Smart Drone with Renewable Smart System
MAYS ABBAS AL-BAHRANY, AHMAD T. ABDULSADDA*
AL Furat Al Awast Technical University, Al Najaf 540001, IRAQ.
Abstract: - In order to lessen its negative effects on the environment and to maintain its future operations in a clear,
renewable, and sustainable manner, the aviation industry has begun developing designs that are dependent on alternative
energy sources but also friendly to the environment and conventional energy. Solar energy has been suggested as a potential
remedy. Aerial vehicles driven by solar energy are viewed as essential to limiting the consequences of global warming. In this
study, a MATLAB/Simulink environment is used to simulate a mathematical model of a solar-powered BLDC motor of a
UAV.
under photovoltaic (PV) array systems, the phrase "maximum power point tracking" (MPPT) is crucial to ensuring that, under
specific circumstances, the connected systems receive the greatest power output. This study simulates "fuzzy logic control,"
one of the preferred MPPT methods, using a solar-powered BLDC motor for an unmanned aerial vehicle (UAV) design.
The PV cell, MPPT, buck-boost converter, and BLDC motor models in the cascade structure are simulated, tested, and the
results are compared to the DC motor technical data. As a result, despite changes in irradiance, the results of mathematical
model simulation overlap with motor technical reference values.
A mathematical model of a solar-powered BLDC motor for a UAV is created and simulated using the MATLAB/Simulink
environment, in contrast to prior solar-powered BLDC motor literature efforts. The fuzzy logic control MPPT technique is
preferred for adjusting the maximum power output at the solar cell, and a buck-boost converter structure is connected
between the MPPT and the BLDC motor mathematical model. It is recommended for usage in solar-powered UAV designs in
the future.
Keywords: - Buck-BOOST Converter, Unmanned Aerial Vehicle, MPPT, BLDC Motor, FLC, Solar Power.
Received: December 12, 2022. Revised: September 13, 2023. Accepted: October 8, 2023. Published: November 6, 2023.
1. Introduction
Given that it is a source of plentiful and clean energy, solar
panels have received a lot of attention recently. Solar cells
are a type of clean, renewable energy source that work by
converting light energy into electrical energy. Solar energy
may lower the emissions from conventional vehicles by
92%. [1]
Since the solar cell has a distinctive voltage, power, and
current graph, the voltage of a solar panel must be
controlled to get the most power. Power conditioning often
employs a regulated converter that uses the Maximum
Power Point Tracking (MPPT) algorithm. To maintain the
panel operating at its best, MPPT will condition the panel
voltage. The Perturb and Observation technique (P&O),
which is simple to regulate but time inefficient and has
significant loss, is the most widely used form of MPPT
algorithm. P&O's tracking stride has an impact on tracking
precision and speed. Peak tracking accuracy will rise if the
tracking step is reduced, but peak tracking speed will fall,
and vice versa.
The Hill Climbing algorithm is one of the most frequently
used algorithms for power optimization. The Hill Climbing
algorithm operates by varying the converter's duty cycle
while monitoring how it affects the output power of the
solar panel. greater duty cycles result from greater power
and current levels, whereas lower duty cycles result from
lower power and current levels [2].
Fuzzy logic control is used to address these drawbacks.
Fuzzy logic outperforms the P&O method in terms of
maximum power point detection time and fluctuations. [3]
The maximum power tracking time is accelerated, and
power oscillations are decreased, using fuzzy logic control.
When the weather changes nonlinearly, fuzzy logic can
make wise judgements [4]
Since the output of fuzzy logic is neither 0 or 1, but rather
the accumulation of the input variable's membership in the
membership functions, the output value depends on the rule
that links the input and the weighting of each membership
function. In this study, an MPPT is created utilizing fuzzy
logic control to provide a switching duty cycle that changes
according on the load and solar panel conditions. The
tracking time of solar electricity is found to be quicker by
using this approach.
1.1 Solar Powered UAV
primary energy source is solar energy, Solar irradiance and
plane position determine how much power is generated by
solar panels, and this fluctuation is constant. Solar panel
power will have an effect on the brushless dc motor's
(BLDC) speed and torque. In order to sustain the speed and
thrust of the BLDC motor, MPPT must maximize and track
quickly.
The goal of this study is still to create an MPPT solar
controller that will power a BLDC motor. This approach's
efficacy is contrasted with that of traditional MPPT solar
controller.
1.2 Photovoltaic Model
A substance known as a photovoltaic may transform the
energy of photons into electrical energy. When photons
have short enough wavelengths, they have the power to
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
188
Volume 5, 2023
liberate electrons from atoms. On a conductor, the electrons
can flow and transform into an electric current. The sun
provides the energy needed to break the bonds. Given that
the earth's surface gets energy at a rate of 6000 times the
planet's daily energy needs, this is a significant potential [3].
