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171 

 

 

 

Article 

An effective power quality enhancement system for 

integrated photovoltaic cells utilizing cascaded 

ANFIS in a unified power quality conditioner   
Saritha Kandukuri1*, Ramesh Guguloth2, A. Sivakumar 3, I. Shivasankkar4, Ananthan Nagarajan5,  

N. Janaki6 

1Department of Electrical and Electronics Engineering, Eklavya University, Damoh, Madhya Pradesh, 470661 
2Department of Electrical and Electronics Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana-

501301, India 
3Department of Electrical and Electronics Engineering, Panimalar Engineering College, Nazerthpattai, Poonamalle, Chennai, 600 

123, India 
4Department of Electrical and Electronics Engineering, Sri Manakula Vinayagar Engineering College, Puducherry, 605107, India 
5Department of Electrical and Electronics Engineering, Vel Tech Multi Tech, Dr. Rangarajan Dr. Sakunthala Engineering College, 

Chennai-62, India 
6Department of Electrical and Electronics Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, 

India 

A R T I C L E   I N F O 
 

Article history: 
Received 22 April 2025  
Received in revised form 
04 June 2025 
Accepted 14 June 2025 
 
Keywords:  
Unified Power Quality Conditioner,  
Artificial Neural Network controller, 
PV system, 
Coupled quadratic SEPIC converter 
Cascaded ANFIS-MPPT 
 
*Corresponding author 
Email address: 
mugatha.saritha@gmail.com 
 
 
DOI: 10.55670/fpll.futech.4.3.16 

A B S T R A C T 
 

The arrival of power electronic devices for the control of loads has an effect on 
the Power Quality (PQ) at the utility grid’s distribution side. Meanwhile, PQ 
problems cause malfunctioning equipment, lost production time, loss of money 
for industry, inconvenience, and possible damage to household electrical 
appliances. Thus, the requirement for increased system efficiency is essential. 
Hence, this study proposes the control of a Unified Power Quality Conditioner 
(UPQC) in conjunction with a Photovoltaic (PV) system. Shunt and series 
converters attached back-to-back via a shared DC-link make up the PV-UPQC 
system. Subsequently, the Artificial Neural Network (ANN) controller reduces PQ 
problems and simplifies the control complexity. A Coupled quadratic Single 
Ended Primary Inductor Converter (SEPIC) connects the PV system to UPQC, and 
the Cascaded Adaptive Neuro Fuzzy Inference System- Maximum Power Point 
Tracking (ANFIS-MPPT) technique enables the optimization of power extraction 
from PV sources. The developed approach is implemented using the 
MATLAB/Simulink platform, and its performance is evaluated for Total 
Harmonic Distortion (THD), sag, and swell. The results show that the control 
maintains THD within the B-phase THD of 3.97% and R and Y phase THDs of 
4.82% and 4.86%, and also obtained a voltage gain ratio of 1:15; the output levels 
increase substantially with reduced voltage stresses on the switching devices. 

1. Introduction 

The usage of non-linear loads and unbalanced loads has 
increased in the modern era due to the expansion of the 
distribution system and the expansion of industry. PQ 
problems get inferior during the non-linear load 
enhancements, and the s distribution grid's structure 
becomes more intricate [1]. This resulted in issues with PQ, 
such as distortion and imbalance in the current, sag/swell, 
and the production of harmonics and imbalance in the 
system’s supply voltage. Voltage quality issues, in particular, 
have the potential to impair the regular functioning of 

sensitive loads that are heavily linked to the distribution grid, 
resulting in financial losses and other consequences [2]. The 
essential industrial load is affected by grid voltage 
disruptions, which result in frequent tripping. In modern 
years, a number of methods and tools have been established 
to address PQ problems in distribution networks. Flexible AC 
Transmission System (FACTS) devices are appealing 
instruments for improving reactive power control and 
reliability in transmission systems. These gadgets react 
swiftly to any disruptions and provide more system flexibility 
[3].  

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.4.3.16 

 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

August 2025| Volume 04 | Issue 03 | Pages 171-181 

mailto:mugatha.saritha@gmail.com
https://doi.org/10.55670/fpll.futech.4.3.16
https://fupubco.com/futech


S. Kandukuri et al. /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 171-181                                                                    

 

172 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The need for Passive Power Filters (PPF), Active Power 

Filters (APF), and hybrid power filters has increased due to 
these limitations, which include fixed compensation, massive 
size, difficulty in adjusting dependency filter settings, and 
resonance with source impedance [4-5]. These filters, which 
are often connected in parallel with the load, are developed to 
remove current harmonics and adjust for reactive power in 
the power system. Despite being more affordable and widely 
accessible, these filters must be retuned to a specific 
harmonic in order to produce the desired effect, which can 
lead to overvoltage situations when power demand is lower 
[6]. The STATCOM is a power electronics device that works by 
injecting reactive current into the power network’s point of 
common coupling. The primary benefit of the STATCOM is 
that it does not rely on the Point of Common Coupling’s (PCC) 
voltage level, hence the compensating current is not reduced 
as the voltage drops. Nevertheless, it has harmonics, high 
initial costs, and limited steady-state operating modes [7].   

