Parameter extraction of photovoltaic cell models using electric eel foraging optimizer
Frontiers in Energy Research,
Год журнала:
2024,
Номер
12
Опубликована: Авг. 1, 2024
Solar
energy
has
emerged
as
a
key
solution
in
the
global
transition
to
renewable
sources,
driven
by
environmental
concerns
and
climate
change.
This
is
largely
due
its
cleanliness,
availability,
cost-effectiveness.
The
precise
assessment
of
hidden
factors
within
photovoltaic
(PV)
models
critical
for
effectively
exploiting
potential
these
systems.
study
employs
novel
approach
parameter
estimation,
utilizing
electric
eel
foraging
optimizer
(EEFO),
recently
documented
literature,
address
such
engineering
issues.
EEFO
emerges
competitive
metaheuristic
methodology
that
plays
crucial
role
enabling
extraction.
In
order
maintain
scientific
integrity
fairness,
utilizes
RTC
France
solar
cell
benchmark
case.
We
incorporate
approach,
together
with
Newton-Raphson
method,
into
tuning
process
three
PV
models:
single-diode,
double-diode,
three-diode
models,
using
common
experimental
framework.
selected
because
significant
field.
It
serves
reliable
evaluation
platform
approach.
conduct
thorough
statistical,
convergence,
elapsed
time
studies,
demonstrating
consistently
achieves
low
RMSE
values.
indicates
capable
accurately
estimating
current-voltage
characteristics.
system’s
smooth
convergence
behavior
further
reinforces
efficacy.
Comparing
competing
methodologies
advantage
optimizing
model
parameters,
showcasing
greatly
enhance
usage
energy.
Язык: Английский
Efficient control strategy for electric furnace temperature regulation using quadratic interpolation optimization
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 2, 2025
Electric
furnaces
play
an
important
role
in
many
industrial
processes
where
precise
temperature
control
is
essential
to
ensure
production
efficiency
and
product
quality.
Traditional
proportional-integral-derivative
(PID)
controllers
their
modified
versions
are
commonly
used
maintain
stability
by
reacting
quickly
deviations.
In
this
study,
the
real
PID
plus
second-order
derivative
(RPIDD2)
controller
introduced
for
first
time
applications,
which
a
novel
alternative
that
has
not
yet
been
investigated
literature.
To
optimal
performance,
parameters
of
RPIDD2
optimized
using
metaheuristic
algorithms,
including
flood
optimization
algorithm
(FLA),
reptile
search
(RSA),
particle
swarm
(PSO)
differential
evolution
(DE).
A
new
approach
proposed
combines
quadratic
interpolation
(QIO)
with
controller,
taking
advantage
fast
convergence,
low
computational
cost
high
accuracy
QIO.
Comparative
analyses
between
QIO-RPIDD2,
FLA-RPIDD2,
RSA-RPIDD2,
PSO-RPIDD2
DE-RPIDD2
performed
evaluating
performance
metrics
such
as
transient
frequency
response.
The
results
show
QIO-RPIDD2
achieves
superior
adapts
different
reference
temperatures
performs
excellently
on
key
indicators.
These
make
promising
solution
contribute
more
efficient
adaptive
techniques.
Язык: Английский
A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling
Chaos Solitons & Fractals,
Год журнала:
2024,
Номер
185, С. 115111 - 115111
Опубликована: Июнь 15, 2024
Язык: Английский
Nonlinear controller design for automotive engine speed regulation utilizing electric eel foraging optimization
International Journal of Dynamics and Control,
Год журнала:
2025,
Номер
13(2)
Опубликована: Фев. 1, 2025
Язык: Английский
A novel pressure control method for nonlinear shell-and-tube steam condenser system via electric eel foraging optimizer
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 4, 2025
Precise
pressure
control
in
shell-and-tube
steam
condensers
is
crucial
for
ensuring
efficiency
thermal
power
plants.
However,
traditional
controllers
(PI,
PD,
PID)
struggle
with
nonlinearities
and
external
disturbances,
while
classical
tuning
methods
(Ziegler-Nichols,
Cohen-Coon)
fail
to
provide
optimal
parameter
selection.
These
challenges
lead
slow
response,
high
overshoot,
poor
steady-state
performance.
To
address
these
limitations,
this
study
proposes
a
cascaded
PI-PDN
strategy
optimized
using
the
electric
eel
foraging
optimizer
(EEFO).
EEFO,
inspired
by
prey-seeking
behavior
of
eels,
efficiently
tunes
controller
parameters,
improved
stability
precision.
A
comparative
analysis
against
recent
metaheuristic
algorithms
(SMA,
GEO,
KMA,
QIO)
demonstrates
superior
performance
EEFO
regulating
condenser
pressure.
Additionally,
validation
documented
studies
(CSA-based
FOPID,
RIME-based
GWO-based
PI,
GA-based
PI)
highlights
its
advantages
over
existing
methods.
