International Journal of Advances in Applied Sciences (IJAAS) Vol.
No.
June 2026, pp.
ISSN: 2252-8814.
DOI: 10.
11591/ijaas.
A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality enhancement in multi-feeder distribution systems Venna Jaya Lakshmi1.
Katragadda Swarnasri2 1Department of Electrical and Electronics Engineering.
Acharya Nagarjuna University.
Guntur.
India 2Department of Electrical and Electronics Engineering.
RVR & JC College of Engineering.
Guntur.
India
Article Info
ABSTRACT
Article history:
Non-linear sensitive loads are increasingly being used in a wide range of industrial and home applications.
Particularly, some nonlinear sensitive loads degrade the power quality (PQ) of a multi-feeder distribution system by causing current as well as voltage quality to deviate from normal To address these PQ issues, a unique multi-terminal interlineunified power quality conditioner (MT-IUPQC) device has been implemented in a multi-feeder distribution system.
This MT-IUPQC is made up of multi-voltage source inverters (VSI) coupled by a common direct current (DC)-linked capacitor, and which is controlled by using proportional integral (PI) control method.
However, due to an inappropriate gain setting choice, this PI is not suitable for regulating the DC voltage at the specified voltage level.
In this paper, an intelligent fuzzy-logic controlled MT-IUPQC provides an intelligent knowledge set with subjective assessments for improved mitigation of PQ difficulties.
The recovered total harmonic distortion (THD) of source current is 2.
45%, 2.
71%, which are well within Ie-519/2014 norms and significantly lower than the THD of non-linear sensitive load current of 30.
19%, 30.
05% in both feeder-1 and 2.
In a similar way, the THD of non-linear sensitive load voltage is obtained at 0.
which fits well under Ie-519/2014 norms and is significantly lower than the THD of the voltage source measured at 20.
62% in feeder-1.
Received Jul 15, 2025 Revised May 13, 2026 Accepted May 23, 2026 Keywords:
Fuzzy-logic controller Multi-feeder distribution system Multi-terminal IUPQC topology Power quality enhancement Total-harmonic distortions This is an open access article under the CC BY-SA license.
Corresponding Author:
Venna Jaya Lakshmi Department of Electrical and Electronics Engineering.
Acharya Nagarjuna University Namburu.
Guntur.
Andhra Pradesh.
India Email: vennajayalakshmi.
e@gmail.
INTRODUCTION
Power quality (PQ) has emerged as an essential necessity for a power distribution network's effective operation.
Distribution networks are extremely vulnerable to power outages and supply disruptions due to their interconnections with multiple-load equipment .
Consequently, a variety of PQ issues have an impact on sensitive loads in power distribution networks.
According to various research studies on PQ problems, loads account for 68% of PQ matters, while the grid-utility system accounts for 32%.
PQ refers to consistency and effectiveness, which are impacted by voltage quality and current quality at the end-user The PQ is ideally referred to as a pure sinusoidal source voltage with a steady magnitude and fundamental of their frequency.
Because of current and voltage imperfections in the common point of the distribution system, severe techno-economic impacts have been observed .
Numerous conditions that arise in highly sensitive loads and result in loss of ability and efficiency are utilized to recognize PQ concerns in the electric distribution supply network.
Effective PQ detection methodologies should be employed to determine the nature of the PQ concern impacting the system, and robust Journal homepage: http://ijaas.
Int J Adv Appl Sci
ISSN: 2252-8814
monitoring systems are required .
The purpose of this research is to minimize the amount of PQ difficulties that arise in multi-feeder distribution systems, including voltage/current harmonics, voltage interruptions, voltage sags/swells, frequency deviations, unbalanced voltage, load unbalanced, and reactive-power drop.
Owing to this perspective, several professional scientists and investigators are driven to build complex PQ enhancement approaches known as customized power compensation (CPC) technology .
Singh and Letha .
describe a number of possible circumstances for compensation of PQ issues by adopting the series/shunt interconnected compensating systems.
Such a CPC system recruits the customized-power devices (CPD.
to compensate any current and/or voltage-specific PQ concerns, resulting in the multi-feeder system becoming sinusoidal in nature, linearly balanced, and fundamental factor .
Several multi-feeders CPD techniques .
Ae.
are reported in literature.
these compensatory techniques operate individually to resolve voltage or current-driven PQ concerns .
, .
