Enhancing Controller Efficiency in Hybrid Power System Using
Interval Type 3 Fuzzy Controller with Bacterial Foraging
Optimization Algorithm
J. VINOTHKUMAR1*, R. THAMIZHSELVAN2
1Department of Electrical Engineering, Annamalai University, Annamalai Nagar,
Chidambaram, Tamil Nadu 608002, INDIA
2Department of Electrical Engineering, Annamalai University, Annamalai Nagar,
Chidambaram, Tamil Nadu 608002, INDIA
Abstract: Microgrids (MGs) are designed with the help of effective power extracted from
renewable sources such as rooftop solar panels, photovoltaic cells, batteries, floating PV and
solar PV with the grid. In a hybrid microgrid, Interlinking Converter (ILC) is a key component
to connect the AC sub-grid and DC sub-grid. DC-DC converters are being used as power
converters in between load and source to enforce and increase the PV depending on the voltage
output signal. Accordingly, the work focused on a Multi-Input (MI) KY boost converter. This
Proposed topology gathered maximum power using multi-input KY boost converters for hybrid
energy. This hybrid topology operates mainly delivered power from renewable energy sources
solar/wind to dc bus. In the absence of any one source, wind or solar supplies power to the dc
bus. Without any renewable energy, sources battery deliver the power to the dc bus. The
research proposed the interval type 3 fuzzy controller is used for controlling the load frequency
of the multi-area system. Swarm-based hybrid metaheuristic optimizer of the Bacterial
Foraging Optimization Algorithm (BFOA) is proposed for optimal tuning and controlling the
PI controller parameters. Controlling the reactive power of the hybrid power system model
with the aid of a Static Synchronous Compensator (STATCOM). A unique controller is
deployed to regulate the AC and DC currents of the STATCOM using two PI controllers. In
this paper effectiveness of the hybrid power system is simulated through
MATLAB/SIMULINK. The battery current and voltage of this produce 2000 A and 205 V,
grid voltage produced in this work is V and the power of the work produce
approximately 90 kW. The results show that the interlink converter improves the flexibility of
the hybrid microgrid and, in addition, the power quality of the energy supplied in the utility
grid is improved. In future, an intelligent control algorithm may be presented to improve the
control strategies of the HMG, respectively.
Keywords: Microgrids, Hybrid Metaheuristic Optimizer, Interlinking Converter, Fuzzy
Controller, Multi-Input KY Boost Converter, and STATCOM.
Received: September 18, 2022. Revised: May 21, 2023. Accepted: June 16, 2023. Published: July 17, 2023.
1. Introduction
To meet the significant rise in electrical
energy demand and address the pollution
issues brought on by the use of fossil fuels,
the integration of renewable energy sources
(RESs) into electrical systems has become
a required and crucial concern. Electricity
is produced using a variety of RESs, such
as wind, photovoltaic (PV), fuel cells, and
biomass. According to the annual growth
rate of RESs, wind and solar power are the
two RESs that are most frequently included
in electrical systems because of their
numerous benefits [1-2]. The best way to
establish microgrids is by using renewable
energy, especially in islands and
autonomous regions where they can be
placed close to areas of demand, reducing
the cost of installing conventional power
grids. Systems for generating renewable
energy might be either grid-connected or
island utilities. The problem of the
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indiscriminate character of renewable
energy resources, where these sources rely
on unexpected weather conditions that did
not have the full accuracy required to
produce energy, is typically overcome by
the use of more than one energy source [3].
In a lot of industry research, controllers are
commonly used. This aids in eliciting the
necessary answers from the various types of
controllers being employed in research.
Three different types of inverter controllers
are frequently employed in the current
study. When an automatic controller is
required to control a process, Proportional
Integral (PI) control is a sort of classic
control that is frequently employed in the
industry. An accurate mathematical model
of the system is required by the PI controller
used in wind turbine pitch control systems.
Here, the pitch angle controller is optimised
using a novel applicable optimization
approach [4]. Adaptive control is the term
used to describe the discontinuous control
method known as sliding mode control
(SMC). It is a reliable control technique.
SMC preserves trajectories on the sliding
surface and changing structure and is
composed of similar control [5-6].
There is excess power available when
generation is greater than the load required,
typically during the busiest weather
conditions. Storage units store the energy at
this time for a brief period. When the
amount of energy generated falls short of
the increase in load demand, this stored
energy is put to use. Integration in the
environment of fractional order (FO)
calculus for proportional-integral-
derivative (PID) controller and fuzzy
controller, referred to as FO-Fuzzy PID
controller tuned with the quasi-opposition-
based harmonic search algorithm, has been
proposed [7] to control the deviation in
frequency and power. To achieve great
efficiency, the intelligent multi-input multi-
output fuzzy controller has been presented
[8]. Even though these systems
demonstrated superior performance, it
might still be raised by using a potent
optimization strategy. A PID controller is
used in the system under study, and the
technique of moth flame optimization is
used to adjust its parameters [9]. A unique
approach based on the fusion of the
harmony search algorithm and the fuzzy
logic controller is proposed in [10]. This
technique, which is based on environmental
data, will determine the ideal size of a
hybrid energy system (load demand, solar
irradiation, and wind speed). Hybrid Wind-
Solar Energy Systems, which combine
wind and solar energy generating, have
been used in recent years to address the
intermittent nature of renewable energy
generation units. To increase the maximum
power tracking efficiency of grid-
connected wind and PV coupled to the
back-to-back converter DC link. The Grid
Side Converter and Rotor Side Converter
are controlled using Stator Flux-Oriented
control [11]. Power converters that connect
the AC and DC sub-grids in the hybrid
microgrid are used for the interfacing of
these grids, which is essential for ensuring
the stability of the entire microgrid. To
measure the power demand of the AC and
DC sub-grids in line with terminal AC bus
frequency and terminal DC bus voltage,
respectively, a bidirectional droop control
approach for the converter is presented.
Additionally, it establishes the amplitude
and direction of the power transmission
through the converter [12].
To maintain a power balance in a hybrid
microgrid while taking the source and load
variation into consideration, an iterative
learning controller is presented [13]. To
maintain constant dc bus voltage, the
primary or local level control is used with
solar and batteries. To deliver stable
voltage, ensure smooth grid
synchronisation, and ensure proper real
power sharing across dc/ac buses, the
secondary or system level control is
achieved by managing the interlinking
converter situated between the ac and dc
bus. Each component of the proposed
system is optimised by utilising
experimental design methodologies based
on the most affecting parameters to attain
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DOI: 10.37394/232030.2023.2.8
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the best performance and efficiency [14]. A
reliable and improved load frequency
control (LFC) approach is necessary for the
power system to run smoothly in the
aftermath of RESs intermittency and
continuously changing load demands. So,
to deal with the frequency abnormality
brought on by the existence of renewable
producing units in the current power
system, a cascade fuzzy non-integer FO-
PID control policy is developed [15]. In a
hybrid microgrid, an interlinking converter
(ILC) is a key component to connect the AC
subgrid and DC subgrid. Its control strategy
significantly affects power flow
management, power quality, system
efficiency and stability. Traditionally, ILC
is usually controlled as a current source,
resulting in poor dynamic stability when a
remote weak AC subgrid is connected by a
large linking impedance. The remaining
part of the work is organized as follows,
section 2 portrays the literature survey of
the study, and the research problem
definition and motivation are exposed in
section 3. The proposed research
methodology is disclosed in section 4,
section 5 elucidates the experimentation
and result discussion section, and section 6
reveals the conclusion of the research work.