By reducing major PQ problems, including sags/swell, 
harmonics, flickers, and interruptions, the DVR protects the 
load from failure or tripping; nonetheless, they are ineffective 
at balancing large-scale voltage sags [8]. Through the 
regulation of voltage, power factor, and harmonics, the Static 
Var Compensator (SVC) enhances PQ. However, in order to 
compensate for surge impedance, SVCs need extra equipment 
[9]. The PCC provides reactive power to the Distribution 
Static Compensator (DSTATCOM), which regulates voltage. 
Nevertheless, its use is restricted by the issue of reactive 
power injection and power losses [10].   

Therefore, this research proposes a UPQC for enhancing 
the PQ. The UPQC protects the vital loads connected to the 
distribution system by addressing issues such as neutral and 
negative sequence currents, harmonic isolation, flow of 
reactive power at harmonic distortions, voltage disturbances, 
and harmonic and fundamental frequencies. A PV system is 
exploited in a UPQC system to leverage the clean, renewable 
energy developed by solar panels to alleviate PQ issues, which 
have the highest annual growth curve among the available 
renewable sources because of their easy installation and 
limitless supply capacity. However, many PQ problems are 
also brought on by the extensive integration of PV into the 

power grid [11-12]. Thus, the design of solar PV integrated 
UPQC has many advantages, including enhancing grid PQ and 
shielding vital loads from grid-side disruptions. Furthermore, 
the current approaches ignore the problem of voltage 
instability brought on by PV system intermittency in favour of 
concentrating solely on the compensating capability and 
design of UPQC [13]. The conventional converters like Boost 
[14], Cuk [15], and SEPIC [16] are employed for boosting the 
voltage of the PV system. However, these conventional 
converters have a complex structure, high ripple current, and 
lower efficiency. Therefore, this research develops a coupled 
quadratic SEPIC converter in the PV-based UPQC system. To 
enhance the efficacy of the PV system, the MPPT approach is 
utilized that tracks the highest power from the PV system 
[17]. The conventional MPPT algorithms like ANN [18], Fuzzy 
logic [19], and ANFIS [20] have oscillations, undesirable 
performance, and excessive complexity. Also, the Perturb and 
Observe (P&O) MPPT [21] method has limitations in terms of 
oscillations around the Maximum Power Point (MPP), causing 
a loss in power, its inability to track rapidly changing 
irradiance conditions, and a lower efficiency with dynamic 
conditions. Similarly, Incremental Conductance MPPT [22] 
has a high computation burden, slower tracking with rapidly 
varying irradiance, and it is also sensitive to noise that causes 
small oscillations around the MPP. As a consequence, this 
paper develops a cascaded ANFIS MPPT algorithm for 
tracking the peak power from the PV system.  

1.1 Problem statement 
PQ issues such as voltage sags, swells, and harmonic 

distortion are a growing problem in modern power systems 
with increasing nonlinear loads and distributed energy 
resources. Poor PQ causes equipment failure, production 
shutdowns, and financial losses. While UPQC is commonly 
employed to mitigate these issues, its implementation leads 
to complex control requirements and wasted energy 
extraction when combined with renewable sources. This 
research presents a novel PV-UPQC system, integrated using 
a Coupled Quadratic SEPIC converter, an ANN controller, and 
a Cascaded ANFIS-MPPT design, making it possible to 
improve efficiency and PQ. The key contributions are: 
• Integrating the UPQC for mitigating the PQ issues like 

voltage sag and swell. 
• Implementing the Coupled quadratic SEPIC converter for 

enhancing the low voltage of the PV system to a higher 
voltage. 

• The cascaded ANFIS MPPT is exploited for tracking 
maximum power from the PV system, which effectively 
enhances the PV system’s efficacy.    

• ANN controller approach to minimize control 
generalization and expand PQ mitigation operations. 

2. Proposed methodology 

The developed PV-based UPQC system is indicated in 
Figure 1. The three-phase AC supply is connected with a 
linear/nonlinear load through a UPQC, which has a series and 
shunt converter with a DC link capacitor. The series converter 
is exploited for compensating voltage distortions and 
maintaining voltage stability at the load end. It ensures a 
seamless power supply for linear or nonlinear loads. Then, 
the shunt converter is exploited to mitigate current 
distortions. It ensures that the current drawn by the load 
remains sinusoidal and balanced, even under nonlinear 
conditions. 

Abbreviations 
APF  Active Power Filters 
ANFIS-MPPT Adaptive Neuro Fuzzy 

Inference System- Maximum Power  
Point Tracking 

ANN  Artificial Neural Network 
DSTATCOM Distribution Static Compensator 
FACTS  Flexible AC Transmission System 
PCC  Point of Common Coupling 
PLL  Phase-Locked Loop 
PPF  Passive Power Filters 
PQ  Power Quality 
PV  Photovoltaic 
PWM  Pulse Width Modulation 
RMSE  Root Mean Square Error 
SVC  Static Var Compensator 
SEPIC  Single Ended Primary Inductor  

Converter 
THD  Total Harmonic Distortion 
UPQC  Unified Power Quality Conditioner 
 



S. Kandukuri et al. /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 171-181                                                                    

 

173 

 

 
Figure 1. Proposed block diagram  

Then, the Pulse Width Modulation (PWM) generator 
generates PWM pulses for better functioning of the series and 
shunt converters. To give the power supply to the DC link, the 
PV system is exploited. Because of the environmental 
changes, low voltage is generated from the PV system that is 
improved by utilizing the coupled quadratic SEPIC converter, 
and its output is supplied to the DC link capacitor. For tracking 
the peak power from the PV system, the cascaded ANFIS 
MPPT controller is utilized.  Consequently, the ANN controller 
is exploited to control the function of UPQC, and the PWM 
generator produces necessary pulses for UPQC. Accordingly, 
the PQ of the overall system is enhanced with reduced THD. 