Along with classical CPD strategies discussed above, the multi-terminal driven interlined unified power-quality compensator (MT-IUPQC) is being developed to handle any voltage and/or current-driven PQ concerns in a multi-feeder distribution network.
Additionally, it can transmit reactive power across the feeders in immediate form while providing uninterrupted power delivery to customers throughout power failures.
The suggested MT-IUPQC system is made up of numerous voltage source inverters (VSI.
coupled in a shunt/series configuration and driven by a usual direct current (DC) link.
The voltage is maintained at a sufficient threshold value.
This MT-IUPQC addresses simultaneous load-related and source-related PQ concerns in a three-phase medium frequency (MF) distribution network by employing well-functioning control algorithms to obtain voltage and current reference values by monitoring the source and load-side In general, the synchronous reference frame (SRF) controller .
is being employed to calculate the voltage reference corresponding to a series-connected MT-IUPQC apparatus.
Similarly, the instantaneous real-power (IRP) controller .
is being employed to calculate the current reference to the shunt-connected MT-IUPQC apparatus.
But, the above SRF/IRP controller utilizes the proportional integral (PI) controller for DC-link voltage regulation at a specified reference DC voltage level .
Ae.
The major problem is identified in PI control, due to inappropriate gain setting choice.
this PI is not suitable for providing suitable gain values because of parameter variations, load situations, and time-varying functions.
The main aim of this work is to regulate the DC voltage at a specified voltage level by using an intelligent fuzzy-logic controller (FLC), which provides the intelligent knowledge set with subjective assessments for improved mitigation of PQ difficulties.
In this work, the working and performance of the FLC-controlled SRF/IRP-controlled the MT-IUPQC device is verified by using the MATLAB/Simulation platform the outcome results are illustrated with suitable comparisons.
COMPENSATION METHODOLOGY
The multi-feeder active compensation systems are the most tailored compensation approaches for addressing diverse PQ challenges in multi-feeder distribution networks.
It addresses a combination of voltage and current-driven PQ concerns in a multi-feeder system.
Actually, it has the ability to shift reactive and actual power across its own and nearby feeds, assuring continuous power transfer to neighbor loads regardless of any abrupt interruptions .
The block diagram of the MT-IUPQC device is illustrated in Figure 1.
It consists of four VSIs connected as a shunt/series combination and interconnected at the point of common coupling (PCC) of a multi-feeder distribution network driven by a common DC-link capacitor (CDC), which are interfaced to a multi-feeder network by using a 1:1 linear transformer.
The shunt VSIs-2 and 4 helps to mitigate any current-driven PQ concerns like current harmonics, reactive-power regulation, load sharing/balancing, and also maintain unity-power factor in both feeder-1 and 2, which are operated as in-phase opposition compensation principle.
Similarly, the series VSIs-1 and 3 helps to mitigate any voltage-driven PQ concerns like voltage harmonics, voltage interruptions, voltage sags-swells, and also maintains the voltage profile in both feeders-1 and 2, which are operated as a direct-compensation principle .
In this way, the proposed MT-IUPQC device is always dependent on obtaining possible reference currents/voltages via substantial control schemes.
Such control schemes obtain reference current/voltage signals from distorted source voltages and currents of their respective feeders via sensing elements.
Some of the well-known control schemes are the SRF and IRP control schemes for both voltage/current compensation In general, the SRF controller is employed to calculate the voltage reference corresponding to the series-connected VSI-1 and 3 of the MT-IUPQC apparatus.
Similarly, the IRP controller is being employed to calculate the current reference to shunt-connected VSIs-2 and 4 of the MT-IUPQC apparatus.
The obtained reference voltages and currents are used to control the switching actions of the MT-IUPQC device VSIs initiated by gate-pulse generation units .
A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 Figure 1.
Schematic diagram of MT-IUPQC topology for PQ enhancement By using Park's conversion technique, the production of reference voltage signals is extracted by comparing the transformed actual and reference voltage vectors in the dq-axis.
This comparator produces some error quantities.
These error values have been minimized for getting a perfect voltage vector by using a PI controller with proper selection of .
through the Ziegler-Nichols method.
The voltage controller's transfer function is as .
yco ycOyceycyc .
= .
yc ycycn.
yc ) y yayceycyc .
OIycOyceycyc .
= ycOyceycyc .
- ycOyceycyc .
c Oe .