2. Literature Survey
Globally, RES-based hybrid power systems
are being used more and more to cut carbon
emissions. However, RESs are sporadic and
incredibly unpredictable, which can lead to
significant frequency variations. By using
controllers that are ideally built, these
fluctuations can be kept within the
appropriate limits. An LFC structure based
on a tilt fraction-order integral (TI)
controller and an FO-PID controller with a
filter was proposed by Ahmed et al [16].
Artificial Gorilla Troops Optimizer is used
to create this proposed controller in the best
possible way. The impact of the large
penetration of renewable is also
investigated on the considered power
system with the proposed controller. The
performance of the system is improved in
[17] using the recently created improved
squirrel search algorithm (ISSA), which is
utilised to adjust the parameters of several
controllers, including the PID, two- and
three-degree PIDs, as well as cascaded
2DOF-PID fractional order integrals (FOI).
The performance of the optimal controller
was modified using integrated Particle
Swarm Optimization (PSO) and Squirrel
Search Algorithm (SSA). Numerous case
studies have been created to evaluate the
system's reliability, adaptability, and
flexibility.
In [18], a hybrid power system is
employed, which includes a thermal
system, wind, solar, diesel engine
generators, electric vehicles, and energy
storage systems such as batteries and
superconducting magnetic energy storage.
The frequency has been controlled via a
reliable FOPI frequency controller. The
Water Cycle Algorithm optimizes the FOPI
parameters. The objective function chosen
is the Integral Time Absolute Error (ITAE).
The results of the proposed algorithm are
compared with those of PSO, another well-
known optimization technique, to
demonstrate its effectiveness. By
maintaining a faster settling time, the
suggested optimization method performs
better than the PSO. To address the
frequency anomaly caused by the existence
of renewable generating units in [19], a
cascade fuzzy-noninteger FO with
proportional derivative with filter-
proportional integral (CFPDF-PI) control
policy is suggested. FPD-F is used as the
master and integer-order PI is used as the
slave in the CFPD-F-PI controller. To fine-
tune the controller parameters, the recently
developed slime mould algorithm is used as
a stochastic optimizer. The proposed
approach's ability in LFC and adaptability
for other real-world applications are
validated by various scenarios for the
robustness study.
To enhance the operation of Microgrids
(MG), increase system reliability, and
increase effective efficiency, which is a
vital characteristic, a new hybrid model is
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presented [20]. FLC-based EMS with
AC/DC microgrid implementation is done.
The power quality of the MG is increased
in addition to the EMS design. It suggests
analysing and managing storage devices.
The FLC extends battery life and produces
an ideal SoC. To reduce power quality
problems caused by nonlinear, unbalanced
load conditions, an FLC-based EMS grid-
integrated MG is implemented. For
automatic load frequency management
(ALFC) of a hybrid power system, a chaotic
search-based hybrid Sperm Swarm
Optimized-Gravitational Search Algorithm
is presented in [21]. The integral time
absolute error of the hybrid system is
minimized to produce the control
parameters of the PID controller for ALFC,
which controls the system's frequency.
With various power source combinations
linked to the system, the effectiveness of
the suggested technique is tested.
Additionally, a sensitivity analysis is
conducted to examine the suggested
technique's flexibility while taking into
account RES's real-time weather
intermittency and load change.
In [22], the innovative Fuzzy-PID+PID
hybrid controller that is employed for
frequency regulation in a hybrid power
system is optimised using the whale
optimization algorithm (WOA). At the
time, the suggested application of the WOA
approach for frequency regulation in HPS
may be considered unique. When compared
to other controllers, the suggested WOA
optimised Fuzzy-PID + PID hybrid
controller performs at the highest level,
demonstrating the effectiveness of WOA in
load frequency control experiments. Due to
the nature of the spatial and temporal
variability connected with renewable
energy resources and the large operation
uncertainties brought on by the changing
natural environment, the long-term optimal
operation of RES is a difficult challenge.
For the long-term operations of the RESs,
the stochastic model predictive control
(MPC) is conceived and implemented
based on probabilistic forecasting and
rolling stochastic optimization [24]. PV
energy generation systems have been rated
as a top energy system by power suppliers
all over the world due to the use of RES to
create electricity. These energy options
have the drawback of being unpredictable
and dependent on climate and weather
conditions. To execute noise-free voltage
stress reduction for DFIG and PV systems,
an effective strategy utilising a landsman
converter is designed [25]. The maximal
power tracking point is controlled using a
PWM-based PI controller that uses the
firefly method. A grid-synchronized 3-
phase VSI with an LC filter inverts the DC
voltage to AC while assuring smooth
operation and eliminating harmonics. The
outputs showed a minimum THD value of
1.8%.
3. Research Problem Definition
and Motivation
Today, the need to generate electricity from
clean and green resources has become a
necessity. Classic thermal power plants,
due to the use of fossil fuels, have polluted
the environment and destroyed many
natural resources. These serious concerns
have led researchers, policymakers, and
investors in energy to research and develop
power generation microgrids that reduce
dependence on fossil fuels and reduce
environmental impacts. Although these
resources have many benefits and are
sustainable, clean, and inexhaustible, they
have low efficiency because they have
significant limitations, such as variable
solar irradiance and fluctuating wind speed.
A combination of more than one resource
for power generation systems from
renewable energy resources or hybrid
renewable energy systems (HRES) is used
to overcome this problem. Furthermore, to
address this problem, it is necessary to
develop appropriate energy storage systems
for the HRES. Among the renewable
energy sources, biodiesel, a clean diesel
fuel producible renewable natural
resources, wind turbines (WTs) and
photovoltaic (PV) systems are often
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considered the most promising
technologies to meet electrical loads in
rural or remote regions. The use of different
renewable energy sources in supplying
power to HRESs increases the reliability of
the power generation system and,
consequently, requires fewer support units;
in other words, the ability to supply power
to the consuming power system increases
during the year.
The impact on the stability of power
systems is rising as the penetration level of
renewable energy with sporadic natures
rises rapidly on the grid. Transient stability
is the property of a power system to regain
its normal operating condition following
sudden and severe faults in the system. The
transient stability study is extremely
important for maintaining the continuity of
the power flow and properly controlling
modern electrical power systems with
multiple renewable energy sources
integrated into them. Some of the
mentioned FCLs and other auxiliary means
of enhancing stability incur additional costs
due to the use of converters, coupling
transformers, and filters. It is important to
investigate a cost-effective and new method
for transient stability improvement of
hybrid power systems. The existing
research has not studied the system
characteristics of the AC-DC hybrid grid
when it is integrated with a large number of
renewable energy technologies, and the
mutual coupling between DC and
renewable energy has not been considered
when evaluating the renewable energy
penetration capacity of the power grid.
Therefore, the renewable energy
penetration capacity evaluation results are
not fully applicable to the AC-DC hybrid
grid.
