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.
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.
Sustainability,
Journal Year:
2023,
Volume and Issue:
15(18), P. 13916 - 13916
Published: Sept. 19, 2023
Photovoltaic
(PV)
systems
are
crucial
for
converting
solar
energy
into
electricity.
Optimization,
control,
and
simulation
PV
important
effectively
harnessing
energy.
The
exactitude
of
associated
model
parameters
is
an
influencing
factor
in
the
performance
systems.
However,
parameter
extraction
challenging
due
to
variability
resulting
from
change
different
environmental
conditions
equipment
factors.
Existing
identification
approaches
usually
struggle
calculate
precise
solutions.
For
this
reason,
paper
presents
improved
differential
evolution
algorithm,
which
integrates
a
collaboration
mechanism
dual
mutation
strategies
orientation
guidance
mechanism,
called
DODE.
This
adaptively
assigns
individuals
at
stages
balance
exploration
exploitation
capabilities.
Moreover,
proposed
use
information
movement
direction
population
centroid
guide
elite
individuals,
preventing
them
being
trapped
local
optima
guiding
towards
search.
To
assess
effectiveness
DODE,
comparison
experiments
were
conducted
on
six
models,
i.e.,
single,
double,
triple
diode
three
other
commercial
modules,
against
ten
excellent
meta-heuristic
algorithms.
these
DODE
outperformed
algorithms,
with
separate
optimal
root
mean
square
error
values
9.86021877891317
×
10−4,
9.82484851784979
9.82484851784993
2.42507486809489
10−3,
1.72981370994064
1.66006031250846
10−2.
Additionally,
results
obtained
statistical
analysis
confirm
remarkable
competitive
superiorities
convergence
rate,
stability,
reliability
compared
methods
identification.
Power Electronics and Drives,
Journal Year:
2025,
Volume and Issue:
10(1), P. 41 - 59
Published: Jan. 1, 2025
Abstract
Accurate
parameter
estimation
is
vital
for
optimising
the
performance
and
design
of
photovoltaic
(PV)
systems.
While
metaheuristic
algorithms
(MHAs)
offer
promising
solutions,
they
often
face
challenges
such
as
slow
convergence
difficulty
balancing
exploration
exploitation.
This
study
introduces
a
novel
hybrid
approach,
WSO-HO,
which
integrates
strengths
war
strategy
optimization
(WSO)
Hippopotamus
Optimization
(HO)
algorithms,
enhanced
by
Newton-Raphson
(NR)
method,
to
achieve
precise
PV
models.
The
effectiveness
WSO-HO
algorithm
was
rigorously
evaluated
through
intensive
testing
on
three
different
solar
panels,
including
RTC
France
cell
using
single
diode
model
(SDM)
double
(DDM),
over
30
iterations.
Comparative
analysis
highlights
superior
against
conventional
struggle
with
accurately
identifying
parameters.
These
results
demonstrate
significant
potential
this
approach
improve
optimisation
in
systems,
enabling
more
overall
system
efficiency.
Furthermore,
simulation
result
benchmarked
other
reported
literature,
further
validating
its
robustness
effectiveness.
