Artificial neural network based unity power factor corrector for single phase DC-DC converters

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 4, August 2020, pp. 4145~4154
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i4.pp4145-4154  4145
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
Artificial neural network based unity power factor corrector
for single phase DC-DC converters
Hussain Attia
Department of Electrical, Electronics and Communications Engineering, School of Engineering,
American University of Ras Al Khaimah, United Arab Emirates
Article Info ABSTRACT
Article history:
Received Sep 14, 2019
Revised Feb 27, 2020
Accepted Mar 8, 2020
Due to the negative effects of the non-linear semiconductor devices and
the passive electrical components (inductor and capacitor) in the converter
circuits, and that are deteriorating the power factor (PF) and total harmonics
distortion (THD) of grid current, this study proposes a novel unity PF
correction controller based on a new algorithm of neural network to improve
the performance of a single phase boost DC-DC converter with respect to
the mentioned concerns. The controller guarantees stable load voltage.
The PF corrector, firstly measures the phase shift between grid voltage and
grid current waveforms, then through a new artificial neural network
(ANN) algorithm, a suitable duty cycle is predicted to guide and control
the converter to reduce the phase shift between grid voltage and grid current
as possible to have maximum PF which is unity PF, and to improve the THD
level of grid current. The proposed system is simulated and evaluated via
Simulink of MATLAB, the simulation results are collected at constant duty
cycle and at controlled duty cycle through the proposed PF controller using
different loads. The presented PF controller guarantees the unity power
factor, and enhances the grid alternating current THD.
Keywords:
DC-DC converter
Duty cycle
Load voltage control
Total harmonic distortion
Simulink
Unity power factor controller
neural network algorithm
Variable loads
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Hussain Attia,
Department of Electrical, Electronics and Communication Engineering,
American University of Ras Al Khaimah,
Ras Al Khaimah, 10021, United Arab Emirates.
Email: hattia@aurak.ac.ae
1. INTRODUCTION
The passive components, such as inductors and capacitors are playing main roles in electrical
systems, this passive component usage increases the reactive power absorption, and consequently increases
the electrical losses due to that the power absorbing from grid does not consume in active load but transfer to
either electromagnetic field and save in the inductor or save as electrical charge in the capacitor.
The mismatch or the phase shift (value and direction) between the grid current waveform and grid voltage
waveform provides indication about the level of this losses. In other words, this level can be represented by
power factor (PF), which is the rate between the active power (consumed in resistive load) over the apparent
power (total power consumed in resistive load and absorbed by the reactive components). So, to reduce or
remove the losses in power, PF need to be in higher level which equals to 1. On the other side,
the semiconductor devices, such as different types of power transistors, and diodes which have been involved
in many power electronics conversion systems, and because of the nonlinear behaviour of these devices,
the grid current waveform shape has been distorted. This current distortion effects negatively the total
harmonics distortion (THD) of the grid current. Based on that, the researchers have been focused on
increasing the PF and enhancing the THD levels through different studies [1-13].
In study of [1], an automatic PF corrector has designed and proposed via microcontroller to reduce
the power losses and the cost as well. In [2, 3] power factor correctors have designed and implemented using