2.1 UPQC 
The UPQC is a power conditioning system with shunt and 

series compensation capabilities that effectively enhances the 
overall PQ of the system. Figure 2 shows the UPQC’s structural 
diagram. An unbalanced three-phase system’s source grid 
voltage 𝑉𝑔𝑟𝑖𝑑(𝑡) has fundamental and harmonics in its zero, 

negative, and positive sequence components. Equation (1) 
provides the system voltage for the equivalent circuit. 

𝑉𝑔𝑟𝑖𝑑(𝑡) = 𝑉𝑔𝑟𝑖𝑑+(𝑡) + 𝑉𝑔𝑟𝑖𝑑−(𝑡) + 𝑉𝑔𝑟𝑖𝑑0(𝑡) + ∑𝑉𝑠ℎ         (1) 

Where 𝑉𝑠ℎ  is the shunt converter’s voltage and 
𝑉𝑔𝑟𝑖𝑑−(𝑡), 𝑉𝑔𝑟𝑖𝑑+(𝑡) and 𝑉𝑔𝑟𝑖𝑑0(𝑡) are the negative, positive 

and zero sequence components. Equation (2) provides the 
inserted voltage of the series converter.    

𝑉𝑠𝑒_𝑐𝑜𝑚𝑝(𝑡) = 𝑉𝐿𝑜𝑎𝑑(𝑡) − 𝑉𝑔𝑟𝑖𝑑(𝑡)           (2) 

Where 𝑉𝑠𝑒_𝑐𝑜𝑚𝑝(𝑡) is the voltage of the series compensator, 

𝑉𝑔𝑟𝑖𝑑(𝑡)is the source voltage, and 𝑉𝐿𝑜𝑎𝑑(𝑡)is the load voltage.  

The current of shunt compensator is, 

𝐼𝑠ℎ_𝑐𝑜𝑚𝑝(𝑡) = 𝐼𝐿𝑜𝑎𝑑(𝑡) − 𝐼𝑔𝑟𝑖𝑑 (𝑡)            (3) 

  

 

 

 

 

Figure 2. Structure of UPQC system 

Where 𝐼𝑠ℎ−𝑐𝑜𝑚𝑝(𝑡) is the compensating current, 𝐼𝐿𝑜𝑎𝑑(𝑡) is 

the current at the load and 𝐼𝑔𝑟𝑖𝑑 (𝑡) is the current passing over 

the grid. The current is injected into the grid by the shunt 
converter.  

𝐼𝐿𝑜𝑎𝑑(𝑡) = 𝐼𝐿𝑜𝑎𝑑+(𝑡) + 𝐼𝐿𝑜𝑎𝑑−(𝑡) + 𝐼𝐿𝑜𝑎𝑑0(𝑡) + ∑ 𝐼𝑠ℎ−𝑐𝑜𝑚𝑝(𝑡)

                       (4) 

Equation (4) provides the distorted load current. Where the 
load current’s positive sequence is denoted by 𝐼𝐿𝑜𝑎𝑑+(𝑡), its 
negative sequence by 𝐼𝐿𝑜𝑎𝑑−(𝑡), and its zero-sequence 
component by 𝐼𝐿𝑜𝑎𝑑0(𝑡). The current passing through the 
shunt compensator is denoted by 𝐼𝑠ℎ−𝑐𝑜𝑚𝑝(𝑡). This system is 

a three-phase system with a non-linear inductive load.  Figure 
3 depicts a circuit of the UPQC system. 

2.1.1 Series converter 
  By lowering voltage-related disturbances, including 
voltage swell and sag, the series converter enhances PQ. The 
series converter preserves the voltage control, as seen in 
Figure 4.  



S. Kandukuri et al. /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 171-181                                                                    

 

174 

 

 
Figure 3. Equivalent circuit of UPQC  

 
Figure 4. Control structure for a series converter  

The series converter is in charge of using a series 
injection transformer to inject the voltage at PCC. The DC-link 
element is charged concurrently with the AC quantity being 
converted to DC by the series converter. The actual power 
exchange is also made possible by the series converter. The 
voltage sensors detect the basic and distorted voltage 
components at the PCC. The input voltage’s peak value is 
divided by the sensed distorted voltage.  

