A Review on Improved Secondary Distribution Network for Power Quality in Grid Integration of Small-Scale Photovoltaic System

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A Review on Improved Secondary Distribution Network for Power
Quality in Grid Integration of Small-Scale Photovoltaic System
Waseem Ahmed Halwegar1,2, Naveen Kumar J. R.2*, Altaf Mudhol3
1 Assistant Professor & HOD, Department of Electronics and Communication Engineering, Anjuman Institute of
Technology and Management, Bhatkal, Karnataka, India.
2 Professor & HOD, Department of Electronics and Communication Engineering, Institute of Engineering and
Technology, Srinivas University. Mangalore, India
3 Professor & HOD, Department of Electrical Engineering, Bharat-Ratna Indira Gandhi College of Engineering, Solapur,
India
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ABSTRACT
The grid integration of small-scale photovoltaic systems involves connecting the distributed energy resources to the electricity
grid. To design an improved secondary distribution network for enhanced power quality in the integrated grid of small-scale
photovoltaic systems, it is imperative to address various technical challenges. Hence, this review paper presents a comprehensive
overview of the recent developments in various power quality improvement methods for power quality enhancement in grid
integration, focusing on PI-Based Reactive Power Control Systems, Flicker Logistic Control Methods, Automated Filtering
Mechanisms, Shunt Active Power Filter modules, Integrated Optimization-based AI Technique and Grid Synchronization
Techniques. The review explores the benefits and drawbacks of each technique, providing an in-depth understanding of their
respective contributions to improving power quality. Also, it provides a comparative analysis to demonstrate the effectiveness of
various techniques for enhancing power quality. The review systematically evaluates how each method addresses challenges such
as voltage fluctuations, harmonics, and flicker, thereby contributing to a more stable and reliable power supply. The review also
highlights the future research directions and challenges in implementing various techniques for enhancing power quality in grid
integration of small-scale photovoltaic systems and provides some suggestions for further improvements to be done in the future
for better power quality in grid integration of small-scale photovoltaic system
Keywords: Logistic Control Methods, Reactive Power Control, Grid Synchronization, small-scale photovoltaic system,
Shunt Active Power Filter
1. INTRODUCTION
Because of the depletion of fossil fuels and the need to reduce their negative impact on the environment, the use of
renewable energy is constantly evolving today. Photovoltaic energy generation is becoming more popular in both
urban and rural areas due to its low cost, low noise, and easy availability. Microgrids are commonly used to
distribute electrical energy in rural communities [1]. There are two types of microgrids: standalone and grid-
connected. The solar power plant is disconnected from the grid in standalone photovoltaic microgrids. In this case,
the plant is designed for a low voltage distribution network and is subject to voltage imbalance as load imbalance
increases, which can be dangerous for some equipment such as 3-phase motors [2]. Furthermore, due to bad
weather conditions, a standalone PV system cannot supply load at night and at times during the day; thus, an
energy storage system is typically used to stabilize the level of produced energy [3]. When power converters are
used, harmonics are introduced into the system. In contrast, the increased use of sensitive electronic circuits in
industries and households, as well as privatization and rivalry in electric energy systems, pose power quality
improvement as one of the key problems in the electrical industry. Harmonics cause source voltage distortion and
addition loss due to unwanted current flowing in the source. It may also lead to the malfunctioning of relays, mains,
and other control units. As a result, it is necessary to reduce the harmonics. There are numerous techniques for
reducing the effect of harmonics [4, 5]. One of these methods is to use SAPF, which generates a harmonic current of
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equal magnitude and polarity to the harmonic current produced in the system, canceling the harmonic current in
the system. Because it contains power electronic devices, it has a fast response time and flexibility in operation.
Shunt Active Power Filter (SAPF) is capable of compensating harmonics, and current distortion, and injecting
power generated by unconventional resources [6]. The SAPF is a load-related voltage source inverter (VSI). For
various load conditions, the Shunt Active Power Filter can keep the current balanced and sinusoidal after
remuneration. Power switching devices are used to convert DC to AC power. As a result, the output waveforms are
composed of distinct values, yielding output that is more oscillatory rather than filtered. When and how long the
power values can become active, the capacity to deliver close sine waveforms around the key recurrence is
directed by the tyrannical regulation technique. Additionally, Static Volt-Ampere Reactive Compensators (SVC)
play a role in controlling system voltages, enhancing transient stability, and increasing grid capacity [7, 8]. The
integration of PV cells involves the challenge of efficiently converting electromagnetic waves into electricity.
