Electric Power Components and Systems,
Год журнала:
2023,
Номер
52(7), С. 1001 - 1021
Опубликована: Ноя. 23, 2023
Fossil
fuel
prices
are
increasing
day
by
due
to
growing
demand
and
resource
limitations.
This
situation
shifts
conventional
electricity
generation
renewable
power
generation.
Among
renewables,
solar
photovoltaic
(PV)
energy
is
very
promising
as
the
third
fastest
one.
However,
PV
system
modeling
requires
estimation
of
unknown
parameters
cells
modules.
problem
remains
a
compelling
task
its
multi-dimensional,
multi-model
non-linear
characteristics.
In
order
contribute
solution
this
optimization
problem,
work
makes
detailed
comparison
artificial
hummingbird
algorithm,
rabbits
optimization,
enhanced
Jaya
flow
direction
algorithm
gorilla
troops
optimizer
for
determining
models.
Experimental
results
demonstrate
that
in
single
diode
cell,
double
cell
module
found
be
capable
estimating
accurate
efficient
design
coefficients
systems.
addition,
mentioned
metaheuristic
algorithms
show
reasonable
convergence
characteristics
corresponding
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Март 5, 2024
Abstract
This
study
presents
the
K-means
clustering-based
grey
wolf
optimizer,
a
new
algorithm
intended
to
improve
optimization
capabilities
of
conventional
optimizer
in
order
address
problem
data
clustering.
The
process
that
groups
similar
items
within
dataset
into
non-overlapping
groups.
Grey
hunting
behaviour
served
as
model
for
however,
it
frequently
lacks
exploration
and
exploitation
are
essential
efficient
work
mainly
focuses
on
enhancing
using
weight
factor
concepts
increase
variety
avoid
premature
convergence.
Using
partitional
clustering-inspired
fitness
function,
was
extensively
evaluated
ten
numerical
functions
multiple
real-world
datasets
with
varying
levels
complexity
dimensionality.
methodology
is
based
incorporating
concept
purpose
refining
initial
solutions
adding
diversity
during
phase.
results
show
performs
much
better
than
standard
discovering
optimal
clustering
solutions,
indicating
higher
capacity
effective
solution
space.
found
able
produce
high-quality
cluster
centres
fewer
iterations,
demonstrating
its
efficacy
efficiency
various
datasets.
Finally,
demonstrates
robustness
dependability
resolving
issues,
which
represents
significant
advancement
over
techniques.
In
addition
addressing
shortcomings
algorithm,
incorporation
innovative
establishes
further
metaheuristic
algorithms.
performance
around
34%
original
both
test
problems
problems.
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Июнь 7, 2023
The
parameter
extraction
of
PV
models
is
a
nonlinear
and
multi-model
optimization
problem.
However,
it
essential
to
correctly
estimate
the
parameters
units
due
their
impact
on
system
efficiency
in
terms
power
current
production.
As
result,
this
study
introduces
developed
Artificial
Hummingbird
Technique
(AHT)
generate
best
values
ungiven
these
units.
AHT
mimics
hummingbirds'
unique
flying
abilities
foraging
methods
wild.
compared
with
numerous
recent
inspired
techniques
which
are
tuna
swarm
optimizer,
African
vulture's
teaching
learning
studying-based
optimizer
other
techniques.
statistical
studies
experimental
findings
show
that
outperforms
extracting
various
STM6-40/36,
KC200GT
PWP
201
polycrystalline.
AHT's
performance
evaluated
using
datasheet
provided
by
manufacturer.
To
highlight
dominance,
its
those
competing
simulation
outcomes
demonstrate
algorithm
features
quick
processing
time
steadily
convergence
consort
keeping
an
elevated
level
accuracy
offered
solution.
Energy Reports,
Год журнала:
2023,
Номер
9, С. 4654 - 4681
Опубликована: Март 30, 2023
The
accurate
estimation
of
model
parameters
is
significant
for
the
simulation,
evaluation,
control,
and
optimization
photovoltaic
systems.
Recently,
meta-heuristic
algorithms(MHAs)
have
been
proposed
to
solve
parameter
identification
problem.
However,
extracting
reliable
PV
models
still
a
great
challenge,
many
HMAs
may
present
unsatisfactory
performance
due
their
premature
or
slow
convergence.
Therefore,
how
develop
algorithms
efficiently
balancing
exploration
exploitation
improve
accuracy
reliability
algorithm
extremely
important.
This
paper
proposes
Hybrid
multi-group
stochastic
cooperative
(HMSCPSO).
In
algorithm,
we
designed
cooperation
search
mechanism
enhance
global
capability:
Each
group
utilized
different
strategies.
first
used
classic
velocity
position
updates,
second
employed
chaos
strategy,
third
lévy
flight
strategy.
Through
between
groups
increase
diversity
population
reduce
possibility
falling
into
local
optimum,
but
also
concentrate
some
individuals
explore
current
optimum
solution.
HMSCPSO
its
variants
were
tested
on
27
benchmark
functions
verify
algorithm's
effectiveness.
Then,
applied
four
problems
models.
Statistical
experiment
results
demonstrate
that
has
excellent
advantages
compared
with
other
in
terms
accuracy,
reliability,
convergence
speed.
expected
be
an
effective
method
solar
cells
modules.
Sustainability,
Год журнала:
2023,
Номер
15(4), С. 3312 - 3312
Опубликована: Фев. 10, 2023
As
the
photovoltaic
(PV)
market
share
continues
to
increase,
accurate
PV
modeling
will
have
a
massive
impact
on
future
energy
landscape.
Therefore,
it
is
imperative
convert
difficult-to-understand
systems
into
understandable
mathematical
models
through
equivalent
models.
However,
multi-peaked,
non-linear,
and
strongly
coupled
characteristics
of
make
challenging
extract
parameters
Metaheuristics
can
address
these
challenges
effectively
regardless
gradients
function
forms,
gained
increasing
attention
in
solving
this
issue.
This
review
surveys
different
metaheuristics
model
parameter
extraction
explains
multiple
algorithms’
behavior.
Some
frequently
used
performance
indicators
measure
effectiveness,
robustness,
accuracy,
competitiveness,
resources
consumed
are
tabulated
compared,
then
merits
demerits
algorithms
outlined.
The
patterns
variation
results
extracted
from
external
environments
were
analyzed,
corresponding
literature
was
summarized.
Then,
for
both
application
scenarios
analyzed.
Finally,
perspectives
research
summarized
as
valid
reference
technological
advances
extraction.