𝑉𝑝𝑒𝑎𝑘 = √
2

3
(𝑉𝑎_𝑠 + 𝑉𝑏_𝑠 + 𝑉𝑐_𝑠)                       (5) 

The three-phase frequency is synchronized using the Phase-
Locked Loop (PLL) circuit. In the PLL circuit, the distorted 
voltage is separated by the peak voltage. Equation (6) 
provides the phase angle difference,  

𝑉𝑃𝐿𝐿_𝑎 = 𝑠𝑖𝑛(𝜔𝑡)           (6) 

𝑉𝑃𝐿𝐿_𝑏 = 𝑠𝑖𝑛 (𝜔𝑡 −
2𝜋

3
)                                                  (7) 

𝑉𝑃𝐿𝐿_𝑐 = 𝑠𝑖𝑛 (𝜔𝑡 +
2𝜋

3
)                                       (8) 

𝑉𝐿𝑜𝑎𝑑_𝑎𝑏𝑐
∗ = 𝑉𝑝𝑒𝑎𝑘 ∗ 𝑉𝑃𝐿𝐿_𝑎𝑏𝑐                                        (9) 

An error signal is developed by comparing the generated 
reference signal with the load signal. The series converter’s 
gate pulse is developed by feeding the resultant error signal 
into a PWM signal generator.     

2.1.2 Shunt converter 
In addition to compensating for current harmonics, 

the shunt converter also compensates for reactive power. The 
actual power required by the series converter at the DC link 
capacitor is provided by the shunt converter. The shunt 
converter transforms the series converter’s DC-link power 
demand back into an AC quantity. The shunt converter uses 
the shunt inductor to compensate for the power consumption 

on the load side. The shunt converter employs the 𝑝 − 𝑞 
theory as its control system, in which Clark’s Transformation 
transforms 𝑎 − 𝑏 − 𝑐 coordinates into 𝛼 − 𝛽 coordinates, as 
represented in Figure 5. Equations (8) and (9) provide the 
electrical quantities in 𝛼 − 𝛽 coordinates. The reactive and 
real power based on the current and voltage at any given time 
are: 

(
𝑣𝛼_𝑙𝑜𝑎𝑑
𝑣𝛽_𝑙𝑜𝑎𝑑

) = √
2

3
(

1 −
1

2
−
1

2

0 √
3

2
−√

3

2

)(

𝑣𝑎_𝑙𝑜𝑎𝑑
𝑣𝑏_𝑙𝑜𝑎𝑑
𝑣𝑐_𝑙𝑜𝑎𝑑

)         (10) 

(
𝑖𝛼_𝑙𝑜𝑎𝑑
𝑖𝛽_𝑙𝑜𝑎𝑑

) = √
2

3
(

1 −
1

2
−
1

2

0 √
3

2
−√

3

2

)(

𝑖𝑎_𝑙𝑜𝑎𝑑
𝑖𝑏_𝑙𝑜𝑎𝑑
𝑖𝑐_𝑙𝑜𝑎𝑑

)        (11) 

 
Figure 5. Control structure for the Shunt converter   

𝑝𝑙𝑜𝑎𝑑(𝑡) = 𝑣𝛼_𝑙𝑜𝑎𝑑(𝑡)𝑖𝛼_𝑙𝑜𝑎𝑑(𝑡) + 𝑣𝛽_𝑙𝑜𝑎𝑑(𝑡)𝑖𝛽_𝑙𝑜𝑎𝑑(𝑡)       (12) 

𝑞𝑙𝑜𝑎𝑑(𝑡) = −𝑣𝛼_𝑙𝑜𝑎𝑑(𝑡)𝑖𝛼_𝑙𝑜𝑎𝑑(𝑡) + 𝑣𝛽_𝑙𝑜𝑎𝑑(𝑡)𝑖𝛽_𝑙𝑜𝑎𝑑(𝑡)      (13) 

Equations (12) and (13), which relate to real and reactive 
power,  
𝑝𝑙𝑜𝑎𝑑 = 𝑝𝑎𝑐_𝑙𝑜𝑎𝑑̃ +𝑝𝑑𝑐_𝑙𝑜𝑎𝑑̅̅ ̅̅ ̅̅ ̅̅ ̅̅                            (14) 

𝑞𝑙𝑜𝑎𝑑 = 𝑞𝑎𝑐_𝑙𝑜𝑎𝑑̃ +𝑞𝑑𝑐_𝑙𝑜𝑎𝑑̅̅ ̅̅ ̅̅ ̅̅ ̅̅          (15) 

(

𝑖𝑎_𝑙𝑜𝑎𝑑
∗

𝑖𝑏_𝑙𝑜𝑎𝑑
∗

𝑖𝑐_𝑙𝑜𝑎𝑑
∗

) = √
2

3

(

 
 

1

√2
1 0

1

√2
−
1

2

√3

2

1

√2
−
1

2
−
√3

2 )

 
 
(

−𝑖𝑜_𝑙𝑜𝑎𝑑
𝑖𝛼_𝑙𝑜𝑎𝑑
∗

𝑖𝛽−𝑙𝑜𝑎𝑑
∗

)     (16) 

The constant DC link voltage is the responsibility of the shunt 
converter. The phase angle δ decides the variation of reactive 
and real power control. The shunt voltage source converter 
receives the gating pulses from the PWM generator. The 
function of UPQC is managed with the aid of an ANN 
controller.          