The MPPT technique employed in the PV system plays a crucial role in maximizing the utilization of the PV array
output power and efficiently tracking the Maximum Power Point (MPP) from the PV array input. Detecting the
voltage or current MPP automatically poses a significant challenge in MPPT techniques [9]. This challenge is
further compounded by the impact of changes in output voltage on MPPT output characteristics [10]. Despite the
advantages of MPPT techniques, existing inverter levels exhibit some inaccuracy due to the additional
functionalities required. This inaccuracy is particularly relevant when considering the time required to predict the
Global Maximum Power Point (GMPP), which is directly proportional to the number of PV arrays and the
complexity of the system design [11].
To address power quality issues, Distributed Flexible AC Transmission System (DFACTS) devices with control
algorithms are deployed at the point of common coupling (PCC). DSTATCOM and UPQC, for example, adjust
voltage, impedances, and power to improve system dynamics. Devices are classified as series, shunt, series-series,
or shunt-series [12, 13]. Traditional control algorithms based on synchronous reference frame theory (SRFT) and
instantaneous reactive power theory (IRPT) have drawbacks such as slow response and poor stability. Overcoming
these, AI-based algorithms based on deep learning and machine learning provide precise PQ issue mitigation via
data modeling. However, these models face challenges such as a lack of creativity and increased system costs. The
least mean square (LMS) and least mean fourth (LMF) adaptive control algorithms address intermittency in solar
PV generation by improving dynamic responses, adaptivity, computational efficiency, and enhanced dynamic
responses in grid-connected solar PV systems [13-15]. Many researchers have introduced various techniques for
power quality enhancement in grid integration of small-scale photovoltaic systems but the directions of analyzing
the approaches have very rarely been summarized. The main contribution of the review paper is to study enhanced
power quality which is given below:
 To reduce the voltage fluctuations, harmonics, and flicker in power quality enhancement in grid integration
of small-scale photovoltaic systems, various power quality improvement methods are reviewed with their
significance and limitations.
 Various power quality improvement methods such as PI-Based Reactive Power Control Systems, Flicker
Logistic Control Methods, Automated Filtering Mechanism, Shunt Active Power Filter modules, Grid
Synchronization Techniques, and Integrated Optimization-based AI Techniques are used to enhance the
power quality, and these techniques are analyzed with their significance and limitations
The content of the paper is organized as follows: Section 2 presents the literature survey of various approaches for
load frequency control in an interconnected power system, section 3 provides comparative analysis; Section 4
discusses the result, section 5 concludes the paper, and finally, section 6 provides the future perspective.

2. LITERATURE SURVEY
In this section, the review has been provided by discussing various technologies for power quality in grid
integration of small-scale photovoltaic systems. Also, the significance and limitations of these techniques are
described. The directions for power quality techniques are shown in Figure 1.
Figure 1: The directions for power quality enhancement techniques
This review has been made in six different phases of power quality enhancement in grid integration of small-scale
photovoltaic systems namely, PI-Based Reactive Power Control System, Flicker Logistic Control Methods,
Automated Filtering Mechanism, Shunt Active Power Filter module, Grid Synchronization Techniques, and
Integrated Optimization-based AI Techniques.
2.1 Review on PI-Based Reactive Power Control System in Grid Integration
In this review of power quality enhancement using various PI-Based Reactive Power Control Systems in grid
integration of small-scale photovoltaic systems, Many PI-based reactive Power Controls are used in power quality
enhancement such as optimal fractional order PID controller, Fuzzy PID controller, Fuzzy adaptive PI controller,
Multi-stage fuzzy-based flexible controller, and Distributed power flow controller. However, there were some
limitations, such as the computational burden and testing is a challenge for real-time applications, as well as the

limited adaptability to a wide range of scenarios. Therefore, controllers must be developed to properly enhance the
power quality in grid integration of small-scale photovoltaic systems[16-25].
Table 1: Review on PI-Based Reactive Power Control System in Grid Integration
Ref no Techniques/method Significance Limitations
[16]
Optimal fractional order
PID controller
Reduce settling time and
overshoot
The controller’s ability is
affected by the errors in the
probabilistic models.
[17] Fuzzy-PI and fuzzy-PID
Reduced voltage
fluctuation, and reduced
overshooting.
Fuzzy controllers have a
computational burden in real-
time applications.
[18]
Fuzzy adaptive PI
controller
Solve power quality
problems and mitigate
voltage sags, voltage
swells, and load harmonic.
Environmental conditions
affect the controller's
robustness.
