifying wind
turbine faults. The results showed that both classifiers
achieved high accuracy, with SVM achieving a slightly
higher accuracy of 0.9861 compared with Naive Bayes,
which achieved an accuracy of 0.967. The findings
suggest that SVM may be a better choice for wind
turbine fault classification due to its slightly higher
accuracy. However, the choice of classifier may depend
on other factors, such as the complexity of the problem,
the amount and quality of available data, and the
interpretability of the model. In conclusion, this research
highlights the benefits of introducing AI applications in
PBL education, particularly focusing on power generation at minimum wind velocity and turbine fault
classification. The findings emphasize the potential of
AI to enhance students' education by providing them
with hands‐on experience in tackling complex challenges
in the field of renewable energy. This work contributes to
the existing body of literature on AI integration in
education and lays the groundwork for further exploration and implementation of AI in PBL settings to enrich
students' learning experiences.
9 | FUTURE WORK AND
RECOMMENDATIONS
The integration of AI techniques in education, specifically in the context of PBL, has shown promising
potential for better application and results. To further
enhance the effectiveness and impact of AI in educational settings, the following future work and recommendations can be considered:
KHAN
ET AL.
1. Expand the scope of AI Applications: While this paper
focused on AI techniques for turbine fault diagnosis in
a turbine generator project within a PBL context,
future work should explore the applicability of AI in
other project domains within PBL. For example, AI
can be incorporated in environmental monitoring,
healthcare systems, or smart infrastructure projects,
providing a comprehensive understanding of AI's
potential across various PBL disciplines.
2. Collaboration with Industry: Collaborating with
industry partners can greatly enhance the application
and impact of AI in PBL projects. Engaging with
industry experts and professionals can provide valuable insights into real‐world project challenges and
requirements. Industry collaborations also facilitate
access to industry‐standard datasets, infrastructure,
and expertise, enabling the implementation of AI
techniques in a more realistic and practical PBL
setting.
3. Promote Interdisciplinary Collaboration: AI techniques often require interdisciplinary collaboration
to achieve optimal results. Future work should
emphasize fostering collaborations between different
disciplines within PBL, such as engineering, computer
science, data science, and other relevant fields. This
collaborative approach can lead to the development of
innovative AI solutions that address complex project
problems effectively within the PBL context.
9.1 | Continuous learning and skill
development
The field of AI is rapidly evolving, and it is essential for
students engaged in PBL to stay updated with the latest
advancements. Integrating continuous learning and skill
development programs into PBL education is crucial to
equip students with the necessary knowledge and
expertise in AI technologies. This can be achieved
through workshops, seminars, online courses, and
practical hands‐on projects that involve AI integration
within the PBL framework.
In conclusion, future work should focus on expanding the scope of AI applications in PBL, improving AI
model performance, addressing data limitations within
PBL contexts, considering ethical implications, collaborating with industry partners, promoting interdisciplinary collaboration, and emphasizing continuous
learning and skill development. By addressing these
areas, the integration of AI techniques in PBL projects
can achieve better application and results, paving the
way for more efficient and innovative PBL practices.
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ET AL.
ACKNOWLEDGMENTS
This Research was funded by Multi‐media University,
Cyber Jaya, Malaysia
11.
CONFLI CT OF I NTER EST STATEMENT
The authors declare no conflict of interest.
12.
DATA A VAILABILITY S TATEMENT
Data sharing is not applicable to this article as no new
data were created or analyzed in this study.
ORCID
Talha Ahmed Khan
6687-0920
13.
https://orcid.org/0000-000114.
REFERENCES
1. P. D. Abd Aziz, A. K. R. Mohamad, F. Z. Hamidon,
N. Mohamad, N. Salleh, and N. M. Yunus, A simulation
study on airfoils using VAWT design for low wind speed
application, 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T) 2014,
105–109. https://doi.org/10.1109/ICE2T.2014.7006228
2. A. Adnan, A. Noon, M. Anwar, H. Ahmed, T. Khan,
H. Köten Assoc. Prof. Dr., M. Siddiqi, and A. Sharif, Blades
optimization for maximum power output of vertical axis wind
turbine, Int. J. Renew. Energy Dev. 10 (2021), 585–595. https://
doi.org/10.14710/ijred.2021.35530
3. S. Ahmad, T. Nawaz, H. U. Rahman, S. Saher, and M. Ahmed,
Prospects of vertical axis wind turbines for urban electrification
in Pakistan, 2021 International Conference on Emerging
Power Technologies (ICEPT). 2021, 1–7. https://doi.org/10.