Simulation
results
confirm
that
reduces
settling
time
22.7%,
overshoot
78.7%,
error
three
orders
magnitude,
ITAE
81.2%
compared
based
The
EEFO-based
achieves
faster
convergence,
enhanced
robustness
precise
tracking,
making
it
highly
effective
solution
real-world
applications.
findings
contribute
optimization-based
strategies
plants
open
pathways
further
bio-inspired
innovations.
Язык: Английский
An advanced PID tuning method for temperature control in electric furnaces using the artificial rabbits optimization algorithm
International Journal of Dynamics and Control,
Год журнала:
2025,
Номер
13(5)
Опубликована: Апрель 25, 2025
Язык: Английский
Spiral-refraction mutation prairie dog algorithm: Optimization framework for engineering design of interconnected multimachine power system
Applied Soft Computing,
Год журнала:
2024,
Номер
165, С. 112036 - 112036
Опубликована: Июль 31, 2024
Язык: Английский
An Optimized and Intelligent Metaverse Intrusion Detection System based on Rough Sets
Internet of Things,
Год журнала:
2024,
Номер
28, С. 101360 - 101360
Опубликована: Сен. 3, 2024
Язык: Английский
Heuristic Optimization-Based Trajectory Planning for UAV Swarms in Urban Target Strike Operations
Drones,
Год журнала:
2024,
Номер
8(12), С. 777 - 777
Опубликована: Дек. 20, 2024
Unmanned
aerial
vehicle
(UAV)
swarms
have
shown
substantial
potential
to
enhance
operational
efficiency
and
reduce
strike
costs,
presenting
extensive
applications
in
modern
urban
warfare.
However,
achieving
effective
performance
complex
environments
remains
challenging,
particularly
when
considering
three-dimensional
obstacles
threat
zones
simultaneously,
which
can
significantly
degrade
effectiveness.
To
address
this
challenge,
paper
proposes
a
target
strategy
using
the
Electric
Eel
Foraging
Optimization
(EEFO)
algorithm,
heuristic
optimization
method
designed
ensure
precise
strikes
environments.
The
problem
is
formulated
with
specific
constraints,
modeling
each
UAV
as
an
electric
eel
random
initial
positions
velocities.
This
algorithm
simulates
interaction,
resting,
hunting,
migrating
behaviors
of
eels
during
their
foraging
process.
During
interaction
phase,
UAVs
engage
global
exploration
through
communication
environmental
sensing.
resting
phase
allows
temporarily
hold
positions,
preventing
premature
convergence
local
optima.
In
hunting
swarm
identifies
pursues
optimal
paths,
while
migration
transition
areas,
avoiding
threats
seeking
safer
routes.
enhances
overall
capabilities
by
sharing
information
among
surrounding
individuals
promoting
group
cooperation,
effectively
planning
flight
paths
for
strikes.
MATLAB(R2024b)
simulation
platform
used
compare
five
algorithms—SO,
SCA,
WOA,
MFO,
HHO—against
proposed
missions.
experimental
results
demonstrate
that
sparse
undefended
environment,
EEFO
outperforms
other
algorithms
terms
trajectory
efficiency,
stability,
minimal
costs
also
exhibiting
faster
rates.
densely
defended
environments,
not
only
achieves
but
shows
superior
trends
cost
reduction,
along
highest
mission
completion
rate.
These
highlight
effectiveness
both
scenarios,
making
it
promising
approach
operations
dynamic
Язык: Английский
Enhancing Temperature Control of Electric Furnaces Using a Modified Pid Controller Design Strategy
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 25, 2024
Abstract
This
paper
presents
the
design
and
implementation
of
a
modified
Proportional-Integral-Derivative
(PID)
control
strategy,
named
Fractionalized
PID
(FrOPID),
to
enhance
transient
frequency
responses,
as
well
robustness
temperature
in
electric
furnaces.
The
FrOPID
controller
introduced
this
study
is
being
used
for
first
time
furnace
temperature.
an
extension
traditional
controller,
requiring
adjustment
four
parameters
compared
three
controller.
effectiveness
proposed
approach
was
validated
through
extensive
analysis
analysis.
performance
HHO/FrOPID
then
benchmarked
against
several
other
controllers,
including
optimized
by
original
Harris
Hawks
Optimization
(HHO)
algorithm,
those
tuned
using
advanced
meta-heuristic
algorithms
such
Optimization-based
(HHO/PID),
Modified
Electric
Eel
Foraging
(MEEFO/PID),
(EEFO/PID),
Whale
Algorithm-based
(WOA/PID).
Simulation
results
demonstrate
that
outperforms
existing
offering
superior
more
robust
terms
percentage
overshoot,
settling
time,
rise
peak
time.
Язык: Английский