A PV module may be represented by an equivalent circuit
made up of a current source, a diode, and a resistor to
represent the internal resistance. When the solar cell is
exposed to sunlight, current is produced as:
The internal resistance Ish and the current flowing through
the diode ID determine the solar cell's output current. The
equation then becomes: -
The equation below must be used to determine current flows
in the diode.
Then total equivalent circuit of solar cell become as Fig (1) :
Figure (1) equivalent circuit of solar cell
The relationship between a PV module's output of current
and voltage may be calculated using Figure 2.
The functioning of the solar panel will produce the I-V
characteristic curve, in accordance with equation above. The
solar panel's operational point occurs when the I-V
characteristic curve and the load characteristics cross at
constant irradiance and temperature. The operational point
of the panel switches from the zero resistance, which results
in the Isc, to the infinite resistance, which results in the
appearance of the Voc [5] as shown in fig. (2).
Fig. 2. characteristic curve of a PV module
1.3 The Buck-boost Converter
The buck-boost converter transforms uncontrolled source
voltages into desired output voltage levels that are greater or
lower. In Figure 3, a common buck-boost model is
displayed. In this diagram, Vs represents the input voltage,
MI a switch, VL represents the voltage across the
inductance, Vo represents the output voltage across the load,
Vref represents the reference voltage, and L, C, D1 and D2,
and RL represent, respectively, inductance, capacitance,
diodes, and load resistance [6, 7].
Figure (3) DC-DC Buck Boost Converter System used in
simulations.
The circuit works in two modes, as seen in Figure 3. When
the transistor is turned on and diode D2 is reverse biased,
the first thing happens. The input current IL passes via the
transistor MI and inductor L in this mode. The voltage
across the inductor is determined by:
whereas equation bellow calculates the current flowing via
the inductor:
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
189
Volume 5, 2023
If the transistor MI is off, the current passes from L via C,
the diode D2, and to the load in the second mode. Equation
bellow provides the voltage across the inductor in this
scenario.
When the energy stored in inductor L is transmitted to the
load and the inductor current decreases, as illustrated in
Figure 4 [8, 9], the transistor is turned back on in the
following cycle.
Figure (4) Waveforms of Buck-Boost converter
Figure 5 depicts the controller’s physical layout in a DC-DC
Buck boost converter. The graphic makes it obvious that the
duty cycle value affects the buck-boost converter’s output
voltage. The duty cycle’s value ranges from 0 to 1.
According to the duty cycle and voltage relationship
indicated in equation 4, if the duty cycle value is more than
0.5, the output voltage will be greater than the input voltage.
Therefore, we may get the desired output voltage by
adjusting the buck-boost converter’s duty cycle.
Figure (5) General block diagram for the control of DC-DC
Buck-boost converter
1.4. Fuzzy Logic Controller
The Fuzzy-based MPPT system of solar panel consists of
two inputs and one output, which are the magnitude of error
and error changes from power tracking respectively. Fuzzy
logic output is a duty cycle change which is used for
MOSFET switching.
errors and changes in errors. The error value will be near to
zero when the power is at its highest, and the direction of
the error change corresponds to the power change. The duty
cycle change will increase with distance from the maximum
point. Fuzzy logic’s membership function receives these
input-output parameters, which causes the duty cycle to
change in response to the power situation rather than remain
constant.
The range of values for the membership function, which
represents the input-output values, is defined through
experiment.
The system’s reaction to the duty cycle modification
determines how the duty cycle will be changed. Figure 6-8
shows how the system’s membership features work.
Fig (6) Membership function of error.
Fig (7) Membership function of error change..
Fig (8) Membership function of duty cycle change.
Rules are used to link the two inputs in order to establish the
relationship between them and the output of the MPPT.
Rules are used to govern the duty cycle so that the error of
input-output power in solar panels is always zero. These
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
190
Volume 5, 2023
rules describe according to different input situations. Table
(1) lists the rules applied in this MPPT system.
TABLE 1. RULES OF FUZZY LOGIC CONTROLLER
FOR MPPT SYSTEM
1.5 BLDC Motor
Due to its advantages over conventional DC brushed
motors, as well as the faster development, efficiency
assessment of control electronics, and control semi-
conductor power technology, brushless DC motors
(BLDCMs) are widely utilized [10]. The BLDC motor is a
permanent magnet synchronized machine (PMSM) that has
six transistors and an electrical system that determines
(on/off) switching based on the position of the device’s
rotor. BLDCM works in the same way as a DC motor, but
because it lacks brushes, maintenance costs are lower.