1109/ICEPT51706.2021.9435525
4. R. Anjali Krishna and L Padma suresh, A brief review on
multilevel inverter topologies, Conference on Circuit,Power and
Computing Technologies [ICCPCT] (2016).
5. SA Arefifar, M Ordonez, and Y. Mohamed, Energy management in multi‐microgrid systems—development and assessment,
IEEE Trans. Power Syst. 32 (2016), 1.
6. Y. M. Atwa, E. F. El‐Saadany, M. M. A. Salama, and
R. Seethapathy, Optimal renewable resources mix for distribution system energy loss minimization, IEEE Trans. Power Syst.
25 (2010), 360–370.
7. Y. Barbosa, L. B. Palma, and J. Martins, Modeling and semi‐
active control for a low power and free‐yaw drive HAWT, 2017
Int. Young Eng. Forum 29 (2017), 69–74. https://doi.org/10.
1109/YEF-ECE.2017.7935643
8. N. Batista, R. Melício, J. Matias, and J. Catalão, New blade
profile for Darrieus wind turbines capable to self‐start, IET
Conference Publications 2011, 1–5. https://doi.org/10.1049/cp.
2011.0219
9. Bharti, K. Varma, and A. Gupta. A review on switching
function of multi level inverter and applications, 7th India
International Conference on Power Electronics (IICPE),
Patiala, India, 2016, 1–5. https://doi.org/10.1109/IICPE.2016.
8079513
10. C. Brad, I. Vadan, and I. Berinde, Design and analysis of an
axial magnetic flux wind generator, 2017 International
15.
16.
17.
18.
19.
20.
21.
22.
23.
25 of 28
Conference on Modern Power Systems (MPS) 2017, 1–7.
https://doi.org/10.1109/MPS.2017.7974377
M. Castilla, C. Bordons, and A. Visioli, Event‐based state‐space
model predictive control of a renewable hydrogen‐based
microgrid for office power demand profiles. J. Power Sources
450 (2020), 227670.
A. Das, K. B. Chimonyo, T. R. Kumar, S. Gourishankar, and
C. Rani, Vertical axis and horizontal axis wind turbine—A
comprehensive review, 2017 International Conference on
Energy, Communication, Data Analytics and Soft Computing
(ICECD S) 2017, 2660–2669. https://doi.org/10.1109/ICECDS.
2017.8389937
P. H. Divshali, B. J. Choi, and H. Liang, Multi‐agent transactive
energy management system considering high levels of renewable
energy source and electric vehicles, IET Gener. Transm. Distrib.
11 (2017), 3713–3721.
T. S. El‐Hasan, Development of axial flux permanent magnet
generator for direct driven micro wind turbine, 2016 IEEE
International Conference on Renewable Energy Research and
Applications (ICRERA) 2016, 169–172. https://doi.org/10.
1109/ICRERA.2016.7884531
Y. El‐Okda, M. S. Emeara, N. Abdelkarim, K. Adref, and
H. A. Hajjar, Performance of a small horizontal axis wind
turbine with blade pitching, 2020 Advances in Science and
Engineering Technology International Conferences (ASET)
2020, 1–5. https://doi.org/10.1109/ASET48392.2020.9118196
H. Gör and E. Kurt, Effects of back iron components on
efficiency and generated power for new wind energy generators,
Electr. Power Compon. Syst. 46 (2018), no. 10, 1105–1122.
https://doi.org/10.1080/15325008.2018.1488013
L Guo, W Liu, X Li, Y Liu, B Jiao, W Wang, C. Wang, and
F. Li, Energy management system for stand‐alone wind‐
powered‐desalination microgrid, IEEE Trans. Smart Grid 7
(2014), 1.
S. Guttowski, S. Weber, E. Hoene, W. John, and H. Reichl,
EMC issues in cars with electric drives, in Proc. IEEE Symp.
Electromagn. Compatibility. Symp. Record 2 (2003),
777–782.
T. G. Habetler, R. Naik, and T. A. Nondahl, Design and
implementation of an inverter output LC filter used for dv/dt
reduction, IEEE Trans. Power Electron. 17(May (2002), no. 3,
327–331.