2.2 ANN controller  
The ANN controller’s response needs to be precise and 

quick for UPQC compensation. The ability of the ANN 
controller to learn, evaluate the mean square error, and 
forecast the uncertainty is needed to reduce the input-output 
disparity. Furthermore, the ANN controller trains the shunt 
and series compensators using the same method. This 
requires the controller to react quickly and accurately in 
order to correct for UPQC. In addition to processing the 
reference signal efficiently, the ANN controller demonstrates 
quick and accurate identification of a perturbed signal. Figure 
6 illustrates the structure of the ANN controller. 

 



S. Kandukuri et al. /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 171-181                                                                    

 

175 

 

 

Figure 6. ANN controller 

The ANN-based controller reacts quickly and 
dynamically under a wide range of operating conditions. All 
of an ANN’s inputs are received by the input layer, after which 
they are processed and stored in the hidden layer. Prior to 
further processing in the hidden layer, the input weights are 
multiplied by the bias. Following the completion of specific 
computations, the results are processed and transmitted to 
the output layer. ANNs handle data concurrently, which leads 
to quicker processing speeds than traditional systems. ANN 
generates reference currents and voltages by combining 
different learning architectures and principles. This design 
diagnoses the mean square error and makes both forward 
and backward weight adjustments until the intended output 
is attained, and the error is removed if the needed output is 
not produced. The ANN controller manages both the series 
and shunt converters in the PV-UPQC system.  Particularly, 
the ANN controller creates the reference signals necessary for 
both converters to successfully adjust for voltage and current 
deviations.  For the series converter, the ANN aids in adjusting 
the input voltage to reduce sags, swells, and harmonics, thus 
stabilising the load-side voltage.  The ANN allows the delivery 
of compensatory currents into the shunt converter, thereby 
eliminating current harmonics and maintaining a balanced 
sinusoidal supply.  This dual-control feature improves the 
general efficiency of the UPQC in handling PQ issues. To give 
the supply to UPQC, the PV system is equipped with a DC-DC 
converter.   

2.3 PV System 
The PV system is made up of several PV cells coupled in 

parallel and series to produce the necessary output voltage 
and current. Figure 7 displays the circuit of the PV system. The 
solar temperature and intensity decide the supplied power of 
the PV system. The expression (1) is the output current 
generated by the solar cell:  

𝐼 = 𝐼𝑃ℎ − 𝐼𝐷 − 𝐼𝑠ℎ                                                             (17) 

Where 𝐼 stands for the PV cell’s output current, 𝐼𝑃ℎ is photo-
generated current, 𝐼𝐷 is the current of the diode and 𝐼𝑠ℎ is the 
shunt current. The current that is redirected through the 
diode is described using the Shockley diode equation as:   

𝐼𝐷 = 𝐼𝑜 (𝑒𝑥𝑝 [
𝑞(𝑉+𝐼𝑅𝑠)

𝑚𝑘𝑇𝑐
] − 1)                        (18)

   
The current in a PV cell is: 

𝑰 = 𝑰𝑷𝒉 − 𝑰𝒐 (𝒆𝒙𝒑 [
𝒒(𝑽+𝑰𝑹𝒔)

𝒎𝒌𝑻𝒄
] − 𝟏) − (

(𝑽 + 𝑰𝑹𝒔)
𝑹𝒔𝒉
⁄ )      (19) 

Where 𝑇𝑐 is the absolute temperature, 𝑅𝑠 is the series 
resistance, 𝑅𝑠ℎ is shunt resistance, 𝐼𝑜 is diode saturation 
current, 𝑞 is elementary charge, 𝐾 is the Boltzmann constant, 
𝑚 is quality factor of diode and 𝑉 is the output voltage. Here, 
the low voltage of PV system is enhanced by a coupled 
quadratic SEPIC converter.   
 

 
Figure 7. Circuit of the PV system  

2.4 Coupled quadratic SEPIC converter 
The coupled quadratic SEPIC converter transforms low 

and intermittent input voltage from the PV system to a higher 
voltage. Figure 8 reveals the coupled quadratic SEPIC 
converter. The following presumptions are taken into 
consideration in order to summarize the converter's 
principle: all of the components are ideal, the resistance of the 
capacitors and inductors is minimal; the ON resistance of the 
𝑆, the diodes and parasitic capacitances’ voltage drop are all 
very small. The developed converter is operated in 3 modes, 
as shown in Figure 9 and Figure 10, which represent the 
functional waveform of the developed converter.   

 

  
Figure 8. Coupled quadratic SEPIC converter 

 

 
Figure 9. Stages of the developed converter  

Mode I  
The diode 𝐷1 and switch 𝑆 are conducting in this state. 
Capacitors 𝐶3and 𝐶𝑜are reverse biases 𝐷2 and𝐷𝑜, which are 
not conducting. With the current route 𝑉𝑑𝑐 − 𝐿1 − 𝐷1 − 𝑆 −
𝑉𝑑𝑐 , the input source (𝑉𝑑𝑐)energizes inductor𝐿1. Through the 
current path, windings 𝑁1and 𝑁2become magnetized as 𝑁2 −
𝑁1 − 𝐶2 − 𝑆 − 𝐶3 − 𝑁2. The output capacitor𝐶𝑜, which is 
separated from the DC source, powers the resistive load. The 
current of 𝐿1 and 𝐿𝑀increases from 𝑡0 𝑡𝑜 𝑡1.  