4. Proposed Research
Methodology
In recent years, smart grids and microgrids
are becoming important topics for energy
demand. The microgrid paves a way to
effectively integrate various sources of
distributed generation (DG), especially
renewable energy sources, and thus reduce
CO2 emissions. However, it must be
considered that increasing levels of
penetration of DGs may cause severe phase
voltage imbalance, resulting in a larger
ground current and descending power
quality. An improved fuzzy-based energy
management strategy is proposed for a
hybrid power system with multiple power
sources consisting of Rooftop solar
panels/photovoltaic cells (PV)/battery
(BAT)/floating PV/ solar PV with the grid.
The power demand from the propeller and
user terminal is afforded by the power
sources connecting to power converters.
The high and stable power satisfies the
power demand even under extreme work
conditions. Figure 1 illustrates the flow
diagram of the proposed work.
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Figure 1: Flow Diagram of the Proposed Work
Nowadays, the power system dynamics
have been considerably influenced because
of the growing renewable power
penetrations. A microgrid is a small-scale
electrical system composed of distributed
generation (DG) and energy storage devices
(ESD) technologies, with the aiming to
meet the demand of local loads. These
devices, acting together, allow the
microgrid to operate in both connected and
standalone modes. Such aspects increase
the system's versatility and power flow
possibilities, which improve the efficiency,
reliability, and quality of the energy
supplied. According to the sources and
loads connected to them, the microgrids can
be classified as AC microgrid, DC
microgrid, and hybrid AC/DC microgrid.
4.1 Modelling of AC/DC Microgrid
Microgrids have become an attractive
option for distributed generation (DG) with
the increase in Renewable Energy Sources
(RES) and storage systems. Microgrids
(MGs) are designed with the help of
effective power extracted from renewable
sources such as Rooftop solar panels,
Photovoltaic cells, batteries, floating PV
and solar PV with the grid. Furthermore,
particularly when paired with renewable
generators, batteries help provide reliable
and cheaper electricity in isolated grids and
off-grid communities. Lithium-ion batteries
were chosen for this study due to their high
energy density, long life cycle, and high
efficiency. The existence of both AC and
DC microgrids has led to a new concept of
hybrid AC/DC microgrids which consists
of both AC and DC grids tied by an
Interlinking Converter (ILC).
Rooftop Solar Power Plant:
Appropriate sizing of the rooftop PV
system in terms of the number of PV panels
and the sizing of the battery are important
aspects of the design. In this section, we
present a mathematical model, in which we
develop the expressions for sizing and the
number of solar panels and batteries as a
function of load requirement. The model
includes both technical and economic
analysis.
Photovoltaic Cells: Solar Photovoltaic
(PV) cells generate electricity by absorbing
sunlight and using that light energy to
create an electrical current. There are many
PV cells within a single solar panel, and the
current created by all of the cells together
adds up to enough electricity to help power
your school, home and businesses.
Floating PV System: A developed PV
floating power generation results from the
combination of PV plant technology and
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floating technology. This fusion is a new
concept for technology development. As a
new generation technology, it can replace
the existing PV plants that are installed on
top of woodland, farmland and buildings.
The PV floating plant consists of a floating
system, mooring system, PV system and
underwater cables.
Grid-Connected Photovoltaic System:
Grid-connected solar PV (GCPV) systems,
floating PV, rooftop PV, and PV cells
include building integrated PV (BIPV)
systems and terrestrial PV (TPV) systems.
TPV systems include plants in deserts and
tides. Analysis of optimal photovoltaic
(PV) array and inverter sizes for a grid-
connected PV system. The inverters and
DC-DC converters or DC-AC converters
are used for power conversion. However,
hybrid AC/DC microgrids are presented in
the following section.
4.1.1 Hybrid AC/DC Microgrid
Description
The hybrid AC/DC microgrid topology
studied in this paper comprises distributed
generation, electrical loads, an energy
storage device, and the utility grid. Each
one of these elements is detailed in the
following subsections.
Distributed Generation
The DG of the microgrid is composed of
RES, such as photovoltaic and wind
generation. These primary sources behave
according to the profile of solar irradiation
and wind speed. The electric current
generated by the RES does not meet the
voltage and frequency requirements of the
power grid; thus, power electronic
converters are employed to interface the
connection between the DG and the
microgrid. Such converters can be
represented by a current source. In the
analyzed hybrid microgrid, the photovoltaic
and the wind generation are defined as a DC
source and AC source, respectively, as
detailed hereafter.
DC Source: The DC source represents a
set of photovoltaic modules connected to a
DC-DC converter operating with
Maximum Power Point Tracking (MPPT).
To simplify the analyses, such a system is
approximated by an ideal current source
injecting power into the microgrid’s DC
side.
AC Source: Wind generation is an
example of RES as an AC source. In
general, these systems consist of a wind
turbine associated with a back-to-back
converter, which allows injecting the power
generated into the utility grid. In a
simplified way, the wind system is
represented by a controlled three-phase AC
source connected to the AC microgrid.
Loads
DC Load: Devices that consume direct
currents, such as DC machines, electric
vehicles, and many electronic devices, are
defined as DC loads. Similarly to the DC
source, the DC load is represented as a
controlled direct current source, but
functions by absorbing power from the
microgrid.
AC Load: The AC loads are represented
by sets of impedance, composed of
balanced three-phase resistors and
inductances in series. Thus, these AC loads
consume the active and reactive power of
the AC microgrids (ACMG). The input and
output of such loads occur according to the
desired power profiles.
Nonlinear Load: The nonlinear load
represents devices that distort the electrical
current waveform, contaminating the grid
with harmonic content. Such loads are
modelled as a diode rectifier bridge with a
resistive load and inductive filter at the
input. The distortion in the waveform
occurs due to the harmonic components
added to the fundamental sinusoidal
current. These harmonic distortions cause
issues in the utility grid, thereby affecting
the power quality of the energy supplied by
the system.
Energy Storage Device
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Among different existing energy storage
technologies, Li-ion batteries are chosen as
ESD in this work. The dynamic behaviour
of such ESD is based on the Shepherd
model, which represents the battery as a
controlled DC source in series with internal
resistance.
It is possible to describe, in a simplified
way, the output voltage of the battery bank,
according to equation (1).
(1)
Where, and are the output and
internal battery voltage, respectively;
is the internal resistance, and is the
battery's electrical current.
Utility Grid
Despite the main aspect of a microgrid
being the possibility of operating in the
standalone or grid-connected mode, this
paper focuses exclusively on studying the
grid-connected operation. In this study, the
utility grid is implemented by a balanced,
60 Hz, three-phase, 220 V RMS, phase-to-
phase AC source. Each voltage source
represents a phase, with 120o of phase-shift
between each phase, in the positive
sequence. RL impedances are employed to
characterize the conductors’ dynamic
effect, emulating the transmission line
impedances. The value of these impedances
is determined according to conventional
short-circuit power for electrical
distribution systems.
ILC Control
The ILC is the device that connects and
promotes the bidirectional power flow
between the DCMG and ACMG. The main
function of such a converter is to form the
DCMG and perform the power-sharing
inside the HMG. Two control loops are
employed to drive the ILC. The internal
loop controls the current in the L filter,
while the outer loop controls the voltage on
the DC-link capacitors.