C.W. Hsu, C.C. Chang, and C.J. Lin, A practical guide to
support vector classification, National Taiwan Univ. 7 (2010).
https://www.researchgate.net/publication/2926909_A_
Practical_Guide_to_Support_Vector_Classification_ChihWei_Hsu_Chih-Chung_Chang_and_Chih-Jen_Lin
TDK Electronics. Innovative filter solutions for e‐mobility,
2015. https://www.tdk-electronics.tdk.com/en/373562/
techlibrary/articles/applications-cases/applications-cases/
innovativefilter-solutions-for-e-mobility/1311962
V. Jayakumar, B. Chokkalingam, and J. L. Munda, A
comprehensive review on space vector modulation techniques
for neutral point clamped Multi‐Level inverters, IEEE Access 9
(2021), 112104–112144. https://doi.org/10.1109/ACCESS.2021.
3100346
A. Keyhani, M. N. Marwali, and M. Dai, Integration of green
and renewable energy in electric power systems, Wiley Online
Library, 2009.
10990542, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/cae.22691 by Gazi University, Wiley Online Library on [16/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
KHAN
|
24. A. Kolakoti, B. Prasadarao, K. Satyanarayana, M. Setiyo,
Köten Assoc. Prof. Dr. H., and M. Raghu, Elemental, thermal
and physicochemical investigation of novel biodiesel from
wodyetia bifurcata and its properties optimization using
artificial neural network (ANN), Automot. Exp. 5 (2021),
3–15. https://doi.org/10.31603/ae.6171
25. M. Liao, L. Dong, L. Jin, and S. Wang, Study on Rotational
Speed Feedback Torque Control for Wind Turbine Generator
System, 2009 International Conference on Energy and
Environment Technology, 2009, 853–856. https://doi.org/10.
1109/ICEET.2009.211
26. M. Murshed, Y. Arafat, and M. A. Razzak, Design of blades for
a low‐speed 400W wind turbine suitable for coastal area of
Bangladesh, 2015 3rd International Conference on Green
Energy and Technology (ICGET) 2015, 1–6. https://doi.org/10.
1109/ICGET.2015.7315117
27. R. Nair, R. Mahalakshmi and K. C. Sindhu Thampatty,
Performance of three phase 11‐level inverter with reduced number
of switches using different PWM techniques, 2015 International
Conference on Technological Advancements in Power and Energy
(TAP Energy), 2015, 375–380. https://www.semanticscholar.org/
paper/Performance-of-three-phase-11-level-inverter-with-NairMahalakshmi/6166d2acee58636653ed465985c5d38356715dc2
28. M. M. Namar, O. Jahanian, and H. Koten, The start of
combustion prediction for methane‐fueled HCCI engines:
traditional vs machine learning methods, Math. Probl. Eng.
2022 (2022), 1–11.
29. C. Ostia, J. M. Martinez, K. T. Coderes, G. J. Estacio,
A. R. Ramchahi, and J. V. Pulido, Development of a Dual
Wind Turbines using the Savonius and Archimedes Spiral
Principle, 2018 IEEE 10th International Conference on
Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM) 2018, 1–5. https://doi.org/10.1109/
HNICEM.2018.8666238
30. M. Pamujula, A. Ohja, R. D. Kulkarni, and P. Swarnkar,
Cascaded ‘H’ Bridge based multilevel inverter topologies: a
review, 2020 International Conference for Emerging Technology (INCET), Belgaum, India 2020, 1–7. https://doi.org/10.
1109/INCET49848.2020.9154031
31. N. Patel and M. N. Uddin, Design and performance analysis of
a magnetically levitated vertical axis wind turbine based axial
flux PM generator, 2012 7th International Conference on
Electrical and Computer Engineering 2012, 741–745. https://
doi.org/10.1109/ICECE.2012.6471657
32. N. Patel and M. N. Uddin, Design and performance analysis of
a magnetically levitated vertical axis wind turbine based axial
flux PM generator, 2012 7th International Conference on
Electrical and Computer Engineering, 2012, 741–745, https://
doi.org/10.1109/ICECE.2012.6471657
33. P. RoshanKumar, P. P. Rajeevan, K. Mathew, K. Gopakumar,
J. I. Leon and L. G. Franquelo, Common‐mode voltage
eliminated three‐level inverter using a three‐level flying‐
capacitor inverter and cascaded H‐Bridge, 2012 IEEE International Conference on Power Electronics, Drives and Energy
Systems (PEDES), Bengaluru, India, 2012, 1–6. https://doi.
org/10.1109/PEDES.2012.6484276
KHAN
ET AL.