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Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 4145 - 4154
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microcontroller integrated circuits. In [2], the microcontroller worked on generating the pulse width
modulation pulses to drive the connected DC-DC converter towards increasing the level of power factor.
Whereas in [3], a high performance PF corrector has designed and implemented around the microcontroller
ATmega328. An effective power factor corrector for a Cuk DC-DC converter has presented in [4], the study
focused on PF correcting and improving the Total Harmonics Distortion level of a grid current which is
drawn to deliver power to a light emitting diode (LED) lamp.
In [5], a new PF controller has designed and simulated via PWM pulses generator based on variable
switching frequency. The controller worked on improving the power factor, controlling the load voltage, and
enhancing the THD of grid current. The behaviour of the PF corrector has analysed in [6] in terms of
performance effectiveness and THD enhancement by considering non-linear load.
Other studies proposed an intelligent algorithm for high priority PF corrector [7-13], Fuzzy logic
control (FLC) based algorithm have proposed in [7-9], whereas recently, Artificial neural network (ANN)
based algorithm have proposed in [10-13] for PF correcting. A single-phase PF corrector have presented via
two controllers in [7], the controllers are represented by a FLC controller and a predictive controller.
In [8], the performance of PF corrector for a boost DC-DC converter has analysed, the proposed PF corrector
included three individual controllers; FLC controller, hysteresis controller and proportional integral PI
controller. An analytical study has presented in [9] based on simulation results of a PF corrector via FLC.
The controller controlled the excitation current of the synchronous motor.
A comparative study in terms of power quality of a power factor corrector is reported in [10],
an ANN algorithm is designed in the controller which is integrated with a conventional proportional integral
controller. A real-time power factor correction controller has proposed in [11] through an ANN algorithm,
which received the instantaneous values of the active and reactive power absorbed by the load, the controller
turned ON or OFF a set of the capacitors in the system. A developed power factor correction controller has
presented in [12] to improve the performance of the power system. The study tested the PF corrector
effectiveness using a variable load through a fast response algorithm of clustering neural network to
automatically control the power factor. In [13], a novel line power factor corrector has presented using
a synchronous motor, the excitation current of the motor has controlled for PF increasing through an
intelligent controller based on ANN.
This study reports a new intelligent power factor corrector for a single-phase boost DC-DC
converter. This paper proposes a new design of an artificial neural network algorithm to predict the suitable
duty cycle to have the desired opposite phase shift which is needed for fully matching between grid current
and grid voltage, this matching guarantees unity power factor and reduces the distortion in the grid current
and enhances the THD of the grid current. The design aspects of the proposed PF correction controller are
shown in Section 2, whereas the presented system explanation and main block diagram are shown in Section 3.
The collected simulation results are analysed in Section 4. Concluding the outcomes of the presented study is
reported in Section 5.
2. DESIGN ASPECTS OF THE PROPOSED PF CONTROLLER
Based on the fact of that different loads among resistive, inductive, capacitive, and non-linear might
be connected to the grid through the electrical and/or power electronics systems [7]. Resistive loads are
consuming an active power from the grid. The inductive and capacitive loads are absorbing a reactive power
from the grid and do not consume the power but transfer it to an electromagnetic field through the inductor,
or save it in the capacitor as electrical charge. In other words, these reactive components are losing the power
by absorbing it from grid without consuming it, in addition, these electrical passive components are shifting
the grid current to leg or lead the grid voltage. On the other side, the non-linear semiconductor devices such
as MOSFETs transistors, IGBTs transistors, and diodes are distorting the grid current, consequently
increasing the Total Harmonics Distortion of the current [14, 15]. Power Factor is the indication of the rate
between the active power (P) to the apparent power (S) that should be in high range which is more than 0.9,
the apparent power S represents vector value includes real value of the active power (Watt) and imaginary
value of the reactive power (Volt∙Amp) as shown in (1), and explained in Figure 1. To have lower losses
system, unity power factor (PF) is necessary as shown in (2).
𝑆 = 𝑃 + 𝑗 𝑄 (1)
𝑃𝐹 =
𝑃
𝑆
= 1 (𝑤ℎ𝑒𝑛 𝑆 = 𝑃 𝑜𝑛𝑙𝑦, 𝑜𝑟 𝑤ℎ𝑒𝑛 𝑄 = 0) (2)