[19]
Multi-stage fuzzy-based
flexible controller
Achieved voltage
stabilization and ensured
the lowest harmonic
distortion.
Fuzzy controllers have a
computational burden in real-
time applications.
[20]
Distributed power flow
controller
Mitigate the power quality
issues and current
harmonics.
The controller needs to be
adopted.
[21] Fuzzy Logic Controller
Ensured voltage recovery,
increased accuracy, and
negligible voltage dip
Environmental conditions
affect the controller's
robustness and testing is a
challenge for real-time
applications
[22]
Fuzzy logic-based
modified real-reactive
power control
Limiting grid overcurrent
by reducing active power
flow.
Risk of changes in the
operating environment.
[23]
Double second-
order generalized
integrator phase-locked
loop
PI controller's gain to
obtain a fast response and
improve the system's
power quality.
The controllers are required
to be adaptable.
[24]
Highly Reduced Fuzzy
Logic Controller
This simplifies the
implementation process
and also results in
significant time and cost
savings.
Adaptability to a wide range
of scenarios is limited.
[25]
Cascaded H-bridge
inverter
Improve the system's
power quality, flexibility,
and efficiency of the
photovoltaic system.
Unwanted oscillation happens
in rapid changes.
Table 1 gives the power quality enhancement using various PI-Based Reactive Power Control Systems in grid
integration of small-scale photovoltaic systems. From the table, it is observed that the power quality enhancement
in an in-grid integration of a small-scale photovoltaic system is reviewed and the advantages as well as the

disadvantages of using various PI-Based Reactive Power Controls are also reviewed. Many PI-based reactive Power
Controls are used in power quality enhancement such as optimal fractional order PID controller, Fuzzy PID
controller, Fuzzy adaptive PI controller, Multi-stage fuzzy-based flexible controller, and Distributed power flow
controller. However, there were some limitations, such as the computational burden and testing is a challenge for
real-time applications, as well as the limited adaptability to a wide range of scenarios. Therefore, controllers must
be developed to properly enhance the power quality in grid integration of small-scale photovoltaic systems.
2.2 Review on Flicker Logistic Control Methods in Grid Integration
This review gives the various flicker logistic control methods using power quality enhancement in grid integration
of small-scale photovoltaic systems. it is observed that the logistic control methods are reviewed and the
advantages as well as the disadvantages of the various secure data transmission in network security are also
reviewed. Many logistic control methods such as Unified Power Quality Conditioner, Dynamic voltage restorer,
Static Var compensator, Static synchronous compensator, and Distribution static compensator were analyzed.
However, all these approaches have some limitations such as reactive power imbalances caused by solar power's
fluctuating nature, power consumption increases and the cost of batteries and associated power electronics make
this approach too costly. Hence, there is a need for logistic control methods to improve the power quality in grid
integration of small-scale photovoltaic systems[26-35].
Table 2: Review on Flicker Logistic Control Methods in Grid Integration
[26]
Distribution static
compensator
Achieving unity power
factor and keeping the
power quality stable.
Adapting the system to
different scales and
configurations necessitates
additional considerations
[27]
Static compensator
integration in
distribution networks
Reduce active and reactive
power demand and reliance
Cost-effective and power
consumption increases.
[28] Static Var compensator
Provides well-enhanced
voltage stability, the
smallest voltage deviation,
and active power loss
Reactive power imbalances
are caused by solar power's
fluctuating nature.
[29] Dynamic voltage restorer
Supply power to sensitive
loads.
The cost of batteries and
associated power
electronics make this
approach too costly.
[30] Dynamic voltage restorer
Mitigation of voltage sags,
improving the overall
power quality.
The combination of various
technologies makes the
system more complex.
[31]
Static synchronous
compensator
Improving voltage
regulation, improving the
performance and reliability
of these systems
Introduce additional system
disturbances in the form of
current unbalanced and
harmonic injections.
[32]
Unified Power Quality
Conditioner
Integrating power quality
improvement and clean
energy generation.
Local loads experience some
disruption during the switch.
[33]
Quasi Z-source inverter-
based UPQC
Reducing current and
voltage instabilities, and
Scalability across different
scales of solar PV

harmonic content. installations poses a
challenge.
[34]
Transformerless
dynamic voltage restorer
The converter
achieved high voltage gain
through its soft-switching
capability.
This affects the consistent
performance of the DVR
system.
[35]
Integrated distribution
static synchronous
compensator
Voltage fluctuation was
negligible.
Reactive power imbalances
are caused by solar power's
fluctuating nature.