From the active and reactive power
references, the current references are
established for axis, respectively, as:
(2)
(3)
Where, and are the current
references axis, respectively; and
are the active and reactive power
references, respectively. These equations
are the same for both ILC and VSCa
devices.
The PI controllers employed on the ILC
are designed by frequency response by
setting the crossing frequency and the phase
margin. The ILC control strategy has the
objective to compensate the reactive power
into the utility grid and apply a power-
sharing by droop technique.
4.2 Converter
In a hybrid microgrid, Interlinking
Converter (ILC) is a key component to
connect the AC subgrid and DC subgrid. Its
control strategy significantly affects power
flow management, power quality, system
efficiency and stability. It comprises a DC
grid and an AC grid interlinked by a multi-
objective control scheme of a bidirectional
DC/AC converter. Such a hybrid AC/DC
microgrid has the advantages of both AC
and DC with increased efficiency and less
cost. The primary or local level control is
implemented for solar and battery for
maintaining stable dc bus voltage. DC-DC
converters are being used as power
converters in between load and source to
enforce and increase the PV depending on
the voltage output signal. Further, the work
is focused to build a new boost converter of
Multi-Input (MI) KY boost converter. The
secondary or system level control is
implemented by controlling the interlinking
converter placed between ac and dc buses
to supply stable voltage along with the
smooth grid synchronization and ensuring
the proper real power sharing between
dc/ac buses. Additionally, the ILC control
prevents any negative sequence currents
from entering the AC source.
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Volume 2, 2023
4.2.1 Multi-Objectives Control
Framework
In this section, a study about the design of
the proposed two-stage control framework
is exposed for the stable and voltage-
tracking operation of the HMG over the
variable generation and load changes. For
the reliable operation of the HMG, the
following points need to be met leastwise
while designing the control framework.
Maintain stable DC bus voltage for the
variation of temperature and irradiance.
Require to incorporate the precise
converter control model to assist the DC
bus bar in reaching fast transient
performance against the varying generation
conditions.
Control the inverter accurately
associated with the load network to
minimize the mismatch voltage with the
corresponding reference voltage set by the
consumer.
Implement a robust control method
in the inverter associated with the load
network to deal with high performance over
the external uncertainties produced due to
changes in load dynamics.
To require a synchronised operation
between the AC and DC side of the HMGs
so as proper power sharing among the
primary grid and DC side can be made.
The design process of the proposed
controller has been divided into two stages
to deal with multiple objectives over the
above-mentioned challenges. The first
control stage designed via the partial
feedback linearization method addresses
the first pair of mentioned challenges,
whereas the second stage deals with the
next two pairs. Additionally, a VSC
controlled by the PI controller is
implemented to maintain the operational
synchronization between the DC side and
AC side of the HMG.
4.2.2 Bidirectional DC–AC Converter
Topology
To validate the fuzzy logic-based control
technique implemented in this proposed
HMG, we had to design a bidirectional DC–
AC converter that was capable of
functioning as both an inverter and a PFC
rectifier. The transition between these two
modes of operation needed to be fully
automated and without human intervention
for our HMG to autonomously store,
produce and supply energy for domestic
use.
Before validating its operation by
appropriate experimental measurements, it
is essential to detail the operating modes of
the converter, as well as its control
strategies.
Proposed Topology and Details of Its
Operating Modes
The energy transfer between a DC voltage
source and an AC voltage source, and vice
versa, was the basis of this structure. The
association in series of a DC–DC stage and
a DC–AC stage ensured this principle of
operation with these two stages being
necessarily bidirectional.
The DC–DC stage needed to generate a
rectified sine wave from the PWM
command of the power switches. Since the
power devices in this stage switched at a
frequency of a few hundred kilohertz to
optimize the compactness of the whole
converter, the inductance, noted L1 was
sized so that the ripple of the current was
negligible compared to the sinusoidal
component at low frequency (in this case,
50 Hz). Therefore, the DC–DC converter
acted as a controllable output voltage
source.
By changing the voltage of a modulation
stage, the output current could be regulated.
This is very interesting, especially when the
output current is strongly reduced or in the
of variable DC voltage. This strategy
allowed the output voltage of the DC–DC
stage to be modulated. Specifically, when
this modulated voltage was higher than the
mains voltage, the output current had a
positive value. In the opposite case, the
output current was negative.
The design approach that was chosen
allowed the converter to be used in both
grid-connected and off-grid modes. Since
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the microcontroller was synchronized with
the AC grid to sensibly drive the DC–DC
and DC–AC stages, only the grid-
connected mode will be discussed in the
remainder of this paper.
The DC–AC stage was responsible for
inverting every other sinusoidal half-wave
to obtain a full sinusoidal output signal.
Compared to existing voltage source
converter topologies, such as multilevel
structures, the coupling of a DC–DC stage
with an H-bridge has many advantages:
The standard DC–DC converter and
the H-bridge are two very common and
mastered topologies;
Many H-bridge topologies are
composed of four or more power
components that switch at high frequencies.
In our proposed architecture, only those in
the DC–DC stage (see transistors and
) operated at high frequency, i.e., 150
kHz implemented here. In the DC–AC
stage, all components (see components ,
, and ) switched at low frequency,
i.e., 50 Hz;
When switching at high frequency,
it is imperative to take into account the
delay between the two switching operations
in the same branch for safety reasons. Here,
the safety delay was easier to regulate since
only one stage operated at high frequency;
In our architecture, the capacitance
used at high frequency to modulate the
voltage was small (about 10 µF).
Control Strategies
In this section of the paper, we will describe
the control strategies of the proposed
bidirectional DC–AC converter. We will
only give the principles and thus, we will
not detail the control circuit or the AC
network connection strategy because
several patents are pending.
The DC–AC stage was controlled by the
very same signal as the inverter mode, so it
was synchronized with the AC grid at a
switching frequency of 50 Hz
4.2.3 Multi-Input (MI) KY Boost
Converter
As shown in figure 2 (a), a multi-input
converter is a circuit structure that
integrates different input voltage sources
with different voltage levels and supplies an
output dc load. According to the principles
of circuits and systems, a syncretization of
dc-dc converters depends on whether it
consists of a pulsating current source or
pulsating voltage source. In the case of
pulsating current sources such as boost
converters, these modules need to be
connected in parallel to supply the load.
Figure 2 (a) and figure 2(b) respectively
show this principle and a circuit example in
boost converters.
Figure 2: Derivation of Multi-Input DC-DC Converters
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The main part of the proposed system is
the multi-input KY boost dc–dc converter
that is linked to two renewable energy
sources. DC–DC KY boost converter
connected between energy sources and the
dc bus. Multi-input KY boost converter
system architecture of a solar array
system/wind system. The maximum power
point (MPP) tracking of both renewable
energy sources, is accomplished with two
controllable switches. The integration of a
synchronously rectified boost converter, a
KY converter and coupled inductor,
modified KY boost converter is presented
here. The coupling inductor improves the
voltage gain.