Int J Elec & Comp Eng ISSN: 2088-8708 
Artificial neural network based unity power factor corrector … (Hussain Attia)
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The grid voltage waveform is representing by (3), and the grid current waveform can be represented
by (4) due to the phase shift between the grid voltage and the grid current. The phase shift (value and
direction) between the two waveforms is depending on the value and the type of the impedance. In other
words, as shown in Figure 1, the phase shift between the grid voltage and grid current represents the same
phase shift of the equivalent impedance, and the value of the power factor PF can be calculated by (5).
𝑉𝑔𝑟𝑖𝑑 = 𝑉𝑚 sin(𝑤𝑡) (3)
𝐼𝑔𝑟𝑖𝑑 = 𝐼 𝑚 sin(𝑤𝑡 + Ө) (4)
Figure 1. Apparent power S contents active power P and reactive power Q
Where Vgrid, Igrid, Vm, Im, and Ө represent grid voltage, grid current, peak value of grid voltage, peak value of
the grid current, and phase shift between grid voltage and grid current waveforms, whereas w equals to 2πfo ,
and fo is the grid frequency, t is the time (sec).
𝑃𝐹 =
𝐴𝑐𝑡𝑖𝑣𝑒 𝑃𝑜𝑤𝑒𝑟
𝐴𝑝𝑝𝑒𝑎𝑟𝑒𝑛𝑡 𝑃𝑜𝑤𝑒𝑟
=
𝑃
𝑆
= 𝐶𝑜𝑠(Ө) (5)
Power factor PF can be one when having zero phase shift between the apparent power and active
power. In terms of converter, to have PF improvement, the converter drive pulses which are pulse width
modulation technique based pulses can be controlled. The width of the PWM pulses can be controlled based
on ANN predicting to the duty cycle which is required to compensate the phase shift between the grid voltage
and the grid current. Based on the fact of that the artificial neural network ANN can quickly predict
the suitable numerical value by considering the instantaneous numerical values of the input variables of
the related system’s parameters [16-18]. So, a new ANN algorithm has designed in this paper to predict
the suitable duty cycle to have 100 percent matching between the grid voltage and grid current that
consequently lead to have unity PF. Neural network can be designed and implemented by involving one
input layer, one hidden layer, and one output layer, whereas there is possibility to involve more than one
hidden layer. Neuron is the fundamental unit in the ANN, Figure 2 shows the neuron’s contents and
processing stages which are representing by input variables receiving stage, variables weighting stage, then
adder stage to have Z value as shown in (6). This value is manipulated by one of the three activation
functions as shown in (7-9).
𝑍 = ∑ 𝑊𝑛 𝑋 𝑛 + 𝐵 (𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 𝑊𝑒𝑖𝑔ℎ𝑡𝑖𝑛𝑔 & 𝐴𝑑𝑑𝑖𝑡𝑖𝑜𝑛)
𝑁
𝑛=1
(6)
𝑦𝑛 = 𝑓(𝑧) (𝐿𝑖𝑛𝑒𝑎𝑟 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛) (7)
𝑓(𝑧) =
1
1 + 𝑒𝑥𝑝−𝑧
(𝑆𝑖𝑔𝑚𝑜𝑖𝑑𝑎𝑙 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛) (8)

 ISSN: 2088-8708
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𝑓(𝑧) =
1+ 𝑒𝑥𝑝−2𝑧
1− 𝑒𝑥𝑝−2𝑧 (𝐻𝑦𝑝𝑒𝑟𝑏𝑜𝑙𝑖𝑐 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 (9)
The accuracy of the designed ANN algorithm can be evaluated by monitoring the value of the mean
square error (MSE) which reflects the result of error level between the target value and the predicted value as
explained in (10, 11).
𝑀𝑆𝐸 =
1
𝑞
∑ 𝑒(𝑘)2
𝑞
𝑘=1
(10)
𝑀𝑆𝐸 =
1
𝑞
∑[𝑡(𝑘) − 𝑎(𝑘)]2
𝑞
𝑘=1
(11)
Where q is the elements number of the input vector, e(k) is the error value, t(k) is the target value, and a(k) is
the output vector value which is predicted by the neural network algorithm [19].
Figure 2. The neuron representation
3. DESIGN OF THE PRESENTED ANNBASED PF CORRECTOR
The paper design mainly focuses on increasing the PF of the converter system to the aim of unity
power factor, and at the same time to enhance the THD level of the grid current. Figure 3 shows the main
diagram of the presented power factor corrector. The system blocks are divided to two groups, controlling
blocks and power electronics blocks. The controlling blocks are starting by a measuring block which receives
the instantaneous values of the grid voltage and grid current, calculates the phase shift (Ө) between the grid
voltage and current waveforms and calculates the power factor PF value using (3)-(5). The instantaneous
value of the phase shift enters the artificial neural network block, and at the same time, the ANN block
receives the absolute value of the grid voltage. The function of the designed ANN is to predict the initial
value of duty cycle D, the final value of duty cycle is found after considering the error signal between
the load current and the reference current and the error signal between the load voltage and the reference
voltage. The final value of the duty cycle D enforces the converter to have full matching between the grid
voltage and the grid current. The other meaning of full matching is the phase shift is zero and the power
factor (cos (Ө)) is one. The designed ANN includes three hidden layers in addition to one input layer and one
output layer as shown in Figure 4. Pulse Width Modulation (PWM) pulses generator generates the drive
pulses to the involved boost DC-DC converter [20-25]. The second group of blocks in the proposed system
involves the power electronics device which is the boost DC-DC converter, in addition to the electrical
resistive load. The converter is designed in this study through the design steps of [26], in which the converter
output load voltage (VO) is relate to the converter input voltage (Vin) by considering the Duty cycle (D) value (12).
The electrical load used in this study is a variable resistive load with three resistances of 160 Ω, 120 Ω, and 80 Ω.
𝑉𝑜 =
1
1 − 𝐷
𝑉𝑖𝑛 (12)

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Figure 3. The main block diagram of the proposed PF corrector
Figure 4. The proposed controlling neural network algorithm
4. PF CONTROLLER SIMULATION AND SIMULATION RESULTS
The proposed PF corrector is simulated via MATLAB/Simulink, the designed algorithm of
the neural network is simulated which includes input layer of two neurons, three hidden layers of ten neurons
each, and output layer of one neuron as shown in Figure 5, whereas the ANN accuracy is shown in Figure 6
and evaluated through the mean square error (MSE) of the algorithm. On the other hand, the converter
parameters setting is shown in Table 1, in which, the considered switching frequency is 12 kHz. The system
performance is evaluated, firstly via constant 60% duty cycle and variable resistive loads (160 Ω, 120 Ω,
80 Ω) during 3 seconds simulation time. The simulation time is divided equally into three times of one
second for each load value. Grid voltage of Vrms value 110 V / 60 Hz and grid current waveforms are shown
in Figure 7, it’s clearly noticeable the phase shift between grid voltage and grid current, and also
the distortion of the grid current which is increased during the increasing of the load current. The total
harmonics distortion (THD% = 44.57%) and the high levels of the low order harmonics of the grid current at
80 Ω load are also clear shown in Figure 8. The system behavior in terms of load voltage is shown in
Figure 9, the overshoot of load voltage at beginning of converter response is clearly monitored. At the same
time the load voltage level is varying with different values during load variation. The PF levels all are
monitored and recorded in Table 2 during the constant duty cycle, and at the controlling function later.
The system performance is evaluated and monitored through the proposed controller for same
resistive loads and same simulation time divisions. Grid voltage and grid current are shown in Figure 10,
the matching between the grid voltage and current is clearly noticeable and positively affects the PF
improving, and enhances the low order harmonics and THD of grid current as well as shown in Figure 11,
in which the THD level at load 80 Ω equals to (THD% = 7.68%). The merit of the proposed controller in terms
of controlling and stabilizing the load voltage at different loads is shown in Figure 12. Via the presented controller,
the overshoot in load voltage is completely removed, and the load voltage is stabilized during the load