Where ycOyceycyc .
and OIycOyceycyc .
are the error and the change in error.
The major problem is identified in PI control, due to inappropriate gain setting choice.
this PI is not suitable for providing suitable gain values because of parameter variations, load situations, and time-varying An intelligent fuzzy logic controller (FLC) is most relevant when the inference system is symbolically modelled, and considerable expert knowledge is applied .
This FLC exemplifies an intelligent, knowledge-driven process that includes FLC membership functions and fuzzy logic rule structure.
These FLC-membership functions and FLC rules are essential components of fuzzy controllers, converting critical relevant judgments from human knowledge data to artificial knowledge data .
Several attempts are made to understand the needed fulfillment in system performance using the fantastic learning technique to calculate the relationship between FLC rules and FLC membership functions.
The structure of FLC is shown in Figure 2.
The fuzzy inference method illustrates how the FLC is produced by employing confident logical actions and a knowledge base of "IF and THEN" from diverse language logical operations .
The FLC membership functions and FLC rule-structure for elimination of error components by utilizing expertise intelligence knowledge are clearly shown in Figure 3 and Table 1.
The block diagram of the proposed FLC-SRF controller is presented in Figure 4.
The seven FLC-MFAoS are used and defined as positive small (PS), negative small (NS), positive medium (PM), negative medium (NM), positive big (PB), negative big (NB), and zero (ZE), accordingly.
Int J Adv Appl Sci.
Vol.
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June 2026: 760-774
Int J Adv Appl Sci
ISSN: 2252-8814
Figure 2.
Structure of FLC e( .
AEe( .
e( .
AEe( .
Figure 3.
FLC membership functions Table 1.
FLC rule structure yceycyc .
OIyceycyc .
Figure 4.
Block diagram of FLC-SRF controller A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 Thus, the obtained error quantities are minimized by using the FLC-SRF controller, which helps to produce the feasible voltage reference in the dq-frame, which is retransformed into the abc-frame by using inverse-ParkAos conversion method, and the final voltage reference signal is described in .
ycaycuycyuE ycycnycuyuE ycOycyceyceyca.
2yuU 2yuU ycOycyceyceycc.
cOycyceyceyca.
] = .
uE Oe 3 ] ycycnycu .
uE Oe 3 ] .
[ O ycOycyceyceyc.
ycOycyceyceyca.
uE 3 ] ycycnycu .
uE 3 ] 1 Similarly, using ClarkeAos conversion technique produces the reference current signals, which are extracted by comparing the transformed actual and reference current components in a symmetrical orthogonal coordinate.
The attained current components are propagated to a second-order high-pass filter to allow higher-order frequencies for better generation of reference currents.
Along with reference to the current generation, the FLC-IRP controller maintains the DC-link voltage as constant with a specified voltage value by comparing O the actual DC-link .
cOyccyca.
yca ) and specified DC-link voltage value .
cOyccyca.
yc ) by eliminating the error quantities .
ycOyccyca.
ycayceyc = ycOyccyca.
yc Oe ycOyccyca.
OIycnyca.
yccyca = yaycy.
ycc O .
cOyccyca.
Oe ycOyccyca.
cuOe.
) yaycn.
ycc O .
cOyccyca.
) .
This FLC exemplifies an intelligent knowledge-driven process that includes FLC membership functions and a FLC rule structure is essential components of fuzzy controllers for producing robust The block diagram of the proposed FLC-IRP controller is depicted in Figure 5.
Thus, the obtained error quantities are minimized by using FLC-IRP controller, which helps to produce the feasible current reference in yuyu-frame, which is retransformed into abc by using inverse-ClarkeAos conversion method, and the final current reference signal is described in .
Oo2 Oo3 Oe1 AE .
yu12 ] AE2 .
]=Oo AE Oo2 yu12 ycnycayc.
Oe1AE Oe Oo3AE [ Oo2 Figure 5.
Block diagram of FLC-IRP controller So finally, the extracted reference voltages from FLC-SRF controller are propagated with the actual voltage signal for generation of feasible switching states to series VSIs-1 and 3 of MT-IUPQC device.
This is done by employing the sinusoidal pulse-width modulation.
Similarly, the extracted reference currents from FLC-IRP controller are propagated with the actual source current for generation of feasible switching states to shunt VSIs-2 and 4 of MT-IUPQC by employing the hysteresis current controller-based gate-driver circuitry.