The corresponding voltage gain can be
expressed as:
(4)
Where,
In this paper, the design parameters are
the inductance and capacitance are chosen
as ,
and A . ,
, the voltage conversion ratio is set at
, frequency 100 kHz.
MPPT algorithm is developed here is the
P&O algorithm. Hybrid energy of wind and
solar incorporates the duty cycle variation
also. Sudden change in the wind and solar
output power the dc bus voltage is preset,
resulting in a change in the output current
of the KY boost converter fed to the dc bus.
A current sensor senses the current and
compares it with the previous value. The
duty ratio of the KY boost converter hence
increases or decreases. Sensing wind speed
and solar insolation by adjusting the duty
ratio of the KY boost converter and
continuously monitoring the parameters. In
all-day shifts, the maximum power of the
PV module and wind is extracted using the
Maximum Power Point Tracking (MMPT)
technique. To gain maximum power from
both renewable energy the perturb and
observe method is implemented.
To obtain maximum power operating
voltage or current of the panel is modified.
If the voltage to a cell increases the power
of a cell, decreases. When the power output
begins to decrease the corresponding
operating voltage begins to decrease. Once
this situation exists, the voltage is decreased
to set back to the maximum power value.
This procedure persists until the maximum
power point is reached. Thus, the power
value fluctuates around a maximum power
value until it settles.
This algorithm mainly focused on
operating voltage and the corresponding
deviation of power is noted. If the
difference in power is positive the future
perturbation should follow the same to
obtain MPP. If the deviation in power is
negative the perturbation should be
reversed and move reverse in direction to
obtain MPP. This procedure continues until
it achieves maximum power.
4.3 Controller
The voltage and frequency of MGs are
strongly impressionable from the active and
reactive load fluctuations. A change in load
leads to an imbalance between generation
and consumption. The output voltage and
frequency of the DGs are primarily
controlled by the droop characteristics. But,
in case of severe changes in load, the DGs
may be failed and the Microgrid is
collapsed. Subsequently, the research
proposed the interval type 3 fuzzy
controller is used for controlling the load
frequency of the multi-area system. A 1%
step load perturbation is applied to the load
demand of the power plant and analysis of
the system responses in terms of settling
time, peak overshoot and peak undershoot.
Henceforth, it is necessary to maintain the
system frequency to be constant.
Consequently, the research proposed a
swarm-based hybrid metaheuristic
optimizer of the Bacterial Foraging
Optimization Algorithm (BFOA) which
optimally tunes and controls the PI
controller parameters. However, most
fuzzy controllers are static in that they
respond only to current input, so they may
not offer any improvement over the
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dynamic nature of designs. By replacing
nonlinear and time-varying aspects of a
neural network with uncertainties, a robust
reinforcement learning procedure results
that are guaranteed to remain stable even as
the neural network is being trained. The
behaviour of this procedure is demonstrated
and analyzed on two simple control tasks.
4.3.1 Interval Type 3 Fuzzy Controller
The interval type-3 FLSs (IT3-FLSs) are
developed to handle more levels of
uncertainty. In this study, IT3-FLSs are
used for online dynamic identification. The
structure of IT3-FLS is explained step-by-
step below:
The inputs of IT3-FLSs are ,
, , where, and
are the currents of PV and battery,
respectively and is the load voltage.
represents the sample time.
For each input, two Gaussian
membership functions (MFs) are
considered. The centres of MFs are set to
the upper and lower bounds of each input.
For input ,
, respectively. Similarly,
for input , one has: the upper and lower
memberships are obtained as:
(5)
(6)
(7)
(8)
Where, is the level of the horizontal
slice.
and
are the first and second
MFs for input .
and
are the
centres of
and
, respectively.
and
are the standard
division for the upper/lower bounds of
and
, respectively. Similarly, for input
, one has:
(9)
(10)
(11)
(12)
Where,
and
are the first and
second MFs for input Ib.
and
are
the centres of
and
, respectively.
and
are the
standard division for the upper/lower
bounds of
and
, respectively.
Finally, for input , one has:
(13)
(14)
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(15)
(16)
Where,
and
are the first and
second MFs for input .
and
are
the centres of
and
, respectively.
and
are the
standard division for the upper/lower
bounds of
and
, respectively. 3) The
output of and are:
(17)
(18)
Where, and are:
(19)
(20)
Where, and are the parameters
of −th rule for −th IT3-FLS and
represents the number of rules. and
are:
,
(21)
,
(22)
Where, is the number of horizontal
slices and:
,
(23)
The rules are written as:
Rule #1: If is
and is
and is
Then
Rule #2: If is
and is
and is
Then
Rule #3: If is
and is
and is
Then
Rule #4: If is
and is
and is
Then
Rule #5: If is
and is
and is
Then
Rule #6: If is
and is
and is
Then
Rule #7: If is
and is
and is
Then
Rule #8: If is
and is
and is
Then
In the type-3 MFs, the secondary
membership is not a crisp value but it is a
fuzzy set. Also a horizontal slice of a level
is equal with two slices at levels
and in type-2 counterpart.
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4.3.2 Swarm-Based Hybrid
Metaheuristic Optimizer
In BFOA, chemotaxis is the ground of the
local search, and reproduction accelerates
convergence speed. Elimination-dispersal
loops avoid immature convergence and
advance the search in the direction of the
global maxima via elimination-dispersal
loops. But chemotaxis and reproductions
are insufficient to trace the global maxima
as the bacteria may be trapped in local
maxima as the dispersion occurs after
several reproduction stages. In BFOA, there
is a feasibility to avoid being stuck in local
optima by incorporating mutation. The
mutation leads to a diverse population to
obviate the possibility of an immature
convergence and simultaneously avoids
being trapped in local maxima.
In BFOA, the run length in every
generation has a key role in the occurrence
and convergence to the global optimum
value. Because of the fixed step size in
BFOA, the algorithm bears mainly two
problems:
If the run length is kept very small, then
this leads to a rise in the generation count to
reach the global maxima, i.e., the iterations
required will be increased for getting the
optimal solution. An increase in run length
results in a faster convergence rate, i.e., the
bacteria reach the global optima swiftly, but
the accuracy is compromised.
In the proposed paper, a unique
approach for the position update of bacteria
is used to aggravate the convergence rate
and precision of the algorithm. The first
step includes the update in the bacteria
position after evaluating the fitness value
for the generation. In a later stage, the
mutation with PSO parameters is
introduced to tune the diversity in the
bacterium position update. PSO parameters
are independent parameters such as inertia,
mass, weight, or accelerating coefficient for
the best tuning to achieve the global
optimum value. In the hybrid PSO-BFOA
algorithm, any local search is performed
using chemotaxis, whether the global
search is accomplished using reproduction
and mutation.
The role of the mutation operator is
summarized as:
On the completion of the chemotaxis
process, the bacterium positions are
updated using mutation to reach the global
maxima. The mutation performs a crucial
part in the fine-tuning of the PSO-BFOA
algorithm and simultaneously maintaining
precision in achieving the optimal solution.