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varying. Figure 13 shows system performance during 1 second simulation time, in first part of which
the system works with constant 60% duty cycle, and after 0.55 sec, the system works under the proposed PF
corrector role. It is very clear the positive effect of the proposed controller on the system performance in
terms of phase shift reduction between grid voltage and grid current, and in terms of shape of grid current
waveform. Table 2 shows all records of PF levels, load voltages, and THD for all loads, with and without
inserting the proposed PF corrector.
Figure 5. The simulated Feed-forward ANN algorithm
Figure 6. The performance of the simulated ANN algorithm

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Table 1. Designed parameters of the DC-DC converter
Parameter Name Parameter Value
Resistive Load 80 Ω, 120 Ω, 160 Ω,
Capacitor 1200 µF
Inductor 12 mH
Switching Frequency 12 kHz
Duty Cycle 0.5
Figure 7. Grid voltage and grid current waveforms with zoom in for each load 160 Ω,
120 Ω, and 80 Ω respectively at 60% constant duty cycle
Figure 8. Fast Fourier transform and harmonic spectrum content in grid current load 80 Ω
and at 60% constant duty cycle
Figure 9. Load voltage at load 160 Ω, 120 Ω, and 80 Ω respectively and at 60% constant duty cycle

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Figure 10. Grid voltage and grid current waveforms with zoom in for each load 160 Ω,
120 Ω, and 80 Ω respectively via the proposed PF controller
Figure 11. Fast Fourier transform and harmonic spectrum content in grid
current load 80 Ω via the proposed PF corrector
Figure 12. Load voltage at load 160 Ω, 120 Ω, and 80 Ω respectively and via proposed PF corrector

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Figure 13. The proposed PF corrector performance after 0.55 sec at load 80 Ω
Table 2. Simulation results of power factor, load voltage, and THD using different loads through constant
60% duty cycle and ANN controlled duty cycle
Parameters Variable Resistive
Load RL = 80 Ω
Variable Resistive
Load RL = 120 Ω
Variable Resistive
Load RL = 160 Ω
Power factor Without control
Fixed D = 0.6
0.8338 0.8194 0.8394
Controlled Duty Cycle D 0.9993 1 0.9994
Resistive load
Voltage (V)
Without control
Fixed D = 0.6
242.8 V 262.6 V 276.2 V
Controlled Duty Cycle D 275.4 V 276.1V 276.3 V
Total harmonics distortion
THD%
Without control
Fixed D = 0.6
44.57% 34.27% 36.33%
Controlled Duty Cycle D 7.68% 8.68% 11.34%
5. CONCLUSION
This paper presented a new power factor correction controller with load voltage controlling ability
for single phase boost DC-DC converter systems, the presented controller worked also on enhancing
the Total Harmonic Distortion level of the grid current. The controller considered the merits of the artificial
neural network algorithms in terms of fast and accurate response for predicting the numerical values of
the related parameters. This study designed, MATLAB simulated, and results analysed of a new PF
corrector, and the system performance is evaluated in terms of PF level, load voltage regulating, harmonics
spectrum, and THD level of grid current. The collected records indicate the effectiveness of the proposed
controller which guaranteed the unity power factor, and stabilized the load voltage with clearly noticeable
THD enhancing for all direct current loads 80 Ω, 120 Ω, 160 Ω. The high performance of the proposed
controller promises the successfulness of the future practical prototype.
ACKNOWLEDGEMENTS
The authors appreciate the financial support provided by school of engineering, American
University of Ras Al Khaimah – UAE, www.aurak.ac.ae/en/school-of-engineering/
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BIOGRAPHY OF AUTHOR
Hussain Attia earned his Ph.D. degree in power electronics from University of Malaya, Kuala
Lumpur, Malaysia, and M.Sc. degree in electronic engineering from the University of
Technology, Baghdad, Iraq. Hussain’s research interests include power electronics, AC & DC
Drives, PWM Inverters (single phase & three phases), harmonics reduction techniques, and
intelligent control.

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