Smart Science,
Journal Year:
2024,
Volume and Issue:
12(3), P. 495 - 518
Published: June 4, 2024
Among
the
most
significant
non-linear
challenges
for
power
network
design
and
smooth
functioning
of
current
modern
updated
system
networks
is
optimum
flow
(OPF)
problem.
Importance
electrical
modeling
has
recently
come
to
light
due
incremental
use
energy
from
renewable
sources
in
systems
networks.
The
goal
wind,
solar
tidal
recreate
issue
OPF.
In
this
work,
Weibull,
Lognormal,
also
Gumbel
probability
distribution
functions
were
applied
simulate
uncertainties
photovoltaic,
system.
Additionally,
by
adding
test
scenarios
unpredictable
involving
minimization
cost
function,
loss
active
power,
voltage
deviation,
increase
stability
voltage.
accordance
with
chosen
thermal
producing
units,
solutions
evaluated
using
different
locations
IEEE
30-bus
testing
that
incorporate
sources.
proposed
planning
problem
was
solved
multi-objective
function
where
unified
controller
are
utilized
as
flexible
AC
transmission
controllers
via
introduced
optimization
algorithms
simulation
outcomes
aforementioned
technique
have
been
compared
Multi
Objective
Adaptive
Guided
Differential
Evolution
algorithms.
adaptive
improved
flower
pollination
algorithm
(AIFPA)
a
strong
reliable
presented
work.
AIFPA
can
efficiently
deal
many
kinds
high-complexity
objective
regions
situations.
Utilizing
an
system,
suggested
approaches'
performance
examined
range
functions.
results
obtained
effective
finding
optimal
solution
meta-heuristic
reported
literature.
IEEE Systems Journal,
Journal Year:
2023,
Volume and Issue:
17(3), P. 3938 - 3949
Published: March 21, 2023
This
article
presents
the
single
objective
optimal
power
flow
(OPF)
formulation
incorporating
both
renewable
energy
sources,
and
voltage
source
converter-based
multiterminal
direct
current
transmission
lines,
simultaneously.
To
solve
formulated
OPF
problem,
powerful
metaheuristic
optimization
algorithms
including
adaptive
guided
differential
evolution,
marine
predators
algorithm,
atom
search
optimization,
stochastic
fractal
(SFS),
fitness-distance
balance-based
SFS
(FDB-SFS)
are
employed.
The
performance
of
is
tested
for
minimization
fuel
cost,
pollutant
emissions
thermal
generators,
deviation,
active
loss
in
a
modified
IEEE
30-bus
network.
simulation
results
give
that
FDB-SFS
achieved
best
on
cost
(
$786.5361
\,
{\$}/\text{h}$
),
with
valve
point
effect
notation="LaTeX">$815.6644
\,{\$}/\text{h}$
emission-carbon
tax
notation="LaTeX">$820.5991
).
In
addition,
reduced
deviation
values
by
14.2587%
6.7438%
compared
to
SFS.
nonparametric
Wilcoxon
Friedman
statistical
test
confirmed
an
effective
robust
algorithm
can
be
used
introduced
problem.
Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
57(6)
Published: May 9, 2024
Abstract
In
this
study,
the
Learning
Search
Algorithm
(LSA)
is
introduced
as
an
innovative
optimization
algorithm
that
draws
inspiration
from
swarm
intelligence
principles
and
mimics
social
learning
behavior
observed
in
humans.
The
LSA
optimizes
search
process
by
integrating
historical
experience
real-time
information,
enabling
it
to
effectively
navigate
complex
problem
spaces.
By
doing
so,
enhances
its
global
development
capability
provides
efficient
solutions
challenging
tasks.
Additionally,
improves
collective
capacity
incorporating
teaching
active
behaviors
within
population,
leading
improved
local
capabilities.
Furthermore,
a
dynamic
adaptive
control
factor
utilized
regulate
algorithm’s
exploration
abilities.
proposed
rigorously
evaluated
using
40
benchmark
test
functions
IEEE
CEC
2014
2020,
compared
against
nine
established
evolutionary
algorithms
well
11
recently
algorithms.
experimental
results
demonstrate
superiority
of
algorithm,
achieves
top
rank
Friedman
rank-sum
test,
highlighting
power
competitiveness.
Moreover,
successfully
applied
solve
six
real-world
engineering
problems
15
UCI
datasets
feature
selection
problems,
showcasing
significant
advantages
potential
for
practical
applications
problems.
Smart Science,
Journal Year:
2024,
Volume and Issue:
12(3), P. 495 - 518
Published: June 4, 2024
Among
the
most
significant
non-linear
challenges
for
power
network
design
and
smooth
functioning
of
current
modern
updated
system
networks
is
optimum
flow
(OPF)
problem.
Importance
electrical
modeling
has
recently
come
to
light
due
incremental
use
energy
from
renewable
sources
in
systems
networks.
The
goal
wind,
solar
tidal
recreate
issue
OPF.
In
this
work,
Weibull,
Lognormal,
also
Gumbel
probability
distribution
functions
were
applied
simulate
uncertainties
photovoltaic,
system.
Additionally,
by
adding
test
scenarios
unpredictable
involving
minimization
cost
function,
loss
active
power,
voltage
deviation,
increase
stability
voltage.
accordance
with
chosen
thermal
producing
units,
solutions
evaluated
using
different
locations
IEEE
30-bus
testing
that
incorporate
sources.
proposed
planning
problem
was
solved
multi-objective
function
where
unified
controller
are
utilized
as
flexible
AC
transmission
controllers
via
introduced
optimization
algorithms
simulation
outcomes
aforementioned
technique
have
been
compared
Multi
Objective
Adaptive
Guided
Differential
Evolution
algorithms.
adaptive
improved
flower
pollination
algorithm
(AIFPA)
a
strong
reliable
presented
work.
AIFPA
can
efficiently
deal
many
kinds
high-complexity
objective
regions
situations.
Utilizing
an
system,
suggested
approaches'
performance
examined
range
functions.
results
obtained
effective
finding
optimal
solution
meta-heuristic
reported
literature.