Initially, the ratio of global and
becomes very less that results in a wide run
length, but in a later stage, the run length is
minimized as is very close to the
global . Because of the increase in the
number of generations, the bacterium will
be captivated towards the global optima. At
this stage, is updated as:
. Where, is the
bacterium position in the search space, , ,
and stand for the number of bacteria,
chemotaxis step, and reproduction step,
respectively.
is the local optimal position,
represents the global optimal
position, and , are the random
variables.
The Hybrid PSO-BFOA algorithm is
summarized as:
1. Initialization of the Parameters ,
, , .
2. Restore the following parameters
(24)
(25)
Where, represents the fitness value,
and stands for local and global
fitness values, respectively
3. Reproduction loop:
4. Chemotaxis loop:
(a) Calculate the fitness value
for and accordingly,
restore and
(b) Tumble: random vector generation
for every element
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, a random
variable
(c) Evaluate for
(26)
(d) Swim
(i) Let (counter for swim
length)
(ii) While
Let
Evaluate the fitness function
for Restore
If (if
performing better) then
and
Use the equation for to
calculate the new .
Else, let .
(e) Mutation: Change the positions of the
bacterium by mutation. Compare for
(27)
5. If , jump to step 4 and
continue chemotaxis, as the lives of the
bacterium are not completed.
6. The bacteria with the
optimum fitness value dies and other
bacteria have the best value splits. Restore
and .
7. If , go to step 3. Instead,
stop.
The PSO Fitness estimation approach
for solving complex computational
problems has already been formulated.
Standard PSO assumes that each particle
possesses a certain position and velocity.
Every position is a trial solution to the
problem, which is optimized. The
movement is determined by the velocity.
Hence, the flying particle here is the swarm
search of the domain. The velocity and
position of a particle flying from its original
position to a different position are updated
using the relation:
(28)
(29)
Where, and are the th
particles velocity and position at th
iteration; and represents the th
particle’s optimal position observed so far
and the globally observed optimal position
of all the particles up to iteration .
reflects the inertia weight; , stands for
cognitive and social parameters. , are
diagonal matrices, where the diagonal
entries lie in the range of .
Since in the proposed hybrid
metaheuristic optimizer, PSOBFOA, the
mutation with PSO parameters helps to
trace the global optima; hence the fitness
estimation approach of the proposed hybrid
PSO-BFOA optimizer is considered similar
to that of the PSO optimizer.
4.4 System Stability with Penetration
Level Evaluation
Power system stability is an important area
of concern in modern interconnected power
systems. It is termed as the capability of a
power system to become stable after the
removal of disturbances. While an unstable
system loses its control by falling out of
synchronism, this phenomenon may have a
catastrophic impact on the smooth running
of the power system. In this study, a new
reactive power control strategy is employed
for optimization of the reactive power along
with the stability improvement of the
system under different small perturbed
conditions. Therefore, this study focuses on
controlling the reactive power of the hybrid
power system model with the aid of a Static
Synchronous Compensator (STATCOM).
Further, evaluate the renewable energy
resources’ penetration limit of the AC-DC
hybrid grid, which considers both economy
and safety. The static stability indicators are
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considered for an evaluation of the
penetration limit of RES.
4.4.1 Static Synchronous Compensator
STATCOM is developed on the solid-state-
based synchronous source of voltage which
replicates a synchronous machine of ideal
nature. It generates a set of 3-phase
balanced sinusoidal voltages at the
fundamental frequency by continuous
controlling of phase angle and amplitude.
The schematic of the STATCOM and its
equivalent structure has been given in
figure 3. Figure 3 illustrates the voltage
source converter, DC capacitor, and
coupling transformer. The real part of the
STATCOM controller current is
insignificant and considered zero. The
reactive current can be controlled by the
variation of and .
VSC
Vdc
Figure 3: Schematic Diagram for STATCOM Configuration
In this paper, is the STATCOM’s
fundamental output voltage () phase
angle and is the phase angle of the system
bus voltage, where the STATCOM is
connected. The amplitude of the
converter’s fundamental output voltage is
where represents DC voltage
developed in between the DC capacitor.
The STATCOM controller injecting the
reactive power to the connected bus has
been given as in equation (30).
(30)
In the considered HPS, bus voltage is
taken as a reference voltage; so, the bus
angle is zero. In the above equation, the
term is also insignificant, because
characterizes the admittance of the
step-down transformer. Thus, equation (30)
now becomes equation (31), considering
and as zero.
(31)
Here, in equation (31), and , are the
variable terms on which the reactive power
depends; under the small perturbations, the
change in reactive power of STATCOM
can be written as equation (32).
(32)
Where, and
.
It evaluates the renewable energy
resources’ penetration limit of the AC-DC
hybrid grid, which considers both economy
and safety. The static stability indicators are
considered for an evaluation of the
penetration limit of RES.
5. Experimentation and Result
Discussion
The HMG is simulated using the
Matlab/Simulink software to evaluate the
performance of the proposed hybrid power
system employed on the modified ILC. This
evaluation consists of the power flow
analyses and power quality factors at the
DC-link of the DCMG, and at the utility
grid. The loads and sources are rated up to
4.5 kW. The interlinking converters are
designed for 3 kW of nominal power, which
allows an adequate hybrid power system
operation. The inductors and capacitors are
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designed considering the power converters'
voltage ripple and nominal power.
Figure 4: Simulation Model for Hybrid Renewable Energy Resources
The simulation model for hybrid
renewable energy resources is presented in
figure 4, which was done in
MATLAB/SIMULINK environment. In
Hybrid PV System, the PV system acts as a
main source. Renewable energy resources
are connected in parallel and the across this
parallel combination, more than 30 V
battery is connected which is in charging
mode. If the voltage across this parallel
combination is less than 30 V, the battery is
in discharging mode. If the battery is only
present in the circuit, the percentage semi-
oxide concentration linearly decreases and
battery voltage rapidly decreases. The main
blocks in the above Simulink diagram are
the PV model block, type 3 fuzzy model,
governor, turbine, MPPT block, DC/DC
converter block, Battery model and power
system. The optimum power control and
pitch angle control act as prime movers for
the induction generator. The external inputs
to the turbine are wind speed and rotor
speed. The grid side converter's main
objective is to regulate the DC link
capacitor voltage and this converter
controls the power flow between the DC
bus and the AC side.
Table 1: Simulation System Configuration
Simulation System Configuration
MATLAB Simulink
Version R2021a
Operation System
Windows 10 Home
Memory Capacity
6GB DDR3
Processor
Intel Core i5 @ 3.5GHz
Simulation Time
10.190 seconds
The simulation system configuration of
the proposed work is portrayed in table 1.
Subsequently, the proposed technique is
evaluated and tested under the Matlab
R2021a software. The proposed work
operates under windows 10 home and its
memory capacity is 6GB DDR3.
Additionally, it utilizes an Intel Core i5 @
3.5GHz processor and the simulation time
of the work is 10.190 seconds.
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(a) Current
(b) Voltage
Figure 5: Current and Voltage Graph for Battery
The current and voltage graph for the
battery is presented in figure 5. It produces
a battery current of approximately 2000 A
for 1 second. However, the voltage
produced due to the battery is
approximately 205 V for time 1 second.
The input charging current to the battery
stack in a photovoltaic hybrid solution will
not be constant due to the atmospheric
conditions and the time of day.