Int J Adv Appl Sci.
Vol.
No.
June 2026: 760-774
Int J Adv Appl Sci
ISSN: 2252-8814
DISCUSSION OF MATLAB/SIMULINK RESULTS
In this work, the working and performance of the FLC-controlled SRF/IRP-controlled MT-IUPQC device is verified by using the MATLAB/Simulation platform.
The specifications used in the simulation model are presented in Table 2.
These specifications serve as the basis for evaluating the effectiveness of the proposed controller.
Table 2.
Simulation specifications Parameters Three-phase source voltage (Vrm.
Line impedance Sensitive load impedances Linear .
transformer Series-connected VSIs-1 and 3 filters Shunt-connected VSIs-2 and 4 filters Common DC-link capacitor Values Feeder-1 and 2 Vsabc12-415 V, 50 Hz Rs12 =0.
15 E.
Ls12-0.
9 mH RL =30 E.
L L-20 mH (NL-loa.
415 V, 50 Hz, linear model -5 KVA, 10% leakage reactance Lse-3 mH.
Cse-100 AAF Rsh =0.
001 E.
Lsh-10 mH Vdc.
c =880 V.
Cdc.
c =1500 AAF Compensation of voltage or current concerns in feeder-1 using FLC-based SRF/IRP-controlled MT-IUPQC device The simulation outcomes of current compensation in feeder-1 using FLC-based IRP-controlled VSI-2 of MT-IUPQC device is depicted in Figure 6.
In this case, the feeder-1 of the multi-feeder distribution network is energized with a voltage of 415 Vrms, 50 Hz for driving the sensitive non-linear load.
This sensitive non-linear load produces the uneven harmonic currents into the PCC of feeder-1.
Due to these harmonic distortions, feeder-1 has been damaged and loss of control in the system.
Then the shunt VSI-2 of MT-IUPQC in feeder-1 mitigates the harmonic distortions in source current, which is operated as in-phase opposition compensation principle is shown in Figure 6.
Figure 7 shows the current total harmonic distortion (THD) spectrum analysis in feeder-1.
The THD of the non-linear sensitive load current of 30.
19% in feeder-1 is shown in Figure 7.
, while the recovered THD of the source current is 2.
45% as shown in Figure 7.
, which is well within Ie-519/2014 norms.
The FLC-IRP controller always maintains DC-link voltage as the specified voltage value of 880 V is depicted in Figure 8, and also the source current of feeder-1 is always in-phase with the source voltage, which represents the unity power-factor is shown in Figure 9.
The simulation outcomes of voltage compensation in feeder-1 using FLC-based SRF-controlled VSI-1 of MT-IUPQC device are depicted in Figure 10.
In this case, the feeder-1 of the multi-feeder distribution network is energized with a voltage of 415 Vrms, 50 Hz for driving the sensitive non-linear load.
This sensitive non-linear load produces the uneven harmonic currents, which cause the voltage harmonics in feeder-1 and damage the entire load apparatus.
Then the series VSI-1 of MT-IUPQC in feeder-1 mitigates the voltage harmonics in load voltage, which is operated as a direct compensation principle, as shown in Figure 10.
Figure 11 shows the voltage THD spectrum analysis in feeder-1: the THD of source voltage is 62% as in Figure 11.
, and the recovered THD of sensitive load voltage is 0.
43% as in Figure 11.
, which is well within Ie-519/2014 norms.
Similarly, the VSI-1 of MT-IUPQC mitigates the voltage sag, voltage swell, and voltage interruptions in feeder-1 are shown in Figures 12 to 14, respectively.
The obtained results show that the feeder-1 becomes sinusoidal in nature, linearly, balancing and fundamental factor, and delivers the quality-power and reliable power to consumer loads.
Figure 6.
Simulation outcomes of current compensation in feeder-1 using FLC-based IRP-controlled VSI-2 of the MT-IUPQC device A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 .
Figure 7.
Current THD spectrum analysis in feeder-1 .
THD of non-linear sensitive load current and .
THD of source current Figure 8.
DC-link capacitor voltage Figure 9.
Source voltage and current .
n-phas.
Int J Adv Appl Sci.
Vol.
No.
June 2026: 760-774
Int J Adv Appl Sci
ISSN: 2252-8814
Figure 10.
