Accurate parameters extraction of photovoltaic models using Lambert W-function collaborated with AI-based Puma optimization method
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 104268 - 104268
Опубликована: Фев. 1, 2025
Язык: Английский
Efficient identification of photovoltaic cell parameters via Bayesian Neural Network-artificial ecosystem optimization algorithm
Bo Yang,
Ruyi Zheng,
Yucun Qian
и другие.
Global Energy Interconnection,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 1, 2025
Язык: Английский
Comparative Analysis of Rate of Penetration Prediction and Optimization in Deep Wells Using Real-Time Continuous Stacked Generalization Ensemble Learning: A Case Study in Shunbei Field
Опубликована: Янв. 1, 2024
Язык: Английский
Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 30, 2024
Selective
Harmonic
Elimination
Pulse
Width
Modulation
(SHEPWM)
has
excellent
harmonic
characteristics,
but
its
nonlinear
transcendental
system
of
equations
is
difficult
to
be
solved,
and
the
practical
application
encounters
a
bottleneck.
In
this
paper,
modulation
optimization
method
for
seven-level
SHEPWM
inverter
based
on
Evolutionary
Particle
Swarm
Optimization
(EPSO)
algorithm
proposed
address
problem,
so
that
quickly
converges
global
optimum
solution.
The
EPSO
incorporates
population
strategy
in
two
phases
improve
diversity
real
time.
initialization
phase,
initialized
optimized
using
Opposition-Based
Learning
(OBL)
quality
initial
population.
iterative
stage,
we
combine
adaptive
(PSO)
algorithm,
Tunicate
Algorithm
(TSA),
Adaptive
Gaussian
Variation,
Quasi-Opposition-Based
(QOBL)
other
methods
solve
problem
insufficient
process
searching
optimal
solution,
break
through
local
optimum,
convergence
speed
accuracy
algorithm.
Experiments
19
benchmark
functions
show
ability
ahead
TSA,
INFO,
MA
(Mayfly
Algorithm),
EO
(Equilibrium
Optimizer)
algorithms.
solution
about
three
times
PSO,
which
achieves
fast
highly
accurate
convergence,
with
small
error
output
inverter,
better
distortion
rate
than
standard
requirement.
Язык: Английский
Parameter Extraction of Photovoltaic Cell and Module with Four Diode Model Using Flood Algorithm
Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 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.
Язык: Английский