Mode II 
When the power switch 𝑆 is turned off and diode 𝐷1 is 
reverse-biased by capacitor𝐶2, in operating mode II. 𝐿1 uses 



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176 

 

𝑉𝑑𝑐 − 𝐿1 − 𝐶1 − 𝑉𝑑𝑐 to discharge the energy it has stored in𝐶1. 
Through the 𝐶3 −𝑁2 − 𝑁1 − 𝑉𝑂 − 𝐶3route, the energy saved 
in the coupled-inductors is released to the 𝐶3 and load. The 
current of 𝐿1and 𝐿𝑀 reduces from 𝑡1 to 𝑡2.   

Mode III  
In mode III, 𝐷𝑜is revered as biased while the power switch is 
off as a consequence of the leaking inductance effect. The 
consequence of turning off the diode 𝐷𝑜 is disregarded in 
converter operation by using the appropriate magnetizing 
inductance, high coupling coefficient, and low leakage 
inductance. The voltage relation among windings 𝑁1and 𝑁2 is:    

𝑛 =
𝑉𝑁1

𝑉𝑁2
                                      (20) 

By applying KVL in state 1, 

𝑉𝐿1 = 𝑉𝑑𝑐                      (21)
  

𝑉𝐿𝑚 =
𝑉𝐶3−𝑉𝐶2

𝑛−1
                                           (22) 

For mode 2, 

𝑉𝐿1 = 𝑉𝑑𝑐 − 𝑉𝐶1                                 (23) 

 𝑉𝐿𝑚 = −
𝑉𝐶2

𝑛
                                (24) 

 

Figure 10. Functional waveform of the developed converter  

By utilizing the volt-second balance law for the inductors 
and magnetizing 𝐿𝑀, 

∫ 𝑉𝐿1𝑑𝑡 + ∫ 𝑉𝐿1𝑑𝑡 = 0
𝑇𝑆
𝐷𝑇𝑆

𝐷𝑇𝑆
0

         (25) 

∫ 𝑉𝐿𝑚 𝑑𝑡 + ∫ 𝑉𝐿𝑚 𝑑𝑡 = 0
𝑇𝑆
𝐷𝑇𝑆

𝐷𝑇𝑆
0

                 (26)

  

Where 𝑇𝑆 and 𝐷 denote the switching period and duty cycle of 
the proposed converter.  

𝑉𝐶1 =
1

1−𝐷
𝑉𝑑𝑐                                  (27) 

 𝑉𝐶2 =
𝑛𝐷

(1−𝐷)2(𝑛−1)
𝑉𝑑𝑐                            (28) 

𝑉𝐶3 =
𝑛−1+𝐷

(1−𝐷)2(𝑛−1)
𝑉𝑑𝑐                 (29) 

The output dc voltage is: 

𝑉𝑜 =
𝑛−1+𝑛𝐷

(1−𝐷)2(𝑛−1)
𝑉𝑑𝑐            (30) 

The voltage gain is: 

𝐺 =
𝑉𝑜

𝑉𝑑𝑐
=

𝑛−1+𝑛𝐷

(1−𝐷)2(𝑛−1)
                   (31) 

The cascaded ANFIS MPPT controller is exploited for tracking 
the peak power from the PV system.   

2.5 Cascaded ANFIS MPPT controller  
By continuously modifying the operational parameters, 

the proposed work uses a cascaded ANFIS MPPT controller to 
optimize the power output from the PV system. Figure 11 
shows a flow chart of the developed controller. Reference 
voltage and current are produced by the ANFIS controller 
based on the PV system’s operating state at the time. 
Important parameters, including temperature, pressure, and 
the PV system’s output current and voltage, are used as 
inputs. The membership function fuzzifies the input variables, 
which are in charge of converting clear input into fuzzy sets 
so that the ANFIS manages the inherent uncertainty in the 
behavior of PV systems. Finally, a set of fuzzy rules is 
developed according to the past data. The relationship 
between input variables and output is defined by these rules. 
To make sure the PV system runs at its MPP, the secondary 
ANFIS controller modifies the developed converter’s duty 
cycle. The secondary ANFIS receives real-time voltage and 
current measurements as well as reference values produced 
by the primary ANFIS controller. The secondary controller 
controls the duty cycle changes of the converter using a rule-
based and fuzzification.    
 

 

Figure 11. Flowchart of cascaded ANFIS-MPPT controller  



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177 

 

2.5.1 Pair selection module  
By choosing the optimal input variable pairings, the 

goal is to increase the ANFIS model’s accuracy. Each pair of 
input variables is assessed according to its capacity to reduce 
the Root Mean Square Error (RMSE) among the actual and 
expected outputs in a sequential feature selection procedure. 
An ANFIS model with two inputs is trained and evaluated for 
every pair. The pairing with the lowest RMSE is chosen.    

2.5.2 Training module  
  Each data pair’s RMSE is determined by comparing the 

predicted and actual outputs. Iterative training is applied to 
the cascaded ANFIS model till the RMSE is less than a 
predetermined goal error. The accuracy of the model is 
improved by using the outputs from each iteration as inputs 
for the next one. Assume that the four input variables 
𝑍1, 𝑍2, 𝑍3and 𝑍4. The defined optimization problem is as 
follows.     

