A high-speed MPPT based horse herd optimization algorithm with dynamic linear active disturbance rejection control for PV battery charging system
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 25, 2025
This
study
first
proposes
an
innovative
method
for
optimizing
the
maximum
power
extraction
from
photovoltaic
(PV)
systems
during
dynamic
and
static
environmental
conditions
(DSEC)
by
applying
horse
herd
optimization
algorithm
(HHOA).
The
HHOA
is
a
bio-inspired
technique
that
mimics
motion
cycles
of
entire
horses.
Next,
linear
active
disturbance
rejection
control
(LADRC)
was
applied
to
monitor
HHOA's
reference
voltage
output.
LADRC,
known
managing
uncertainties
disturbances,
improves
anti-interference
capacity
point
tracking
(MPPT)
speeds
up
system's
response
rate.
Then,
in
comparison
traditional
(perturb
&
observe;
P&O)
metaheuristic
algorithms
(conventional
particle
swarm
optimization;
CPSO,
grasshopper
GHO,
deterministic
PSO;
DPSO)
through
DSEC,
simulations
results
demonstrate
combination
HHOA-LADRC
can
successfully
track
global
peak
(GMP)
with
less
fluctuations
quicker
convergence
time.
Finally,
experimental
investigation
proposed
accomplished
NI
PXIE-1071
Hardware-In-Loop
(HIL)
prototype.
output
findings
show
effectiveness
provided
may
approach
value
higher
than
99%,
showed
rate
converging
oscillations
detection
mechanism.
Язык: Английский
Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF
Technologies,
Год журнала:
2024,
Номер
12(11), С. 226 - 226
Опубликована: Ноя. 11, 2024
This
study
focuses
on
microgrid
systems
incorporating
hybrid
renewable
energy
sources
(HRESs)
with
battery
storage
(BES),
both
essential
for
ensuring
reliable
and
consistent
operation
in
off-grid
standalone
systems.
The
proposed
system
includes
solar
energy,
a
wind
source
synchronous
turbine,
BES.
Hybrid
particle
swarm
optimizer
(PSO)
genetic
algorithm
(GA)
combined
active
disturbance
rejection
control
(ADRC)
(PSO-GA-ADRC)
are
developed
to
regulate
the
frequency
amplitude
of
AC
bus
voltage
via
load-side
converter
(LSC)
under
various
operating
conditions.
approach
further
enables
efficient
management
accessible
generation
general
consumption
through
bidirectional
battery-side
(BSC).
Additionally,
method
also
enhances
power
quality
across
link
mentoring
photovoltaic
(PV)
inverter
function
as
shunt
filter
(SAPF),
providing
desired
harmonic-current
element
nonlinear
local
loads
well.
Equipped
an
extended
state
observer
(ESO),
PSO-GA-ADRC
provides
estimation
compensation
disturbances
such
modeling
errors
parameter
fluctuations,
stable
solution
interior
current
loops.
positive
results
from
hardware-in-the-loop
(HIL)
experimental
confirm
effectiveness
robustness
this
strategy
maintaining
real-world
scenarios.
Язык: Английский
An efficient linear-extrapolation catch-fish algorithm for maximizing the harvested power from thermoelectric generators sources
Applied Thermal Engineering,
Год журнала:
2025,
Номер
unknown, С. 125916 - 125916
Опубликована: Фев. 1, 2025
Язык: Английский
Numerical Modeling and Neural Network Optimization for Advanced Solar Panel Efficiency
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 16, 2025
Abstract
Maximizing
output
from
renewable
solar
panels
requires
higher
efficiency.
Conventionally,
such
optimization
techniques
-
MPPT
(Maximum
Power
Point
Tracking)
along
with
heuristic
algorithms
suffer
significantly
slow
adaptability
and
track
sub
optimality
under
dynamic
environments.
This
article
proposes
a
numerical
modeling
framework
hybrid
AI
models,
combining
physics-informed
neural
networks
RL
for
real-time
of
orientation
in
panels.
The
methodology
uses
precise
energy
transformation
analysis,
deep
learning-based
dynamically
adjusts
the
angles
to
maximize
power
output.
A
self-learning
adaptive
network
is
developed
improve
tracking
accuracy
based
on
irradiance
temperature
variations.
Moreover,
an
Edge
architecture
introduced
make
low-latency
decisions
reduced
dependency
cloud
computation,
thus
improving
efficiency
system.
Besides,
advanced
model
CNN-LSTM
applied
forecasting
predictive
control
maximum
yield.
Experimental
validation
was
performed
using
UTL
335W
330W
PV
modules,
where
data
acquisition
followed
by
AI-driven
optimization.
Results
show
increase
yield
10–15%
compared
traditional
systems,
while
computations
are
40–50%
faster
AI-based
modeling.
proposed
approach
achieves
25%
lower
error
(RMSE/MAE)
30%
consumption
through
implementation.
study
sets
up
new
paradigm
AI-integrated
optimization,
which
ensures
enhanced
performance
practical
deployment.
findings
advance
intelligent
set
benchmark
management.
Язык: Английский
A Novel Maximum Power Point Inference Method for Distributed Marine Photovoltaic Monitoring
Energies,
Год журнала:
2025,
Номер
18(11), С. 2760 - 2760
Опубликована: Май 26, 2025
In
actual
operation,
the
output
power
of
distributed
marine
photovoltaic
monitoring
faces
challenges
from
wind,
waves,
and
other
dynamic
motion
factors.
To
address
these
challenges,
this
paper
proposes
a
novel
maximum
point
inference
method
for
monitoring.
First,
digital
fusion
model
has
been
constructed
to
obtain
comprehensive
dataset
system.
Second,
Multilayer
Convolutional
Neural
Networks
(CNN)
are
extract
local
high-frequency
characteristics,
Squeeze
Excitation
Attention
(SE-Attention)
is
employed
capture
global
low-frequency
Long
Short-Term
Memory
(LSTM)
utilized
perform
temporal
modeling
characteristics.
Subsequently,
Crested
Porcupine
Optimizer
(CPO)
algorithm
used
achieve
high-precision
recognition
in
Finally,
effectiveness
verified
through
experiments
simulations.
The
results
indicate
that
exhibits
multi-spectral
with
highest
frequency
at
335.2
Hz
lowest
12.9
Hz.
proposed
enables
efficient
under
conditions,
an
accuracy
98.63%.
Язык: Английский