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.
Biomimetics,
Journal Year:
2024,
Volume and Issue:
9(7), P. 419 - 419
Published: July 8, 2024
The
football
team
training
algorithm
(FTTA)
is
a
new
metaheuristic
that
was
proposed
in
2024.
FTTA
has
better
performance
but
faces
challenges
such
as
poor
convergence
accuracy
and
ease
of
falling
into
local
optimality
due
to
limitations
referring
too
much
the
optimal
individual
for
updating
insufficient
perturbation
agent.
To
address
these
concerns,
this
paper
presents
an
improved
called
IFTTA.
enhance
exploration
ability
collective
phase,
proposes
fitness
distance-balanced
strategy.
This
enables
players
train
more
rationally
phase
balances
exploitation
capabilities
algorithm.
further
perturb
agent
FTTA,
non-monopoly
extra
strategy
designed
get
rid
optimum.
In
addition,
population
restart
then
boost
diversity
paper,
we
validate
IFTTA
well
six
comparison
algorithms
CEC2017
test
suites.
experimental
results
show
strong
optimization
performance.
Moreover,
several
engineering-constrained
problems
confirm
potential
solve
real-world
problems.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 97806 - 97832
Published: Jan. 1, 2024
This
paper
addresses
the
critical
challenge
of
optimal
allocation
Thyristor-Controlled
Series
Compensator
(TCSC)
devices
in
transmission
power
systems
through
an
innovative
optimization
framework.
Leveraging
Enhanced
Gradient-Based
Algorithm
(EGBA)
augmented
with
a
crossover
operator,
proposed
methodology
seeks
to
promote
diversity
solutions
generated
each
iteration,
aiming
maximize
efficiency
networks.
The
algorithm
incorporates
key
components
such
as
Gradient
Search
Process
(GSP)
and
Local
Escaping
(LEP)
guide
exploration
process
prevent
premature
convergence
suboptimal
solutions.
Additionally,
novel
addition
EGBA,
facilitates
exchange
TCSC
configurations
between
solutions,
contributing
solution
potentially
revealing
allocations.
Initially,
EGBA
GBA
performances
are
estimated
using
CEC
2017
benchmarks.
Moreover,
assess
practical
applicability
suggested
it
is
specifically
tailored
implemented
enhance
operation
systems.
primary
objective
minimize
technical
losses,
considering
varying
numbers
experimentation
on
two
distinct
IEEE
systems,
one
30
buses
another
57
buses.
results
analyzed
validate
ability
method
optimizing
addressing
losses.
significantly
reduces
losses
compared
original
both
tested
In
first
system,
achieved
0.85%,
2.99%,
1.32%
lower
than
when
for
one,
two,
three
devices,
respectively.
addition,
enhancing
security
margin
lines
involved
optimize
flow
besides
minimization
function
Similarly,
second
outperformed
by
5.19%,
6.32%,
5.12%
same
configurations.
simulation
demonstrate
that
not
only
more
effective
but
also
efficient
other
recent
approaches.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(14), P. e34326 - e34326
Published: July 1, 2024
This
article
introduces
an
innovative
application
of
the
Enhanced
Gorilla
Troops
Algorithm
(EGTA)
in
addressing
engineering
challenges
related
to
allocation
Thyristor
Controlled
Series
Capacitors
(TCSC)
power
grids.
Drawing
inspiration
from
gorilla
group
behaviors,
EGTA
incorporates
various
methods,
such
as
relocation
new
areas,
movement
towards
other
gorillas,
migration
specific
locations,
following
silverback,
and
engaging
competitive
interactions
for
adult
females.
Enhancements
involve
support
exploitation
exploration,
respectively,
through
two
additional
strategies
periodic
Tangent
Flight
Operator
(TFO),
Fitness-based
Crossover
Strategy
(FCS).
The
paper
initially
evaluates
effectiveness
by
comparing
it
original
GTA
using
numerical
CEC
2017
single-objective
benchmarks.
Additionally,
recent
optimizers
are
scrutinized.
Subsequently,
suitability
proposed
TCSC
apparatuses
transmission
systems
is
assessed
simulations
on
IEEE
grids
30
57
buses,
employing
apparatus
quantities.
A
comprehensive
comparison
conducted
between
EGTA,
GTA,
several
prevalent
techniques
literature
all
applications.
According
average
attained
losses,
presented
displays
notable
reductions
losses
both
first
second
when
compared
GTA.
Specifically,
system,
achieves
1.659
%,
2.545
4.6
%
optimizing
one,
two,
three
apparatuses,
respectively.
Similarly,
suggested
6.096
7.107
4.62
GTA's
findings
considering
apparatuses.
underscore
superior
efficiency
over
contemporary
systems.
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.
