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.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(20), P. e39301 - e39301
Published: Oct. 1, 2024
Harnessing
the
potential
of
solar
photovoltaic
(PV)
technology
relies
heavily
on
accurately
estimating
model
parameters
PV
cells/modules
using
real
current-voltage
(I-V)
data.
Achieving
optimal
parameter
values
is
essential
for
performance
and
efficiency
systems,
necessitating
use
advanced
optimization
techniques.
In
our
endeavor,
we
introduce
a
multi-strategy
improvement
approach
Runge
Kutta
(RUN)
optimizer,
cutting-edge
tool
used
tackling
this
critical
task
in
both
single-diode
double-diode
unit
models.
By
aligning
experimental
model-based
estimated
data,
seeks
to
reduce
errors
improve
accuracy
system
performance.
We
conduct
meticulous
analyses
two
compelling
case
studies
CEC
2020
test
suite
showcase
versatility
effectiveness
improved
RUN
(IRUN)
algorithm.
The
first
study
involves
standard
dataset
derived
from
well-known
R.T.C.
France
silicon
cell,
where
IRUN
performs
favorably
compared
competing
methods,
demonstrating
its
effectiveness.
effectively
manages
complex
defining
an
industrial
module
situated
at
Engineering
Faculty
Düzce
University
Turkey.
real-world
I-V
obtained
under
conditions
with
temperature
radiance
of,
provide
strong
evidence
practical
applicability
benefits
innovative
method.
Additional
through
three-diode
models
further
confirm
efficacy
IRUN.
A
mean
absolute
error
down
6.5E-04
root
square
7.3668E-04
are
achieved.
Our
provides
efficient
improving
enhancing
their
when
existing
methods.
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.