Table 2 gives the various flicker logistic control methods using power quality enhancement in grid integration of
small-scale photovoltaic systems. From the table, it is observed that the logistic control methods are reviewed and
the advantages as well as the disadvantages of the various secure data transmission in network security are also
reviewed. Many logistic control methods such as Unified Power Quality Conditioner, Dynamic voltage restorer,
Static Var compensator, Static synchronous compensator, and Distribution static compensator were analyzed.
However, all these approaches have some limitations such as reactive power imbalances caused by solar power's
fluctuating nature, power consumption increases and the cost of batteries and associated power electronics make
this approach too costly. Hence, there is a need for logistic control methods to improve the power quality in grid
integration of small-scale photovoltaic systems.
2.3 Review on automated filtering mechanism in grid integration
The review gives the various automated filtering mechanisms using power quality enhancement in grid integration
of small-scale photovoltaic systems. It is observed that the power quality enhancement in grid integration of small-
scale photovoltaic systems using various automated filters is reviewed and the advantages as well as the
disadvantages of the various are also provided. Various automated filtering mechanisms are used for power quality
enhancement in grid integration of small-scale photovoltaic systems such as LCL filter, Shunt hybrid active filter,
Hybrid series active power filter, Fractional order notch filter-based control, LC passive filters Recursive Digital
Filter, Hybrid harmonic filter. However, some limitations include its inability to adapt to dynamic real-world
conditions, variations in frequency and voltage that cause grid instability, and voltage spikes that occur across the
filter components. To effectively improve the power quality in grid integration of small-scale photovoltaic systems,
the filters' performance must be improved [35-45].
Table 3: Review on automated filtering mechanism in grid integration
[36] LCL filter
Filters out harmonics and
increases power quality.
Voltage spikes occur across
the filter components
[37]
Shunt hybrid active
filter
PV systems to improve
performance under different
operating conditions.
Solar irradiation caused
fluctuations in the generated
power.
[38]
Hybrid series active
power filter
To minimize the usage of
energy from the utility grid.
Variations in frequency and
voltage cause grid instability.
[39]
Fractional order
notch filter-based
control
Designed control system
handled harmonic distortion
in grid current, reactive
power demand of the load,
and unbalanced load currents.
Filters exhibit overshoot and
ringing behavior in sudden
changes.

[40] LC passive filters
The grid system significantly
reduced the inverter output
current.
Distortions of voltage and
current waveform, decreased
system efficiency, and
increased losses in the
system.
[41]
Recursive Digital
Filter
Improving power quality and
ensuring power transfer
between the utility grid and
connected loads.
Performance reduces due to
the slow response of variable
insolation conditions.
[42]
Shunt active power
filter
Provide a smooth DC-link
voltage and reduce total
harmonic distortion.
Inefficient for dynamic load
changes, and high
computational burden.
[43]
Hybrid shunt active
power filter
Improve power quality by
compensating harmonics and
regulating reactive power.
When renewable energy
generation is low and load
demands are high, this harms
the power supply's
dependability and stability.
[44]
Hybrid harmonic
filter
Optimized the size and
number of passive filters, and
hysteresis bandwidth.
Power quality needs to be
improved by using different
hybrid filter topologies.
[45] LC filters
Enhancing dynamic stability
and power quality under any
conditions.
Lack of adaptability to
dynamic real-world
conditions.
Table 3 gives the various automated filtering mechanisms using power quality enhancement in grid integration of
small-scale photovoltaic systems. It is observed that the power quality enhancement in grid integration of small-
scale photovoltaic systems using various automated filters is reviewed and the advantages as well as the
disadvantages of the various are also provided. Various automated filtering mechanisms are used for power quality
enhancement in grid integration of small-scale photovoltaic systems such as LCL filter, Shunt hybrid active filter,
Hybrid series active power filter, Fractional order notch filter-based control, LC passive filters Recursive Digital
Filter, Hybrid harmonic filter. However, some limitations include its inability to adapt to dynamic real-world
conditions, variations in frequency and voltage that cause grid instability, and voltage spikes that occur across the
filter components. To effectively improve the power quality in grid integration of small-scale photovoltaic systems,
the filters' performance must be improved.
2.4 Review on Shunt Active Power Filter Module in Grid Integration
The review gives the various shunt active power filter modules for power quality in grid integration of small-scale
photovoltaic systems, it is observed that the detection of power quality in grid integration are reviewed and the
advantages as well as the disadvantages of the various shunt active power filter module are also reviewed. Many
shunt active power filter modules such as Hybrid active power filter, Shunt Active Power Filter, switched power
filter compensator, and Three-phase half-bridge interleaved buck shunt active power filter are presented to reduce
settling time and total harmony distortion and improve power quality in grid integration of small-scale
photovoltaic systems. However, all these approaches have some limitations such as low reactive power levels
leading to voltage instability, and the ability to address voltage fluctuations is limited. Hence, there is a need to
develop a shunt active power filter module to effectively improve the power quality[46-55].