𝑖𝑛𝑝𝑢𝑡 = {𝑍1, 𝑍2, 𝑍3, 𝑍4}          (32) 

𝑖𝑛𝑝𝑢𝑡𝑝𝑎𝑖𝑟𝑠 = {𝑍1, 𝑍3}, {𝑍2, 𝑍1}, {𝑍3, 𝑍4}, {𝑍4, 𝑍1}      (33) 

The two outputs are the result of 𝑅𝑀𝑆𝐸𝑖 and predicted output 
𝑌𝑖 . 

𝑅𝑀𝑆𝐸 = √(𝐴 − 𝑃)2̅̅ ̅̅ ̅̅ ̅̅ ̅̅ ̅̅                      (34) 

 𝑅𝑀𝑆𝐸𝐴,𝑃 = [∑
(𝑂𝐴𝑖−𝑂𝑝𝑖)

2

𝑁

𝑁
𝑖=1 ]

1
2⁄

                  (35) 

𝑓 =
𝜔1

𝜔1+𝜔2
𝑓1 +

𝜔2

𝜔1+𝜔2
𝑓2 +

𝜔3

𝜔2+𝜔3
𝑓3 +

𝜔4

𝜔3+𝜔4
𝑓4     (36) 

The predicted and actual results are denoted as 𝑃 𝑎𝑛𝑑 𝐴 
while 𝑁 is the size and sample. The obtained outputs from 𝑌 
and RMSE at the end of the first iteration. After comparing the 
RMSE and goal error, the subsequent iteration is selected 
appropriately. The distinctive aspect of this approach is that 
the outcomes from iterations 𝑌1, 𝑌2, 𝑌3and 𝑌4are exploited as 
inputs for later iterations. To extract the peak power from the 
PV panel, the same process is used. The trained ANFIS 
modules continuously monitor the PV system variables while 
it is operating. Based on inputs, these modules forecast the 
required current and voltage, which is utilized to optimize the 
PV power output. The controller modifies the operating 
conditions to keep the system running at maximum efficiency.       

3. Results and discussion 

This section discusses the outcomes of the PV-based 
UPQC system for voltage swell and sag conditions. The 
developed research is executed in the MATLAB/Simulink tool, 
and a performance comparison is included to reveal the 
efficacy of the developed research. Table 1 depicts the 
parameter values of the proposed research.   

Figure 12 reveals the waveform of the AC source. The 
voltage of the AC source is 400 V in the starting period, and it 
is reduced to 280 V. Then, it changed back to 400 V (voltage 
sag is 120 V). Likewise, the current of an AC source does not 
maintain a stable value and experiences continuous 
variations. Both the voltage waveform and the current 
waveform from the AC source are continuously changing 
throughout the analysed time frame. Figure 13 represents the 
waveform of the developed converter. The input voltage of 
the developed converter is maintained at 72 V in the entire 
system. In the initial stage, the input current is varied and 
then sustained at 2500 A throughout the system. The output 
voltage of the converter is gradually raised and settled at 740 

V. Likewise, the output current is randomly changed and 
maintains a value of 12A.   

Table 1. Specification of parameters 

Parameter Specification 
AC Source 

Load Resistance 100Ω 
Load Inductance 10𝑚𝐻 

PV system 
Total Power 10K W 

Voltage (Open circuit) 22.6 𝑉 
Current (Short circuit) 8.95 A 

Maximum Peak Current 8.35 𝐴 

Number of panels in series connection 2 
Number of panels in the shunt 

connection 
17 

Coupled quadratic SEPIC converter 

𝐿1 4.7 𝑚𝐻 
𝐶1, 𝐶2 𝑎𝑛𝑑𝐶3 22 𝜇𝐹 

𝐶0 2200 𝜇𝐹 
Switching Frequency 10 𝑘𝐻𝑧 

 

Case 1: Voltage sag condition 

 
Figure 12. Waveform of the AC source  

 

   
Figure 13. Waveform of the developed converter  

 

 



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178 

 

Figure 14 shows the output voltage waveform of the 
Coupled Quadratic SEPIC Converter controlled by a Cascaded 
ANFIS MPPT Controller. The output voltage stabilizes at 
320 V, suggesting a fast response and proper regulation of 
voltage at the output by the controller. 

The waveform of UPQC for the voltage sag condition is 
represented in Figure 15. The 3𝜙 reference voltage for the 
series converter is randomly changed, and it increased to 80 
V with small fluctuations. Then, the 3𝜙 reference current is 
initially altered and is enhanced to a value of 40 A. 
Consequently, the power factor improved during the time 
frame that considering, stabilizing at 1, indicating the voltage 
and current are in sync. The waveform of the load is displayed 
in Figure 16. The constant voltage of 450 V and stable load 
current of 35 A is sustained in the entire system stability 
throughout the testing. Thus, the developed PV-UPQC is 
effective in increasing the PQ on the source and load sides. 

