Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 5, 2024
Photovoltaic
(PV)
cells
exhibit
a
nonlinear
characteristic.
Before
modeling
these
cells,
obtaining
accurate
parameters
is
essential.
During
the
phase,
using
crucial
for
accurately
characterizing
and
reflecting
behavior
of
PV
structures.
Therefore,
this
article
focuses
on
parameter
extraction.
A
cell
module
were
selected
modeled
four-diode
model
(FDM).
This
problem,
consisting
eleven
unknown
related
to
FDM,
was
solved
with
flood
algorithm
(FLA).
To
compare
algorithm’s
performance
same
polar
lights
optimizer
(PLO),
moss
growth
optimization
(MGO),
walrus
(WO),
educational
competition
(ECO)
also
employed.
These
five
metaheuristic
algorithms
used
first
time
in
study,
both
solving
extraction
problem
FDM.
The
objective
function
aimed
at
smallest
root
mean
square
error
(RMSE)
evaluated
compared
through
assessment
metrics,
computational
accuracy,
time,
statistical
methods.
minimum
RMSE
obtained
FLA,
calculated
as
9.8251385E-04
FDM-C
1.6884311E-03
FDM-M.
statistically
demonstrate
reinforce
FLA’s
success
over
other
algorithms,
Friedman
test
Wilcoxon
signed-rank
utilized.
According
tests,
FLA
produced
significantly
better
results
than
outperformed
them
pairwise
comparisons.
In
conclusion,
has
proven
be
successful
promising
extraction,
its
validated.
Frontiers in Energy Research,
Journal Year:
2024,
Volume and Issue:
12
Published: Aug. 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.
PeerJ Computer Science,
Journal Year:
2025,
Volume and Issue:
11, P. e2646 - e2646
Published: Jan. 27, 2025
This
study
conducts
a
comparative
analysis
of
the
performance
ten
novel
and
well-performing
metaheuristic
algorithms
for
parameter
estimation
solar
photovoltaic
models.
optimization
problem
involves
accurately
identifying
parameters
that
reflect
complex
nonlinear
behaviours
cells
affected
by
changing
environmental
conditions
material
inconsistencies.
is
challenging
due
to
computational
complexity
risk
errors,
which
can
hinder
reliable
predictions.
The
evaluated
include
Crayfish
Optimization
Algorithm,
Golf
Coati
Crested
Porcupine
Optimizer,
Growth
Artificial
Protozoa
Secretary
Bird
Mother
Election
Optimizer
Technical
Vocational
Education
Training-Based
Optimizer.
These
are
applied
solve
four
well-established
models:
single-diode
model,
double-diode
triple-diode
different
module
focuses
on
key
metrics
such
as
execution
time,
number
function
evaluations,
solution
optimality.
results
reveal
significant
differences
in
efficiency
accuracy
algorithms,
with
some
demonstrating
superior
specific
Friedman
test
was
utilized
rank
various
revealing
top
performer
across
all
considered
optimizer
achieved
root
mean
square
error
9.8602187789E-04
9.8248487610E-04
both
models
1.2307306856E-02
model.
consistent
success
indicates
strong
contender
future
enhancements
aimed
at
further
boosting
its
effectiveness.
Its
current
suggests
potential
improvement,
making
it
promising
focus
ongoing
development
efforts.
findings
contribute
understanding
applicability
renewable
energy
systems,
providing
valuable
insights
optimizing
International Journal of Modelling and Simulation,
Journal Year:
2025,
Volume and Issue:
unknown, P. 1 - 28
Published: Feb. 26, 2025
High-performance
solar
photovoltaic
models
rely
on
a
precise
understanding
of
(PV)
cell
parameters.
This
necessity
arises
from
the
importance
comprehending
and
optimizing
performance
systems
to
ensure
reliable
efficient
energy
production.
Nonetheless,
due
intrinsic
nonlinear
nature
systems,
employing
algorithm
is
essential
for
accurate
modeling.
In
this
article,
an
enhanced
inspired
by
behavior
Tasmanian
Devil,
named
Improved
Devil
Optimization
(ITDO),
proposed
enhance
original
TDO.
Our
includes
improvements
exploitation
phase,
increasing
frequency
prey
detection
attacks
in
target
zone.
The
method
retains
steps,
with
exploration
stage
unchanged.
However,
second
step
has
been
adaptation
mechanism,
final
improved
efficiently
select
global
optimum.
These
modifications
do
not
impact
method's
complexity.
To
assess
ITDO's
effectiveness,
experiments
were
conducted
using
single,
double,
PV
module
models.
Thorough
comparison
seven
other
algorithms
revealed
superior
solution
accuracy.
Additionally,
statistical
analyses
Wilcoxon
rank-sum
Friedman
tests
confirmed
superiority
as
most
robust
parameter
estimation
systems.