Table 4: Review on shunt active power filter module in grid integration
[46]
Shunt active power filter
based on a PLL
SAPF mitigation suppressed
non-sinusoidal harmonic
current and increased
active power.
Low reactive power levels
lead to voltage instability.
[47]
Three-phase half-bridge
interleaved buck shunt
active power filter
Achieve a high degree of
compensation for current
harmonics and reactive
power
Low reactive power levels
lead to voltage instability
[48]
Solar PV-integrated
universal active power
filter
The proposed system
combines the advantages of
clean energy generation
with improved power
quality.
Voltage and frequency
fluctuations are caused.
[49] Shunt active power filter
The proposed controller
reduced the harmonics.
Processing power and
memory of digital signal
processors are limited,
affecting the controller's
efficiency and performance.
[50]
SRF theory-based Shunt
active power filter
Reduced settling time and
total harmony distortion.
Affected the system's real-
time performance.
[51]
Shunt Active Power
Filter
LME provided a significant
steady-state response and
power quality
improvement.
The computational
complexity of the system
has an impact on its real-
time performance.
[52]
Grid-tied Shunt Active
Power Filter
Estimates the harmonic
current.
Affected the system's real-
time performance.
[53]
Hybrid active power
filter
Hysteresis current control
eliminates harmonic.
Their ability to address
voltage fluctuations is
limited.
[54]
A switched power filter
compensator and a
switched active power
filter
Eliminate the harmonic
amplitude of the current
and voltage and controller
error.
[55]
Single-phase active
power filter
Ensure high grid current
quality and minimize
switching frequency.
Implementing a modified
PUC converter requires the
use of additional
components.
Table 4 gives the various shunt active power filter modules for power quality in grid integration of small-scale
photovoltaic systems. From the table, it is observed that the detection of power quality in grid integration are
reviewed and the advantages as well as the disadvantages of the various shunt active power filter module are also
reviewed. Many shunt active power filter modules such as Hybrid active power filter, Shunt Active Power Filter,
switched power filter compensator, and Three-phase half-bridge interleaved buck shunt active power filter are
presented to reduce settling time and total harmony distortion and improve power quality in grid integration of

small-scale photovoltaic systems. However, all these approaches have some limitations such as low reactive power
levels leading to voltage instability, and the ability to address voltage fluctuations is limited. Hence, there is a need
to develop a shunt active power filter module to effectively improve the power quality.
2.5 Review on Grid Synchronization Techniques in Grid Integration
The review gives the power quality improvement using grid synchronization techniques in grid integration of
small-scale photovoltaic systems, it is observed that the power quality improvement in grid integration of small-
scale photovoltaic systems is reviewed and the advantages as well as the disadvantages of using various grid
synchronization techniques are also reviewed. Many synchronization techniques are used in power quality
improvement such as Phase Locked Loop, DQ current control theory, Mixed third and fourth-order complex filter,
Vector-based synchronization, Adaptive feed-forward PLL, and lightweight inertial PLL. However, there were some
limitations such as PLLs exhibiting transient responses during sudden grid changes, lack of robustness resulting in
instability and it requires rapid and accurate synchronization. Therefore, synchronization techniques must be
developed to properly improve the power quality in in grid integration of small-scale photovoltaic systems[56-65]
Table 5: Review on Grid Synchronization Techniques in Grid Integration
[56]
Adaptive feed-
forward PLL
Increased the stability region,
reduced the effect of voltage
variation, and provided high
harmonic attenuation.
Practical implementation of
adaptive algorithms is
difficult, and lack stability.
[57]
Vector-based
synchronization
The battery charges and
discharges to manage excess
and deficient power.
System stability requires
rapid and accurate
synchronization.
[58]
Iterated Extended
Kalman Filtering
Achieving unity power factor,
reducing mean square error,
and increasing convergence
speed
The accuracy of KF
prediction is not very good
[59]
Mixed third and
fourth-order complex
filter
Reduced computation time,
addressed the poor power
quality issue,
eliminated harmonics, and
reduced burden and noise
rejection.
Lack of robustness results
in instability.