 

Figure 14. Waveform of developed converter output voltage using 
cascaded ANFIS MPPT controller 

 

 

Figure 15. Waveform of UPQC  

       
Figure 16. Waveform of load   

 

Case 2: Voltage swell condition 
Figure 17 illustrates the waveform of the AC source under a 
voltage swell condition. The source voltage is 400 V, and it 
increased to 450 V. Finally, it sustained at 400V, and it is 
influenced by the PQ issue with a voltage swell of 50 V.  The 
source current continues to vary due to PQ issues, and 
because both the source voltage and current are continuously 
changing, the source current maintains these continual 
variations. Figure 18 indicates the waveform of the developed 
converter under a voltage swell condition. The input voltage 
is sustained at a stable value of 72 A in the entire system. 
Similarly, the input current is sustained at a value of 2400 A 
throughout the system.  Then, the converter’s output voltage 
is randomly varied and settled at a value of 880 V with little 
variation. Finally, the output current is varied arbitrarily in 
the whole system.  

The waveform of UPQC for the voltage swell condition is 
seen in Figure 19. Initially, the reference voltage of the series 
converter is 10 V, and it increased to 80 V (that is, the voltage 
swell is 70 V). Also, the reference current of the shunt 
converter is 10 A, and it increased to 40 A. Then, the power 
factor exhibits changes at the beginning due to system 
changes, but as the time frame transitions, and the power 
factor stabilizes at the value of 1. The waveform of load 
voltage and current is depicted in Figure 20. The load voltage 
is sustained at a value of 450 V, and the load current is settled 
at 35A for stable operation of the system. The stabilization of 
the load current and voltage makes a substantial difference in 
the reliability and operation of the power system.   

   
Figure 17. Waveform of the AC source 

 

Figure 18. Waveform of the developed converter 

 

 



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179 

 

Figure 19. Waveform of UPQC  

 

 

Figure 20. Waveform of load  

THD waveforms presented on the three R, Y, and B 
phases are illustrated in Figure 21. The B phase had the 
lowest THD of 3.97%, followed by the R and Y phases at 4.82% 
and 4.86% respectively, which shows that the presented 
harmonics were adequately reduced, resulting in an 
improvement in PQ.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

The analysis of voltage gain, improved high gain [23], 
Non-isolated buck-boost [24], and developed converter is 
depicted in Figure 22. The developed converter attains the 
highest voltage gain compared to other approaches, ensuring 
the overall performance of the system is enhanced. Figure 23 
displays the comparison of voltage stress for the developed, 
switched LC-based high-gain [25] and improved high-gain 
[23] converter. The developed converter has the lowest 
voltage stress compared to other approaches, indicating that 
the efficacy of the system is enhanced.  The technical benefits 
of this converter, such as the increased voltage gain and lower 
voltage stress, support its use in applications needing high 
DC-DC conversion, and also support overall output quality 
improvements, reduced voltage ripple, and increased energy 
efficiency, which all justify using this design in renewable 
energy systems, including PV applications. 

 
Figure 22. Analysis of voltage gain 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 21. Waveform of THD 



S. Kandukuri et al. /Future Technology                                                                         August 2025| Volume 04 | Issue 03 | Pages 171-181                                                                    

 

180 

 

 
Figure 23. Analysis of voltage stress  

The analysis of Grid current THD (%) for R, Y, and B 
phases for NN [26] and the developed control approach is 
illustrated in Figure 24. The developed approach 
demonstrates a reduction in THD compared to NN control 
across R, Y, and B phases, indicating better harmonic 
suppression and enhanced PQ.    

Figure 25 compares the tracking efficiency of listed 
MPPT techniques. The proposed method has an efficiency of 
98.90%, higher than both ANFIS (97.71%) [27] and Fuzzy 
(97%) [19], thus proving that it extracts more power under 
the same conditions than both these techniques. The 
proposed method demonstrates that it achieved improved 
tracking efficiency over ANFIS-based and Fuzzy-based MPPT 
methods, and superior performance. 
 

 
Figure 24. Analysis of grid current THD (%) for R, Y and B phases for 
unbalanced load conditions 

 

 
Figure 25. Comparison analysis of tracking efficiency 

 

 

4. Conclusion 

This research presents a novel UPQC system with an ANN 
controller to diminish PQ problems and offset the load 
demand in PV systems. As a result, the PV-UPQC provides a 
superior solution for electrical distribution systems' PQ 
issues. By eliminating current harmonics, reducing voltage 
fluctuation, lowering the THD level in accordance with IEEE 
standards, and improving PQ, the ANN control system offers 
superior control. Since the PV is an intermediate power 
source, connecting it directly to the UPQC results in voltage 
instability, which is resolved by utilizing a coupled quadratic 
SEPIC converter with better efficacy. Consequently, the 
cascaded ANFIS MPPT is also exploited for tracking the peak 
power from the PV system with better tracking efficiency. The 
series converter compensates for voltage sags, swells, and 
harmonics, ensuring a stable voltage supply. Then, the shunt 
converter mitigates current harmonics, corrects power 
factor, and balances load currents. The results of the MATLAB 
simulation demonstrate that the developed PV-UPQC system 
effectively raises the PQ of the source voltage and load current 
with the lowest THD.     

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The author adheres to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
Datasets analyzed during the current study are available and 
can be provided upon a reasonable request from the 
corresponding author. 

Conflict of interest 
The authors declare no potential conflict of interest. 

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