Additionally, it is known as an electronically commutated
motor (ECM), is powered by DC energy, and operates with
a great deal of reliability [11] . BLDC motors are frequently
used for a variety of applications in industries including
automation and medical solutions for a variety of equipment
[12]. The BLDCM is becoming more popular as
performance increases. These motors have several appealing
qualities, including high instantaneous torque, longer life,
the ability to regulate speed over a wide range with little
maintenance required, less inertia, a higher power to volume
ratio, and lower friction [13] . The main problem with this
engine is its expensive design and development costs, as
well as the fact that the BLDC motor controller is far more
difficult to use than a conventional motor controller[14] .
BLDCMs have a higher energy density than other types of
motors (such as induction machines (IM)), and they appear
to have no loss and no commutation inside the copper of the
rotor, making them perfect for high-performance
applications [15,16].
Figure (9). Basic BLDC motor construction[17]
2. Proposed System
In order to manage the voltage obtained from the PV cell
and supply it to the BLDC motor of UAV at a consistent
level, we constructed and simulated a buck-boost converter
using fuzzy logic controller in this paper.
The drone’s BLDC motor requires electricity, which is
produced by the PV array. The BLDC motor receives this
electrical energy via a buck-boost converter. The primary
issues with PV systems are their relatively poor energy
conversion efficiency and the clear climate dependency of
their energy characteristics. The maximum power point
tracking method was created utilizing a fuzzy logic
controller to improve the effectiveness of power conversion
and achieve the necessary voltage for the BLDC motor.
3. Design of Proposed System
The following provides a full description of the design of
several stages, including a PV array, buck-boost converter,
and BLDC motor which was simulated by using
MATLAB/Simulink.
3.1 Design of PV Array
Table (2) The design parameters regarding the PV system
3.2 Design of buck-boost converter
System Variables
Values
PV Input Voltage
Volt- Volt
Output Voltage
21 Volt
Filter Inductance
133e-5H
Filter Capacitance
100.67e-3F
Output Resistance
10 Ohm
Table (3) The design parameters of buck-boost converter
Electrical characteristic
100 W
0 5 W
21.90 V
17.90 V
6.03 A
5.59 A
1000 V
15 A
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
191
Volume 5, 2023
3.3 Design of BLDC motor
Physical quaintly
Numerical
value
Suitable unit
Rated speed
3,000
(rpm)
Rating voltage
500
(Vdc)
Rating (P)
1.00
(kW)
Number of phases
(connection)
3 (star)
Stator phase
resistance Rs
2.8750
(ohm)
Stator phase
inductance Ls
8.5e-3
(H)
Flux linkage
stablished
by magnets (V.s):
0.175
Voltage constant
146.6077
(V_peak L-
L/krpm)
Torque constant
1.4
(N.m/A_peak)
Back EMF flat area
120
(degrees)
Inertia, friction
factor, pole pairs
[0.8e-3, 1e-3
4]
[ J(kg.m^2)
F(N.m.s) p()]:
Initial conditions
[0,0, 0,0]
[ wm(rad/s) the
tam(deg)
ia,ib(A)]:
Table (3) The design parameters of BLDC motor
4. Simulation and Results of
Proposed System
Using MATLAB/Simulink, a photovoltaic array is
simulated as the necessary power source from a BLDC
motor for the drone as shown in figure (10). To demonstrate
the stability of the system under dynamic conditions, the
solar radiation level is instantly reduced from 1000 W/m2 to
100 W/m2, and that power passes through a converter buck-
boost, which regulates it to suit the needs of the BLDC
motor. This process is done by designing one of the MPPT
(FLC) technologies that controls the duty cycle of the switch
in the buck-boost converter depending on the reference
voltage of the BLDC motor.
Figure (10) simulated system
Figure (11) the output of the PV array and the output of
BUCK-BOOST converter
Figure (12) the results of the BLDC motor
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
192
Volume 5, 2023
5. Conclusion and Discusion
The term "maximum power point tracking" (MPPT) under
photovoltaic (PV) array systems is essential to guarantee
that, under specified conditions, the linked systems receive
the greatest power production. In this work, a solar-powered
BLDC motor is used in the construction of an unmanned
aerial vehicle (UAV) to replicate "fuzzy logic control," one
of the popular MPPT techniques.
The cascade structure's PV cell, MPPT, buck-boost
converter, and BLDC motor models are simulated, put to the
test, and the results are contrasted with the technical
information for DC motors. As a result, the outcomes of
mathematical model simulation overlap with motor
technical reference values despite variations in irradiance.
A mathematical model of a solar-powered BLDC motor for
a drone was created and simulated using the
MATLAB/Simulink environment.
The motor requires a voltage of 21 volts to operate, and a
solar cell is used to provide this value. The solar cell gives
off different levels of energy as a result of its influence on
changing solar radiation. The voltage range produced by the
solar cell ranges between 21.9 volts when the solar radiation
value is 1000 W/m2 and 20 volts when the radiation value is
100 W/m2, so it is best to use MPPT fuzzy logic control
technology to adjust the maximum output. For energy in the
solar cell, the boost converter structure is connected
between the solar cell and the mathematical model of the
BLDC motor, which adjusts the voltage fluctuation to a
constant level value according to the needs of the BLDC.