[60] Phase-locked loop
Improve power quality, and
reduce harmonics. less
distorted power output
[61] Phase Locked Loop
Total harmonic distortion was
reduced and improved
synchronization accuracy
PLLs exhibit transient
responses during sudden
grid changes.
[62] Improved PLL
Provided a great dynamic
response
Sudden changes in grid
voltage quality
impair current extraction
accuracy.
[63] PLL synchronization
Improve power quality and
high harmonic attenuation.
Communication
delays impact
synchronization accuracy

and speed
[64]
lightweight inertial
PLL
Provides the desired inertial
response and damping capacity,
resulting in the lowest
frequency drop at the system.
IPLL encounters challenges
in dynamic grid conditions.
[65]
DQ current control
theory
Computational burden reduced,
and steady-state performance
was improved.
Mismatches in frequency
have an impact on the
reliability of power control
and tracking.
Table 5 gives the power quality improvement using grid synchronization techniques in grid integration of small-
scale photovoltaic systems. From the table, it is observed that the power quality improvement in grid integration of
small-scale photovoltaic systems is reviewed and the advantages as well as the disadvantages of using various grid
synchronization techniques are also reviewed. Many synchronization techniques are used in power quality
improvement such as Phase Locked Loop, DQ current control theory, Mixed third and fourth-order complex filter,
Vector-based synchronization, Adaptive feed-forward PLL, and lightweight inertial PLL. However, there were some
limitations such as PLLs exhibiting transient responses during sudden grid changes, lack of robustness resulting in
instability and it requires rapid and accurate synchronization. Therefore, synchronization techniques must be
developed to properly improve the power quality in in grid integration of small-scale photovoltaic systems.
2.6 Review on Integrated Optimization-Based AI Techniques in Grid Integration
The review gives the power quality enhancement using various Integrated Optimization-Based AI techniques, it is
observed that the power quality enhancement in a grid integration of a small-scale photovoltaic system is reviewed
and the advantages as well as the disadvantages of the various are also reviewed. Various Integrated Optimization-
Based AI techniques such as Tunable Q-factor wavelet transform and an Artificial neural network, Grey wolf-
optimized Artificial neural network, Decision-tree-based fuzzy logic controller, Multi-Feature-attention-LSTM,
Statistical feature-based Deep neural network, Deep-learning classifier based on LSTM, Adaboost algorithm, and
convolutional neural network are used for power quality enhancement in an interconnected power system.
However, all these approaches have some limitations such as effective decision-making is needed, the reliable
detection of islanding is difficult in dynamic response, real-time use is computationally complex and threshold-
setting is difficult. Hence, there is a need to develop Optimization-Based AI techniques for effectively controlling
the power quality enhancement in grid integration of a small-scale photovoltaic system[66-75].
Table: Review on Integrated Optimization-Based AI Techniques in Grid Integration
[66]
MPPT-based AI
controllers
Improved power quality for
the grid-connected PV
system.
AI controllers lack
robustness in the face of
rapidly changing operating
conditions.
[67]
Tunable Q-factor
wavelet transform and
an Artificial neural
network
Highly accurate islanding
condition and insensitive to
external grid disturbances.
Changes in environmental
conditions, load variations,
and other factors that
affect photovoltaic system
performance.

[68]
Grey wolf-
optimized Artificial
neural network
More accurate and
dependable islanding
detection, and improving
efficiency, reliability, and
sustainability.
Difficult to ensure reliable
and precise islanding
detection under various
conditions.
[69]
Decision-tree-based
fuzzy logic controller
Effective islanding
detection, improved
transient response, and
reduced settling time.
Total harmonic distortion
exceeds the limits.
[70]
Continuous Wavelet
Transforms and
convolutional neural
network
Improves the model's
robustness to frequency
and amplitude variations.
Real-time is extremely
computational and poses
difficulties for execution in
control systems.
[71]
Multi-Feature-attention-
LSTM
Higher accuracy reduces
noise interference, ensures
safe operation, and shorter
detection time
The issue of the active
method affects the
microgrid power quality.
[72]
Statistical feature-based
Deep neural network
Performed well under noisy
conditions and detected
islanding conditions
accurately.
Effective decision-making is
required.
[73]
Deep-learning classifier
based on LSTM
Detecting islanding
effectively and using
less detection time
Careful hyperparameter
selection is required.
[74] Adaboost algorithm
Effectively reducing the
complexity of the island
classification and accurate
detection.
There is a difficulty in
threshold-setting.
[75]
convolutional neural
network
Process large amounts of
imagery rapidly while also
ensuring the power
system's safety.
Careful hyperparameter
selection is required.