(a) Current
(b) Voltage
Figure 6: Grid Current and Grid Voltage
Figure 6 reveals the grid current and grid
voltage. The grid current is regulated to its
nominal value and is in phase with the grid
voltage as can be seen in figure 9. It shows
the high quality of the current injected into
the grid with the proposed current control
scheme. This result meets the requirements
of the grid-connection standard. The grid
voltage produces v. The grid
voltage harmonic disturbances are rejected
by the robust ADRC controller, leading to
much better performance.
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Figure 7: Power for HMG
The power graph for the proposed hybrid
power system is presented in figure 7. It
produces approximately 90 kW of power
generated from hybrid power systems. This
power value is measured based on the time
seconds. This reading of power was
measured when the time was 0 to 1 second,
respectively.
(a) Voltage for PV Panel
(b) Voltage for VDC
Figure 8: Output Voltage Graph
Figure 8 (a) represents the PV panel of
output voltage. By using an array
combination the maximum output voltage
is =325 volts (65 *5). The PV panel
maintains the constant DC voltage (325
volts) shown in this figure. However, figure
(b) portrays the voltage of the Vdc graph,
which produces a voltage of 670 v for the
time of 1 second. But the maximum voltage
produced is 820 v, respectively.
Figure 9: Comparison Graph for Overshoot Voltage
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Figure 9 depicts the comparison graph
for the overshoot voltage of converters. It
shows the output voltage waveforms with
an overshoot of different converters'
performance like P&O, FLC, and proposed
converter. The maximum allowed variation
for the output voltage during load transient
is calculated in this figure. These
perturbations are limited by the joint action
of the output capacitor and the feedback
control: the former limits the initial
overshoot caused by a sudden change in
load current, whereas the controller
behaviour depends on the control circuit
being used. From this figure, the existing
P&O method is unstable, and the FLC is
stable but the proposed method is more
stable than these existing techniques.
Figure 10: Comparison Graph for Efficiency of the Converters
The efficiency graph for the proposed
method is compared with the existing
SEPIC and Cuk converters and is presented
in figure 10. While compare to these
existing converters, the proposed
converters produce higher efficiency that is
the efficiency of the proposed technique is
91.3%, but the other existing converters
SEPIC and Cuk produce lower efficiency
values of 89.16 and 89.51, respectively,
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Figure 11: Comparison Graph for Limits of Controller
The limits of the controller compared to
the existing PID and FLC controllers are
presented in this figure 11. The figure
depicts that the limits of the PID controller
are 0.01, and the limits of FLC controllers
are 0.0027, however, the proposed method
produces lower controller limits of 0.0013,
respectively.
Figure 12: Efficiency Graph for Controllers
Figure 12 reveals the comparison graph
for the controllers’ efficiency graph. The
proposed efficiency is compared with the
existing KGOA FOPID and ChASO
FOPID controllers. The efficiency is
evaluated based on the supply voltage from
50V to 300V. This figure depicted that the
proposed method produces higher
efficiency values of 98.2%.
Figure 13: Comparison of DC Bus Voltage
Figure 13 reveals the comparison graph
for DC bus voltages, the proposed IT3-FLS
controller is compared with the existing
Fuzzy-PID controller. It can be observed
that the error signal contains only the DC
component due to the cancellation of the
ripple component in the measured DC bus
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voltage. Consequently, the line current
waveform is nearly sinusoidal.
Figure 14: Comparison Graph for Dynamic Response of Controllers
Figure 14 reveals the comparison graph
of the dynamic response of different
controllers. The proposed Interval Type-3
FLC is compared with the existing methods
like Type-1 FLC, and Type-2 FLC. It
evaluates the proposed method and knows
the dynamic performance of the control
system. From this figure, the proposed
method produces higher output dynamic
response.
Figure 15: Comparison Graph for Voltage Conversion Ratio
Figure 15 portrays the comparison graph
for the voltage conversion ratio, the
proposed MIB-KYBC converter is
compared with the existing MIB converter.
The proposed converter has the voltage
conversion ratio among these converters
under different duty cycles, it can be seen
that the proposed step-up converter
possesses the highest voltage conversion
ratio. The voltage conversion ratio of the
proposed MIB-KYBC converter is up to
47.5 at the duty cycle is 0.47, respectively.
6. Research Conclusion
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A microgrid is a small-scale electrical
system composed of distributed generation
(DG) and energy storage devices (ESD)
technologies, with the aiming to meet the
demand of local loads. In this work, a
Multi-Input (MI) KY boost converter and
type-3 fuzzy controller are proposed,
Where, Swarm-based hybrid metaheuristic
optimizer of the Bacterial Foraging
Optimization Algorithm (BFOA) is
proposed for optimally tuning and
controlling the PI controller parameters.
Controlling the reactive power of the hybrid
power system model with the aid of a Static
Synchronous Compensator (STATCOM).
A hybrid microgrid comprising solar,
battery, utility grid, and AC and DC loads
is developed with ac and dc bus in
MATLAB Simulink environment. The
results demonstrate the superior
performance of the proposed strategy based
on the algorithm compared to the adaptive
PI (API) controller in terms of fast response
and tight regulation of power flow, voltage
and frequency under different conditions.
Compared to API, it has been found that the
proposed controller results in more
improvement of the dc-side voltage within
the operating frequency range of the ac-side
of the ILC. Accordingly, the penetration
levels parameters are compared with the
converter and the proposed control
technique is to find the efficiency of the
hybrid power system. The grid voltage
produces v, the battery current
produces approximately 2000 A for 1
second, and the voltage produced due to the
battery is approximately 205 V for time 1
second. However, the obtained power-
sharing reduces the energy supplied by the
power grid, thereby avoiding concentrating
the demanded effort in the battery system.
The power quality analysis verified the
effectiveness of the interlinking converters
by mitigating harmonic distortions and
reactive power for all the established
operation conditions. In addition, it was
noted the satisfactory performance of the
voltage regulation, achieved by a
hierarchical control, and applied to correct
the voltage deviation caused by the droop
technique. These results showed the
capacity of the proposed modified
interlinking converters to manage the
power flow at all hybrid microgrid
operating conditions, besides providing the
desired ancillary services. In future, studies
deep uncertainty study and stability
analysis are presented, consequently, also
presented the development of an intelligent
control algorithm for the control of the
HMG, respectively.
References
[1] Mosaad, M.I., Ramadan, H.S.M.,
Aljohani, M., El-Naggar, M.F. and
Ghoneim, S.S., 2021. Near-optimal PI
controllers of STATCOM for efficient
hybrid renewable power system. IEEE
Access, 9, pp.34119-34130.
[2] Munisamy, V. and Sundarajan, R.S.,
2021. Hybrid technique for load
frequency control of renewable energy
sources with unified power flow
controller and energy storage
integration. International Journal of
Energy Research, 45(12), pp.17834-
17857.
[3] Samy, M.M., Elkhouly, H.I. and
Barakat, S., 2021. Multi‐objective
optimization of hybrid renewable
energy system based on biomass and
fuel cells. International Journal of
energy research, 45(6), pp.8214-8230.