It is recommended for use in future solar-powered drone
designs.
References
[1]. A. Diab-Marzouk, “SiC-based bidirectional cuk
converter with differential power processing and
MPPT for a solar powered aircraft,” IEEE
Transaction on Transportation Electrification, vol.
1, no. 4, pp. 369-381, 2015.
[2]. N. V.P, “Fuzzy logic based hill climbing method
for maximum power point tracking in PV system,”
in International Conference on Power, Energy and
Control (ICPEC), 2013.
[3]. G. Masters, Renewable and Effectife Power
System, New Jersey: John Wiley and Sons, 2004.
[4]. A. E. Khateb, “Fuzzy-logic-controller-based
SEPIC converter for maximum power point
tracking,” IEEE Transactions On Industry
Applications, vol. 50, no. 4, pp. 2349-2358, 2014.
[5]. Krismadinata, N. Abd. Rahim, H. Wooi Ping and J.
Selvaraj, “Photovoltaic module modeling using
imulink/matlab,” in The 3rd International
Conference on Sustainable Future for Human
Security, 2012.
[6]. R, R., &Babu, S. (2013, July). Design and Control
of DC-DC Converter using Hybrid Fuzzy PI
Controller. IJREAT International Journal of
Research in Engineering Technology, 1(3), 1-7.
[7]. Hart, D. W. (2010). Power Electronics (11th ed.,
pp. 1-477). New York, NY:McGraw-Hil.
[8]. M. E. Sahin, H. I Okumus, "Fuzzy Logic
Controlled Synchronous Buck DC-DC Converter
for Solar Energy- Hydrogen Systems", INISTA
2009 Conference, 2009.
[9]. M. E. Sahin, "Designing An Electrolyses System
With Dc/Dc Buck Converter", M.Sc. Thesis, Gazi
University Institute of Science andTechnology,
April 2006.
[10]. A. Georgiev, T. Papanchev, and N.
Nikolov, “Reliability assessment of power
semiconductor devices,” 2016 19th International
Symposium on Electrical Apparatus and
Technologies (SIELA), pp. 1–4, 2016, doi:
10.1109/SIELA.2016.7543003.
[11]. J. S. Park, K. D. Lee, S. G. Lee, and W. H.
Kim, “Unbalanced ZCP compensation method for
position sensorless BLDC motor,” IEEE
Transactions on Power Electronics, vol. 34, no. 4,
pp. 3020–3024, Apr. 2019, doi:
10.1109/TPEL.2018.2868828.
[12]. Y. I. Al Mashhadany, “ANFIS-inverse-
controlled PUMA 560 workspace robot with
spherical wrist,” Procedia Engineering, vol. 41, pp.
700–709, 2012, doi: 10.1016/j.proeng.2012.07.232.
[13]. P. Electronics, “Comparative study of
controller design for four quadrant operation of
three,” International Journal of Engineering
Sciences & Research Technology, vol. 3, no. 3, pp.
1181-1186, Mar. 2014.
[14]. C. Ganesh, M. Prabhu, M. Rajalakshmi,
G. Sumathi, V. Bhola, and S. K. Patnaik, “ANN
Based PID Controlled Brushless DC drive
System,” ACEEE Int. J. on Electrical and Power
Engineering., vol. 03, no. 01, pp. 45–48, 2012, doi:
01.IJEPE.03.01.
[15]. Y. A. Mashhadany, K. S. Gaeid, and M.
K. Awsaj, "Intelligent controller for 7-DOF
manipulator based upon virtual reality model,"
2019 12th International Conference on
Developments in eSystems Engineering (DeSE),
2019, pp. 687-692, doi:
10.1109/DeSE.2019.00128.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
193
Volume 5, 2023
[16]. K. Sivaraman, R. M. V. Krishnan, B.
Sundarraj, and S. Sri Gowthem, “Network failure
detection and diagnosis by analyzing syslog and
SNS data: Applying big data analysis to network
operations,” International Journal of Pure and
Applied Mathematics, vol. 8, no. 9, pp. 883–887,
2019, doi: 10.35940/ijitee.I3187.0789S319.
[17]. Y. I. Al-Mashhadany, "Inverse kinematics
problem (IKP) of 6-DOF manipulator by locally
recurrent neural networks (LRNNs)," 2010
International Conference on Management and
Service Science, 2010, pp. 1-5, doi:
10.1109/ICMSS.2010.5577613.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2023.5.21
Mays Abbas Al-Bahrany, Ahmad T. Abdulsadda
E-ISSN: 2769-2507
194
Volume 5, 2023