34. C. Shravani and S. Veluri, Reduction of harmonic content for
power electronic applications using diode‐clamped‐multi level
inverters, IEEE‐HYDCON, Hyderabad, India, 2020, 1–5.
https://doi.org/10.1109/HYDCON48903.2020.9242887
35. S.A. Shufat, E. Kurt, C. Cinar, F. Aksoy, A. Hançerlioğulları,
and H. Solmaz, Exploration of a stirling engine and generator
combination for air and helium media, Appl. Therm. Eng. 150
(2019), 738–749. https://doi.org/10.1016/j.applthermaleng.
2019.01.053
36. A. Singh, S. Bhandari, and J. Kumar, A Comparative study on
multilevel inverters with reduced number of components—a
review, 2022 1st International Conference on Sustainable
Technology for Power and Energy Systems (STPES), SRINAGAR, India 2022, 1–6. https://doi.org/10.1109/STPES54845.
2022.10006427
37. BV Solanki, K Bhattacharya, and CA. Canizares, A sustainable
energy management system for isolated microgrids, IEEE Trans.
Sustain. Energy 8 (2017), 1507–1517.
38. T. T. Song, H.S.H. Chung, and A. Ioinovici, A high‐voltage
dc‐dc converter with Vin/3 – voltage stress on the primary
switches, IEEE Trans. Power Electron. 22 (Nov 2007), no. 6,
2124–2137.
39. Spam Filtering using Naive Bayes Classifier with Feature
Selection by Ritu Arora and Renu Dhir, published in
International Journal of Computer Applications, Volume 20,
No. 6, April 2011
40. M. S. Taha, H. H. Abdeltawab, and Y. A.‐R. I. Mohamed, An
online energy management system for a grid‐ connected hybrid
energy source, IEEE J. Emerg. Sel. Topics Power Electron. 6
(2018), 2015–2030.
41. Z. Ullah, S. M. Ali, I. Khan, F. Wahab, M. Ellahi, and B. Khan,
Major prospects of wind energy in Pakistan, 2020 International
Conference on Engineering and Emerging Technologies
(ICEET) 2020, 1–6. https://doi.org/10.1109/ICEET48479.
2020.9048201
42. A. A. Usmani, M. Shahrukh, and A. Mustafa, Comparison of
different three phase inverter topologies: a review, 2017
International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT),
Coimbatore, India 2017, 19–24. https://doi.org/10.1109/
ICIEEIMT.2017.8116837
43. Z Wang, B Chen, J. Wang, and J. Kim, Decentralized energy
management system for networked microgrids in grid‐connected
and islanded modes, IEEE Trans. Smart Grid 7 (2016),
1097–1105.
44. R. Wang, H. Zhang, C. Li, and C. Ma, Dynamic power flow
calculation method of power system with wind power, 2015 5th
International Conference on Electric Utility Deregulation and
Restructuring and Power Technologies (DRPT) 2015,
1987–1991. https://doi.org/10.1109/DRPT.2015.7432564
45. X. Wu, G. Tan, Z. Ye, Y. Liu, and S. Xu, Optimized
commonMode voltage reduction PWM for three‐phase voltage‐
source inverters, IEEE Trans. Power Electron. 31 (2016), no. 4,
2959–2969.
46. M. Yilmaz, H. Köten, E. Çetinkaya, and Z. Coşar, A
comparative CFD analysis of NACA0012 and NACA4412
airfoils, J. Energy Syst. 2 (2018), 145–159.
10990542, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/cae.22691 by Gazi University, Wiley Online Library on [16/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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ET AL.
AUTH OR BI OGRAPH IES
Talha Ahmed Khan has completed
Doctor of Philosophy (Artificial Intelligence) from Universiti Kuala Lumpur
(UniKL) Malaysia and recently pursuing
his Post doctorate from Multimedia
University (MMU), Malaysia. His education comprises of a Bachelor of Engineering, Masters
of Engineering in Telecommunications, and PhD. He
was previously associated with UIT‐NED since 6 July
2009 as an Assistant Professor. Now Dr. Talha A.
Khan is serving Bahria University Karachi Campus as
an Associate Professor. During his PhD, he deeply
studied the role of Artificial Intelligence and Machine
Learning based Electrical and Electronics Engineering applications and published several research
papers in prestigious and well‐reputed international
journals/conferences.
