Processes,
Journal Year:
2024,
Volume and Issue:
12(12), P. 2718 - 2718
Published: Dec. 2, 2024
The
rise
in
photovoltaic
(PV)
energy
utilization
has
led
to
increased
research
on
its
functioning,
as
accurate
modeling
is
crucial
for
system
simulations.
However,
capturing
nonlinear
current–voltage
traits
challenging
due
limited
data
from
cells’
datasheets.
This
paper
presents
a
novel
enhanced
version
of
the
Brown-Bear
Optimization
Algorithm
(EBOA)
determining
ideal
parameters
circuit
model.
presented
EBOA
incorporates
several
modifications
aimed
at
improving
searching
capabilities.
It
combines
Fractional-order
Chaos
maps
(FC
maps),
which
support
BOA
settings
be
adjusted
an
adaptive
manner.
Additionally,
it
integrates
key
mechanisms
Hippopotamus
(HO)
strengthen
algorithm’s
exploitation
potential
by
leveraging
surrounding
knowledge
more
effective
position
updates
while
also
balance
between
global
and
local
search
processes.
was
subjected
extensive
mathematical
validation
through
application
benchmark
functions
rigorously
assess
performance.
Also,
PV
parameter
estimation
achieved
combining
with
Newton–Raphson
approach.
Numerous
module
cell
varieties,
including
RTC
France,
STP6-120/36,
Photowatt-PWP201,
were
assessed
using
double-diode
single-diode
models.
higher
performance
shown
statistical
comparison
many
well-known
metaheuristic
techniques.
To
illustrate
this,
root
mean-squared
error
values
our
scheme
(SDM,
DDM)
PWP201
are
follows:
(8.183847
×
10−4,
7.478488
10−4),
(1.430320
10−2,
1.427010
10−2),
(2.220075
10−3,
2.061273
10−3),
respectively.
experimental
results
show
that
works
better
than
alternative
techniques
terms
accuracy,
consistency,
convergence.
IET Renewable Power Generation,
Journal Year:
2024,
Volume and Issue:
18(6), P. 959 - 978
Published: Feb. 20, 2024
Abstract
The
pressing
need
for
sustainable
energy
solutions
has
driven
significant
research
in
optimizing
solar
photovoltaic
(PV)
systems
which
is
crucial
maximizing
conversion
efficiency.
Here,
a
novel
hybrid
gazelle‐Nelder–Mead
(GOANM)
algorithm
proposed
and
evaluated.
GOANM
synergistically
integrates
the
gazelle
optimization
(GOA)
with
Nelder–Mead
(NM)
algorithm,
offering
an
efficient
powerful
approach
parameter
extraction
PV
models.
This
investigation
involves
thorough
assessment
of
algorithm's
performance
across
diverse
benchmark
functions,
including
unimodal,
multimodal,
fixed‐dimensional
CEC2020
functions.
Notably,
consistently
outperforms
other
approaches,
demonstrating
enhanced
convergence
speed,
accuracy,
reliability.
Furthermore,
application
extended
to
single
diode
double
models
RTC
France
cell
model
Photowatt‐PWP201
module.
experimental
results
demonstrate
that
approaches
terms
accurate
estimation,
low
root
mean
square
values,
fast
convergence,
alignment
data.
These
emphasize
its
role
achieving
superior
efficiency
renewable
systems.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: April 4, 2024
Abstract
The
growing
demand
for
solar
energy
conversion
underscores
the
need
precise
parameter
extraction
methods
in
photovoltaic
(PV)
plants.
This
study
focuses
on
enhancing
accuracy
PV
system
extraction,
essential
optimizing
models
under
diverse
environmental
conditions.
Utilizing
primary
(single
diode,
double
and
three
diode)
module
models,
research
emphasizes
importance
of
accurate
identification.
In
response
to
limitations
existing
metaheuristic
algorithms,
introduces
enhanced
prairie
dog
optimizer
(En-PDO).
novel
algorithm
integrates
strengths
(PDO)
with
random
learning
logarithmic
spiral
search
mechanisms.
Evaluation
against
PDO,
a
comprehensive
comparison
eighteen
recent
spanning
optimization
techniques,
highlight
En-PDO’s
exceptional
performance
across
different
cell
CEC2020
functions.
Application
En-PDO
single
using
experimental
datasets
(R.T.C.
France
silicon
Photowatt-PWP201
cells)
test
functions,
demonstrates
its
consistent
superiority.
achieves
competitive
or
superior
root
mean
square
error
values,
showcasing
efficacy
accurately
modeling
behavior
cells
performing
optimally
These
findings
position
as
robust
reliable
approach
estimation
emphasizing
potential
advancements
compared
algorithms.