The table gives the power quality enhancement using various Integrated Optimization-Based AI techniques. From
the table, it is observed that the power quality enhancement in a grid integration of a small-scale photovoltaic
system is reviewed and the advantages as well as the disadvantages of the various are also reviewed. Various
Integrated Optimization-Based AI techniques such as Tunable Q-factor wavelet transform and an Artificial neural
network, Grey wolf-optimized Artificial neural network, Decision-tree-based fuzzy logic controller, Multi-Feature-
attention-LSTM, Statistical feature-based Deep neural network, Deep-learning classifier based on LSTM, Adaboost
algorithm, and convolutional neural network are used for power quality enhancement in an interconnected power
system. However, all these approaches have some limitations such as effective decision-making is needed, the
reliable detection of islanding is difficult in dynamic response, real-time use is computationally complex and
threshold-setting is difficult. Hence, there is a need to develop Optimization-Based AI techniques for effectively
controlling the power quality enhancement in grid integration of a small-scale photovoltaic system.

3. Comparison of performance of various Power quality improvement techniques for grid integrated small-
scale photovoltaic system
The comparison analysis of various approaches for power quality improvement is discussed in this section.
Figure 2: Comparison of overshoot and steady-state error of various PI-Based Reactive Power Control
Figure 2 depicts the comparison overshoot and steady-state error of various PI-Based Reactive Power Controls
such as the PI [17], PID [17], Fuzzy-PI [17], and Fuzzy-PID [17]. From the comparison, the PI, PID, Fuzzy-PI, and
Fuzzy-PID controllers achieve an overshoot of 4.6, 4.1, 3.2%, and 2.1% respectively. The steady-state error PI, PID,
Fuzzy-PI, and Fuzzy-PID controllers are 0.9%, 0.7%, 0.6%, and 0.2%. Compared with all the existing models Fuzzy-
PID achieves less steady-state error and overshoot.
Figure 3: Comparison of total harmonic detection with various automated filters
Figure 3 shows the comparison of the total harmonic detection of various automated filters such as passive filter
[44], active filter [44], and hybrid filter [44] which have a frequency deviation value of 1.2%, 4%, and 1.25%
respectively. Compared with all the existing techniques hybrid filters have a low total harmonic detection value of
1.2%.
0
1
2
3
4
5
PI PID Fuzzy PI Fuzzy-PID
Over Shoot Steady -State Error(%)
0
1
2
3
4
5
Pasive Filters Active Filters Hybrid Filters
Total Harmonic detection THD ( % )
Total Harmonic detection THD ( % )

Figure 4: Comparison of reactive power with various flicker logistic control methods
Figure 4 illustrates the comparison of the reactive power of various flicker logistic control methods such as SVC
[26], STATCOM [27], and DSTATCOM [28]. The existing techniques such as SVC, STATCOM, and DSTATCOM have a
reactive power value of 7.8, 8, and 10 kvar respectively. DSTATCOM has a high reactive power value of 10kvar and
SVC has a low settling time value of 7.8 kvar.
Figure 5: Comparison of settling time with various grid synchronization techniques
Figure 5 shows the comparison of settling time with various existing grid synchronization techniques such as SRF
PLL [56], DSOGI PLL [23], EPLL [61], and APLL [56]. The existing techniques such as SRF PLL, DSOGI PLL, EPLL,
and APLL have a settling time value of 58 sec, 6 sec, 30 sec, and 52 sec respectively. SRF PLL has a high settling
time value of 58 and DSOGI PLL has a low settling time value of 6 sec.

Figure 6: Comparison of Islanding detection time with various Integrated Optimization-Based AI techniques
Figure 6 demonstrates the comparison of Islanding detection time with various Integrated Optimization-Based AI
techniques such as decision tree [73], SVM [73], ANN [73], and LSTM [73] attaining an Islanding detection time of
22ms, 18ms, 14ms, and 6ms respectively. Compared with all the existing techniques LSTM has a less Islanding
detection time of 6ms.
Overall, the comparison analysis of various power quality improvement techniques for power quality
enhancement in grid integration of a small-scale photovoltaic system is presented. Here, the overshoot and steady-
state error are compared with some methods such as PI, PID, Fuzzy-PI, and Fuzzy-PID controllers, in this Fuzzy-
PID achieves less steady-state error and overshoot. LSTM has a less Islanding detection time compared with a
decision tree, SVM, and ANN. Settling time is compared with various grid synchronization techniques, in this SRF
PLL, achieves a high settling time. Compared with various automated filters, Hybrid filters attain a low total
harmonic detection. DSTATCOM achieves a high reactive power. However, some errors occurred while navigating
complicated harmonics in grid integration of a small-scale photovoltaic system. Hence these techniques require
further improvement to perform efficient power quality enhancement without harmonics, less settling time,
overshoot, and steady-state error and detection time.