Muhammad Alam is a Professor of
Computer Science. He is recently associated with Riphah International University, Islamabad. He has also worked as
an Associate Dean in CCSIS and HoD
Mathematics, Statistics and Computer
Science Departments of IoBM, Pakistan. Dr. Alam is
enjoying 20 years of research and teaching experience
in Canada, England, France, Malaysia, Saudi Arabia,
and Bahrain and authored 150+ research articles
which are published in well‐reputed journals of high
impact factor, Springer Link book chapters, Scopus
indexed journals and IEEE conferences. He has honor
to work as an online laureate (facilitator) for MSIS
program run by Colorado State University, USA and
Saudi Electronic University, KSA. Dr. Muhammad
has also established research collaboration with
Universiti Kuala Lumpur (UniKL) and Universiti
Malaysia Pahang (UMP). Currently, Dr. Alam is also
working as an adjunct professor in UniKL and
supervising 12 PhD students.Dr. Alam has done
PostDoc from Malaysia in “Machine Learning Approaches for Efficient Prediction and Decision Making”. He has done PhD in Computer Engineering,
PhD in Electrical and Electronics Engineering, MS in
System Engineering, and M.Sc. Computer Science
from France, United Kingdom, and Malaysia. Universite de LaRochelle awarded him Très Honorable
(with distinction) PhD due to his research impact
during his PhD.
|
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Syed Safdar Ali Rizvi completed MCS
(Masters in Computer Science) and MS
(CS) from Mohammad Ali Jinnah University, Karachi. Afterwards, completed PhD
from Electrical and Electronic Engineering
Department, Universiti Teknologi PETRONAS, Malaysia. He is an experienced teacher with
extensive teaching experience at the undergraduate as
well as post‐graduate level. He is currently serving Bahria
University as a Senior Associate Professor. Moreover, he
is the Head of Communication and Networks (COMANET) research group and Head of Computer Sciences
Department. Dr. Safdar has more than 40 publications in
international conferences and peer‐reviewed international ISI indexed impact factor journals. His research
interest includes Ubiquitous Computing, Internet of
Things, Heterogeneous Wireless Networks, AI and
Machine Learning.
Zeeshan Shahid has a PhD and MSc
degrees in Electrical and Electronics Engineering from International Islamic University Malaysia. He obtained his BE from
Usman Institute of Technology (UIT). He
has published a number of research articles
in high‐quality international scientific journals and
conference proceedings. He has numerous years of
experience in the industrial and academic field. His
research interest is in power engineering specialized in
grid‐tied inverters, multilevel inverters, DC‐DC converters, Integration of renewable energy sources (RES) with
utility grids, and Power quality improvement.
M. S. Mazliham, President, Multimedia
University, Cyber Jaya, Malaysia DSAP,
SAP PhD in Computational Intelligence
& Decision, University De La Rochelle,
France Post Master Degree in Electronics, University De Montpellier II France
Master in Electronics Electrotechnics and Automation, Bachelor in Electronics Electrotechnics and
Automation, University De Montpellier II France
Diploma in Science, University De Montpellier II,
France Mazliham. Mohd Su'ud received the Diploma
degree in science, the bachelor's degree in electronics
electrotechnics and automation, the master's degree
in electronics electrotechnics and automation, and
the master's degree in electronics from the University
de Montpellier II, France, the master's degree in
electrical and electronics engineering from the
10990542, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/cae.22691 by Gazi University, Wiley Online Library on [16/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
KHAN
|
University of Montpellier, in 1993, the PhD degree in
computational intelligence and decision from the
University De La Rochelle, France, and the PhD
degree in computer engineering from the Université
de La Rochelle, in 2007. Since 2013, he has been the
President/the CEO of Universiti Kuala Lumpur,
Malaysia. He has vast experience of publishing in
high‐quality international scientific journals and
conference proceedings. He has numerous years of
experience in the industrial and academic field.
KHAN
ET AL.
How to cite this article: T. A. Khan, M. Alam, S.
A. Rizvi, Z. Shahid, and M. S. Mazliham,
Introducing AI applications in engineering
education (PBL): An implementation of power
generation at minimum wind velocity and turbine
faults classification using AI, Comput. Appl. Eng.
Educ. 32, (2024), e22691.
https://doi.org/10.1002/cae.22691
10990542, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/cae.22691 by Gazi University, Wiley Online Library on [16/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
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