Simulation outcomes of voltage compensation in feeder-1 using FLC-based SRF-controlled VSI-1 of the MT-IUPQC device .
Figure 11.
Voltage THD spectrum analysis in feeder-1 .
THD of source voltage and .
THD of sensitive non-linear load voltage Figure 12.
Simulation outcomes of voltage-sag compensation in feeder-1 using FLC-based SRF-controlled VSI-1 of the MT-IUPQC device A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 Figure 13.
Simulation outcomes of voltage-swell compensation in feeder-1 using FLC-based SRF-controlled VSI-1 of the MT-IUPQC device Figure 14.
Simulation outcomes of voltage-interruption compensation in feeder-1 using FLC-based SRF-controlled VSI-1 of the MT-IUPQC device Compensation of voltage/current concerns in feeder-2 using FLC-based SRF/IRP-controlled MT-IUPQC device The simulation outcomes of the current compensation in feeder-2 using FLC-based IRP-controlled VSI-4 of the MT-IUPQC device is depicted in Figure 15.
In this case, the feeder-2 of the multi-feeder distribution network is energized with a voltage of 415 Vrms, 50 Hz for driving the sensitive non-linear load.
This sensitive non-linear load produces the uneven harmonic currents into the PCC of feeder-2.
Due to these harmonic distortions, feeder-2 has been damaged, resulting in loss of control in the system.
Then the shunt VSI-4 of MT-IUPQC in feeder-2 mitigates the harmonic distortions in source current, which is operated as in-phase opposition compensation principle is shown in Figure 15.
Figure 16 shows the current THD spectrum analysis in feeder-2: the THD of non-linear sensitive load current is 30.
05% as in Figure 16.
, and the recovered THD of source current is 2.
71% as in Figure 16.
, which is well within Ie-519/2014 The FLC-IRP controller always maintains DC-link voltage as the specified voltage value of 880 V is depicted in Figure 17, and also the source current of feeder-1 is always in-phase with the source voltage, which represents the unity power-factor is shown in Figure 18.
The simulation outcomes of voltage compensation in feeder-2 using FLC-based SRF-controlled VSI-3 of the MT-IUPQC device is depicted in Figures 19 and 20.
In this case, the feeder-2 of the multi-feeder distribution network is energized with a voltage of 415V rms, 50Hz for driving the sensitive non-linear load.
This sensitive non-linear load is affected by several voltage issues.
Similarly, the VSI-3 of MT-IUPQC mitigates the voltage sag and voltage swell in feeder-2 are shown in Figures 19 and 20, respectively.
The obtained results show that the feeder-2 becomes sinusoidal in nature, linearly, balancing and fundamental factor, and delivers quality power and reliable power to consumer loads.
Int J Adv Appl Sci.
Vol.
No.
June 2026: 760-774
Int J Adv Appl Sci
ISSN: 2252-8814
Figure 15.
Simulation outcomes of current compensation in feeder-2 using FLC-based IRP-controlled VSI-4 of the MT-IUPQC device .
Figure 16.
Current THD spectrum analysis in feeder-2 .
THD of non-linear sensitive load current and .
THD of source current Figure 17.
DC-link capacitor voltage A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 Figure 18.
Source voltage and current .
n-phas.
Figure 19.
Simulation outcomes of voltage-sag compensation in feeder-2 using FLC-based SRF-controlled VSI-3 of the MT-IUPQC device Figure 20.
Simulation outcomes of voltage-swell compensation in feeder-2 using FLC-based SRF-controlled VSI-3 of the MT-IUPQC device The comparisons and bar-graph representation of voltage THD of source voltage, sensitive load-side voltage in a feeder-2 of multi-feeder system is presented in Table 3 and Figure 21.
The comparisons and bar-graph representation of non-linear sensitive load current, source/PCC current in feeder-1 and 2 are presented in Tables 4 and 5.
Figures 22 and 23, respectively.
The illustration of voltage values of source, sensitive load, and VSI-1 and 3 injected voltage in feeder-1 and 2 is presented in Tables 6 and 7.
Int J Adv Appl Sci.
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Int J Adv Appl Sci
ISSN: 2252-8814
Table 3.
Comparisons of voltage THD of source voltage, sensitive load-side voltage in feeder-2 of multi-feeder system Method No compensation PI-SRF Fed MT-IUPQC .