[4] Razmjooy, N., Estrela, V.V., Padilha,
R. and Monteiro, A.C.B., 2021. World
cup optimization algorithm:
Application for optimal control of pitch
angle in hybrid renewable PV/wind
energy system. In Metaheuristics and
Optimization in Computer and
Electrical Engineering (pp. 25-47).
Springer, Cham.
[5] Elnozahy, A., Yousef, A.M., Abo-
Elyousr, F.K., Mohamed, M. and
Abdelwahab, S.A.M., 2021.
Performance improvement of hybrid
renewable energy sources
connected to the grid using artificial
neural network and sliding mode
International Journal on Applied Physics and Engineering
DOI: 10.37394/232030.2023.2.8
J. Vinothkumar, R. Thamizhselvan
E-ISSN: 2945-0489
73
Volume 2, 2023
control. Journal of Power
Electronics, 21(8), pp.1166-1179.
[6] Ramesh, M., Yadav, A.K. and Pathak,
P.K., 2021. An extensive review on
load frequency control of solar-wind
based hybrid renewable energy
systems. Energy Sources, Part A:
Recovery, Utilization, and
Environmental Effects, pp.1-25.
[7] Mahto, T., Malik, H., Mukherjee, V.,
Alotaibi, M.A. and Almutairi, A., 2021.
Renewable generation based hybrid
power system control using fractional
order-fuzzy controller. Energy
Reports, 7, pp.641-653.
[8] Zangeneh, M., Aghajari, E. and
Forouzanfar, M., 2022. Design and
implementation of an intelligent multi-
input multi-output Sugeno fuzzy logic
controller for managing energy
resources in a hybrid renewable energy
power system based on Arduino
boards. Soft Computing, 26(3),
pp.1459-1473.
[9] Mudi, J., Shiva, C.K., Vedik, B. and
Mukherjee, V., 2021. Frequency
stabilization of solar thermal-
photovoltaic hybrid renewable power
generation using energy storage
devices. Iranian Journal of Science and
Technology, Transactions of Electrical
Engineering, 45(2), pp.597-617.
[10] Mahmoudi, S.M., Maleki, A. and
Ochbelagh, D.R., 2021. Optimization
of a hybrid energy system with/without
considering backup system by a new
technique based on fuzzy logic
controller. Energy Conversion and
Management, 229, p.113723.
[11] Kumar, G.B., 2022. Optimal power
point tracking of solar and wind energy
in a hybrid wind-solar energy
system. International Journal of Energy
and Environmental Engineering, 13(1),
pp.77-103.
[12] Chatterjee, S., Alam, A. and Ghose, T.,
2021. Evaluation of the Performance of
Droop Control Scheme for Interlinking
Converter in Stand-Alone Hybrid
Microgrid. In Advances in Smart Grid
Automation and Industry 4.0 (pp. 807-
816). Springer, Singapore.
[13] Angalaeswari, S., Jamuna, K.,
Natrayan, L., Ramesh, L. and
Ramaswamy, K., 2022. Power-Sharing
Analysis of Hybrid Microgrid Using
Iterative Learning Controller (ILC)
considering Source and Load
Variation. Mathematical Problems in
Engineering, 2022.
[14] Tafavogh, M. and Zahedi, A., 2021.
Design and production of a novel
encapsulated nano phase change
materials to improve thermal efficiency
of a quintuple renewable
geothermal/hydro/biomass/solar/wind
hybrid system. Renewable
Energy, 169, pp.358-378.
[15] Sharma, M., Saxena, S., Prakash, S.,
Dhundhara, S. and Arya, Y., 2022.
Frequency stabilization in sustainable
energy sources integrated power
systems using novel cascade noninteger
fuzzy controller. Energy Sources, Part
A: Recovery, Utilization, and
Environmental Effects, 44(3), pp.6213-
6235.
[16] Ahmed, M., Magdy, G., Khamies, M.
and Kamel, S., 2022. An efficient
coordinated strategy for frequency
stability in hybrid power systems with
renewables considering interline power
flow controller and redox flow
battery. Journal of Energy Storage, 52,
p.104835.
[17] Dei, G., Gupta, D.K., Sahu, B.K., Jha,
A.V., Appasani, B., Zawbaa, H.M. and
Kamel, S., 2022. Improved Squirrel
Search Algorithm Driven Cascaded
2DOF-PID-FOI Controller for Load
Frequency Control of Renewable
Energy Based Hybrid Power
System. IEEE Access, 10, pp.46372-
46391.
[18] Kumar, A., Gupta, D.K. and Ghatak,
S.R., 2022, January. Fractional order PI
Controller based Load Frequency
Control of Hybrid Power System with
Electric Vehicle. In 2022 2nd
International Conference on Power
International Journal on Applied Physics and Engineering
DOI: 10.37394/232030.2023.2.8
J. Vinothkumar, R. Thamizhselvan
E-ISSN: 2945-0489
74
Volume 2, 2023
Electronics & IoT Applications in
Renewable Energy and its Control
(PARC) (pp. 1-6). IEEE.
[19] Sharma, M., Saxena, S., Prakash, S.,
Dhundhara, S. and Arya, Y., 2022.
Frequency stabilization in sustainable
energy sources integrated power
systems using novel cascade noninteger
fuzzy controller. Energy Sources, Part
A: Recovery, Utilization, and
Environmental Effects, 44(3), pp.6213-
6235.
[20] Eluri, H. and Naik, M.G., 2022.
Energy Management System and
Enhancement of Power Quality with
Grid Integrated Micro-Grid using
Fuzzy Logic Controller. IJEER, 10(2),
pp.256-263.
[21] Sundararaju, N., Vinayagam, A.,
Veerasamy, V. and Subramaniam, G.,
2022. A Chaotic Search-Based Hybrid
Optimization Technique for Automatic
Load Frequency Control of a
Renewable Energy Integrated Power
System. Sustainability, 14(9),
p.5668.
[22] Kumar, A. and Suhag, S., 2022. Whale
optimization algorithm optimized
fuzzy-pid plus pid hybrid controller for
frequency regulation in hybrid power
system. Journal of The Institution of
Engineers (India): Series B, 103(2),
pp.633-648.
[23] Aryan Nezhad, M., 2022. Frequency
Control and Power Balancing in a
Hybrid Renewable Energy System
(HRES): Effective Tuning of PI
Controllers in the Secondary Control
Level. Journal of Solar Energy
Research, 7(1), pp.963-970.
[24] Zhang, Y., Cheng, C., Cai, H., Jin, X.,
Jia, Z., Wu, X., Su, H. and Yang, T.,
2022. Long-term stochastic model
predictive control and efficiency
assessment for hydro-wind-solar
renewable energy supply
system. Applied Energy, 316,
p.119134.
[25] Chitra, L. and Kavitha Kumari, K.S.,
2022. Artificial Intelligent Based
Control of Improved Converter for
Hybrid Renewable Energy Systems.
In Proceedings of International Joint
Conference on Advances in
Computational Intelligence (pp. 583-
596). Springer, Singapore.
International Journal on Applied Physics and Engineering
DOI: 10.37394/232030.2023.2.8
J. Vinothkumar, R. Thamizhselvan
E-ISSN: 2945-0489
75
Volume 2, 2023
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
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Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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