IET Renewable Power Generation,
Journal Year:
2024,
Volume and Issue:
18(12), P. 1893 - 1925
Published: Aug. 2, 2024
Abstract
Accurate
parameter
identification
plays
a
crucial
role
in
realizing
precise
modelling,
design
optimization,
condition
monitoring,
and
fault
diagnosis
of
photovoltaic
systems.
However,
due
to
the
nonlinear,
multivariate,
multistate
characteristics
PV
models,
it
is
difficult
identify
perfect
model
parameters
using
traditional
analytical
numerical
methods.
Besides,
some
existing
methods
may
stick
local
optimum
have
slow
convergence
speed.
To
address
these
challenges,
this
paper
proposes
an
enhanced
nature‐inspired
OLARO
algorithm
for
under
different
conditions.
improved
from
ARO
incorporating
opposition‐based
learning,
Lévy
flight
roulette
fitness‐distance
balance
improve
global
search
capability
avoid
optima.
Firstly,
novel
data
smoothing
measure
taken
reduce
noises
I
–
V
curves.
Then,
compared
with
several
common
algorithms
on
solar
cells
modules
robustness
analysis
statistical
tests.
The
results
indicate
that
has
better
ability
than
others
extract
models
such
as
single
diode,
double
module
models.
Moreover,
performance
more
excellent
other
algorithms.
Additionally,
curves
two
irradiance
temperature
conditions
are
applied
verify
proposed
extraction
algorithm.
successfully
real
operating
modules,
recent
well‐known
by
FDB.
Finally,
sensitivity
analysis,
stability
discussion
practical
challenges
provided.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: July 5, 2024
The
energy
management
(EM)
solution
of
the
multi-microgrids
(MMGs)
is
a
crucial
task
to
provide
more
flexibility,
reliability,
and
economic
benefits.
However,
MMGs
became
complex
strenuous
with
high
penetration
renewable
resources
due
stochastic
nature
these
along
load
fluctuations.
In
this
regard,
paper
aims
solve
EM
problem
optimal
inclusion
photovoltaic
(PV)
systems,
wind
turbines
(WTs),
biomass
systems.
proposed
an
enhanced
Jellyfish
Search
Optimizer
(EJSO)
for
solving
85-bus
MMGS
system
minimize
total
cost,
performance
improvement
concurrently.
algorithm
based
on
Weibull
Flight
Motion
(WFM)
Fitness
Distance
Balance
(FDB)
mechanisms
tackle
stagnation
conventional
JSO
technique.
EJSO
tested
standard
CEC
2019
benchmark
functions
obtained
results
are
compared
optimization
techniques.
As
per
results,
powerful
method
other
like
Sand
Cat
Swarm
Optimization
(SCSO),
Dandelion
(DO),
Grey
Wolf
(GWO),
Whale
Algorithm
(WOA),
(JSO).
reveal
that
by
suggested
can
reduce
cost
44.75%
while
voltage
profile
stability
40.8%
10.56%,
respectively.
IEEE Internet of Things Journal,
Journal Year:
2024,
Volume and Issue:
11(11), P. 20605 - 20618
Published: March 5, 2024
An
innovative
method
to
raise
wireless
communication
systems'
efficiency
is
use
Reconfigurable
Intelligent
Surface
(RIS).
Unfortunately,
determining
the
quantity
and
locations
of
RIS
elements
continues
be
difficult,
requiring
a
clever
optimization
framework.
Concerning
practical
overlap
between
related
multi-RISs
in
systems,
this
paper
attempts
minimize
number
RISs
while
considering
average
possible
data
rate
technological
constraints.
In
regard,
novel
Enhanced
Artificial
Rabbits
Algorithm
(EARA)
developed
installed.
The
EARA
inspired
by
natural
survival
strategies
rabbits,
including
detour
eating
random
concealment.
A
more
effective
exploring
search
space
around
best
solution
so
far
produced
suggested
combining
an
upgraded
Collaborative
Searching
Operator
(CSO)
arrangement.
Also,
adaptive
time
function
included
increase
effect
exploitation
tactic
increasing
iterations.
simulation
results
show
that
highly
efficient
reaching
maximum
success
producing
smallest
under
various
feasible
threshold
settings.
When
compared
standard
Optimizer
(ARO),
Growth
(GO),
Ecosystem
(AEO),
Particle
Swarm
Optimization
(PSO),
improved
5.32%,
6.7%,
16.73%,
20.06%,
respectively.
Furthermore,
according
data,
outperforms
AEO,
GO,
ARO,
PSO
terms
at
δ=1.4
6.66%,
45.43%,
99%,
Scientific Reports,
Journal Year:
2023,
Volume and Issue:
13(1)
Published: Dec. 21, 2023
The
grey
wolf
optimizer
is
an
effective
and
well-known
meta-heuristic
algorithm,
but
it
also
has
the
weaknesses
of
insufficient
population
diversity,
falling
into
local
optimal
solutions
easily,
unsatisfactory
convergence
speed.
Therefore,
we
propose
a
hybrid
(HGWO),
based
mainly
on
exploitation
phase
harris
hawk
optimization.
It
includes
initialization
with
Latin
hypercube
sampling,
nonlinear
factor
perturbations,
some
extended
exploration
strategies.
In
HGWO,
wolves
can
have
hawks-like
flight
capabilities
during
position
updates,
which
greatly
expands
search
range
improves
global
searchability.
By
incorporating
greedy
will
relocate
only
if
new
location
superior
to
current
one.
This
paper
assesses
performance
(HGWO)
by
comparing
other
heuristic
algorithms
enhanced
schemes
optimizer.
evaluation
conducted
using
23
classical
benchmark
test
functions
CEC2020.
experimental
results
reveal
that
HGWO
algorithm
performs
well
in
terms
its
ability,
speed,
accuracy.
Additionally,
demonstrates
considerable
advantages
solving
engineering
problems,
thus
substantiating
effectiveness
applicability.