4. Result and Summary
The various techniques for power quality enhancement in grid integration of a small-scale photovoltaic system
have been analyzed in various directions such as PI-Based Reactive Power Control Systems, Flicker Logistic Control
Methods, Automated Filtering Mechanisms, Shunt Active Power Filter modules, Integrated Optimization-based AI
Technique and Grid Synchronization Techniques. The analyzed summary is given as follows:
 PI-Based Reactive Power Control Systems optimal fractional order PID controller, Fuzzy PID controller,
Fuzzy adaptive PI controller, Multi-stage fuzzy-based flexible controller, and Distributed power flow
controller were found to be helpful and effective in power quality enhancement, mitigating voltage sags,
voltage swells and ensured the lowest harmonic distortion. However, these approaches were found with
some limitations such as the Fuzzy controller’s computational burden and testing being a challenge for
real-time applications, unwanted oscillation happening in rapid changes also limited adaptability to a wide
range of scenarios.

 The flicker logistic control methods such as Unified Power Quality Conditioner, Dynamic voltage restorer,
Static Var compensator, Static synchronous compensator, and Distribution static compensator helped
reduce current and voltage instabilities and harmonic content of grid integration of small-scale
photovoltaic systems. However, some drawbacks such as power consumption increases and the cost of
batteries and associated power electronics make this approach too costly.
 The automated filters such as an LCL filter, Shunt hybrid active filter, Hybrid series active power filter,
Fractional order notch filter-based control, LC passive filters Recursive Digital Filter, and Hybrid harmonic
filter helped to reduce the total harmonic distortion and improve the power quality in grid integrated
small-scale photovoltaic systems. However, drawbacks such as its inability to adapt to dynamic real-world
conditions, poor stability, and voltage spikes that occur across the filter components.
 The various shunt active power filter modules such as Hybrid active power filter, Shunt Active Power Filter,
switched power filter compensator, and Three-phase half-bridge interleaved buck shunt active power
filters were studied to help improve power quality, reduce settling time, and effectively provide better
accuracy. However, some drawbacks such as low reactive power levels lead to voltage instability and the
ability to address voltage fluctuations is limited.
 The grid synchronization techniques such as Phase Locked Loop, DQ current control theory, Mixed third
and fourth-order complex filter, Vector-based synchronization, Adaptive feed-forward PLL, and lightweight
inertial PLL were studied to help improve quality to effectively increase the performance. However, these
approaches have some drawbacks, PLLs exhibit transient responses during sudden grid changes, lack of
robustness results in instability, and it requires rapid and accurate synchronization.
 Various Integrated Optimization-Based AI techniques such as Tunable Q-factor wavelet transform and an
Artificial neural network, Grey wolf-optimized Artificial neural network, Decision-tree-based fuzzy logic
controller, Multi-Feature-attention-LSTM, Statistical feature-based Deep neural network, Deep-learning
classifier based on LSTM, Adaboost algorithm, and convolutional neural network were found to be efficient
and reliable operations of the modern power network. However, these strategies had some limitations such
as in real-world scenarios complexity, effective decision-making is needed and reliable detection is difficult
for dynamic operation.
5. Conclusion
This review paper presented a comprehensive overview of the recent developments in various techniques for
power quality enhancement, including PI-Based Reactive Power Control Systems, Flicker Logistic Control Methods,
Automated Filtering Mechanisms, Shunt Active Power Filter modules, Integrated Optimization-based AI Technique
and Grid Synchronization Techniques. The review highlighted that power quality improvement methods can
significantly improve the performance of power quality enhancement in in grid integration of a small-scale
photovoltaic system. Overall, this review paper provides valuable insights into the recent developments in power
quality improvement methods for power quality enhancement. A comparison of the performance of various power
quality improvement methods in terms of harmonics, less settling time, overshoot, and steady-state error and
detection time for power quality enhancement in grid integration of a small-scale photovoltaic system has been
provided. The paper concludes that the integration of power quality improvement methods can enhance the
performance of power quality and ensure the stability and reliability of grid integration of a small-scale
photovoltaic system. The future research directions include addressing the challenges in implementing power
quality improvement methods and exploring new techniques to further improve the performance of power quality
enhancement in grid integration of a small-scale photovoltaic system.
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