FLC-SRF Fed MT-IUPQC Source voltage THD (%) Sensitive load voltage THD (%) Figure 21.
Bar-graph representation of voltage THD comparisons in feeder-2 Table 4.
Comparisons of non-linear sensitive load current, source/PCC current in feeder-2 of multi-feeder system Method No compensation PI-IRP Fed MT-IUPQC .
FLC-IRP Fed MT-IUPQC Non-linear sensitive load current THD (%) Source current THD (%) Table 5.
Comparisons of non-linear sensitive load current, source/PCC current in feeder-1 of multi-feeder system Method No compensation PI-IRP Fed MT-IUPQC .
FLC-IRP Fed MT-IUPQC Non-linear sensitive load current THD (%) Figure 22.
Bar-graph representation of current THD comparisons in feeder-2 Source current THD (%) Figure 23.
Bar-graph representation of current THD comparisons in feeder-1 A novel multi-terminal fuzzy-logic controlled IUPQC device for power quality A (Venna Jaya Lakshm.
A ISSN: 2252-8814 Table 6.
Voltage values of source, sensitive load, and VSI-1 injected voltage in feeder-1 Operating condition Under normal case .
efore t-0.
1 se.
Under voltage-sag .
1 < t <0.
2 se.
Under voltage-interruptions .
35 < t <0.
55 se.
Under voltage-swell .
65 < t <0.
75 se.
Source voltage (V) VSI-1 injected voltage (V) Non-linear sensitive load voltage (V) Table 7.
Voltage values of source, sensitive load, and VSI-3 injected voltage in feeder-2 Operating condition Under normal case .
efore t-0.
25 se.
Under voltage-sag .
25 < t <0.
35 se.
Under voltage-swell .
55 < t <0.
65 se.
Source voltage (V) VSI-3 injected voltage (V) Non-linear sensitive load voltage (V) CONCLUSION In this work, the working and performance of the FLC-controlled SRF/IRP-controlled MT-IUPQC device are presented.
The FLC-controlled MT-IUPQC works very well for compensation of various current/voltage-driven PQ issues in a multi-feeder network.
The proposed FLC exhibits an intelligent, knowledge-driven approach that incorporates FLC membership functions and fuzzy logic rule structure.
These components are crucial components of fuzzy controllers.
they provide the intelligent knowledge set with subjective assessments for improved mitigation of PQ difficulties.
The obtained THD of source current 45%, 2.
71%, which is substantially within Ie-519/2014 limits and much lower than the THD of non-linear sensitive load current is 30.
19%, 30.
05% in both feeders-1 and 2.
Similarly, the THD of non-linear sensitive load voltage is 0.
43%, which is considerably within Ie-519/2014 requirements and significantly lower than the THD of the voltage source, which was measured at 20.
62% in feeder 1.
FUNDING INFORMATION
Authors state no funding involved.
AUTHOR CONTRIBUTIONS STATEMENT
This journal uses the Contributor Roles Taxonomy (CRediT) to recognize individual author contributions, reduce authorship disputes, and facilitate collaboration.
Name of Author Venna Jaya Lakshmi Katragadda Swarnasri C : Conceptualization M : Methodology So : Software Va : Validation Fo : Formal analysis E I : Investigation R : Resources D : Data Curation O : Writing - Original Draft E : Writing - Review & Editing E Vi : Visualization Su : Supervision P : Project administration Fu : Funding acquisition CONFLICT OF INTEREST STATEMENT The author declares that there are no known conflicts of interest associated with this publication.
There are no financial or personal relationships that could inappropriately influence or bias the content of this work.
INFORMED CONSENT
Not applicable.
This study did not involve human participants, human data, or any personally identifiable information.
All data used were either publicly available, fully anonymized, or derived from nonAchuman sources, and therefore no informed consent was required from individuals.
Int J Adv Appl Sci.
Vol.
No.
June 2026: 760-774
Int J Adv Appl Sci
ISSN: 2252-8814
ETHICAL APPROVAL
Not applicable.
This research did not involve human subjects, human biological materials, or experimental procedures on animals.
The work was conducted solely on computational models, publicly available datasets, or nonAcsensitive data that did not require intervention with living organisms.
Therefore, ethical approval from an institutional review board or animal ethics committee was not necessary for this study.
DATA AVAILABILITY
Data availability is not applicable to this paper as no new data were created or analyzed in this study.
REFERENCES