Application of Artificial Intelligence in Wind Power Systems
Applied Sciences,
Год журнала:
2025,
Номер
15(5), С. 2443 - 2443
Опубликована: Фев. 25, 2025
Wind
energy
is
an
important
renewable
source,
and
artificial
intelligence
(AI)
plays
role
in
improving
its
efficiency,
reliability
cost-effectiveness
while
minimizing
environmental
impact.
Based
on
analysis
of
the
latest
scientific
literature,
this
article
examines
AI
applications
for
entire
life
cycle
wind
turbines,
including
planning,
operation
decommissioning.
A
key
focus
AI-driven
maintenance,
which
reduces
downtime,
improves
extends
lifetime
turbines.
also
optimizes
design
particularly
development
aerodynamically
efficient
blade
shapes
through
rapid
iterations.
In
addition,
helps
to
reduce
impact
environment,
e.g.,
by
reducing
bird
collisions,
forecasting,
essential
balancing
flows
power
systems.
Despite
benefits,
face
challenges,
algorithmic
errors,
data
accuracy,
ethical
concerns
cybersecurity
risks.
Further
testing
validation
algorithms
needed
ensure
their
effectiveness
advancing
Язык: Английский
A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
Applied Sciences,
Год журнала:
2025,
Номер
15(7), С. 3758 - 3758
Опубликована: Март 29, 2025
Wind
energy
represents
a
solution
for
reducing
environmental
impact.
For
this
reason,
research
studies
the
elements
that
propose
optimizing
wind
production
through
intelligent
solutions.
Although
there
are
address
optimization
of
turbine
performance
or
other
indirectly
related
factors
in
production,
remains
topic
insufficiently
explored
and
synthesized
literature.
This
how
machine
learning
(ML)
techniques
can
be
applied
to
optimize
production.
aims
study
systematic
applications
ML
identify
analyze
key
stages
optimized
Through
research,
case
highlighted
by
which
methods
proposed
directly
target
issue
power
process
turbines.
From
total
1049
articles
obtained
from
Web
Science
database,
most
studied
models
context
artificial
neural
networks,
with
478
papers
identified.
Additionally,
literature
identifies
224
have
random
forest
114
incorporated
gradient
boosting
about
power.
Among
these,
60
specifically
addressed
aspect
allows
identification
gaps
The
notes
previous
focused
on
forecasting,
fault
detection,
efficiency.
existing
addresses
indirect
component
performance.
Thus,
paper
current
discusses
algorithms
processes,
future
directions
increasing
efficiency
turbines
integrated
predictive
methods.
Язык: Английский
Metaheuristic Optimized Semi-Active Structural Control Approaches for a Floating Offshore Wind Turbine
Applied Sciences,
Год журнала:
2024,
Номер
14(23), С. 11368 - 11368
Опубликована: Дек. 5, 2024
Among
all
the
existing
possibilities
within
renewable
energies
field,
wind
energy
stands
out
due
to
significant
expansion
of
offshore
turbines
installed
in
coastal
and
deep-sea
areas.
Although
latter
represent
considerable
generation
potential
their
larger
size
location
areas
strong
winds,
they
are
exposed
harsh
environmental
disturbances,
particularly
waves,
causing
these
structures
experience
vibrations,
increasing
this
way
fatigue,
reducing
efficiency,
leading
higher
maintenance
operational
costs.
In
work,
vibration
reduction
is
achieved
using
two
structural
control
systems
for
a
5
MW
barge-type
floating
turbine
(FOWT),
tuned
via
metaheuristic
method,
with
genetic
algorithms
(GAs).
Firstly,
standard
deviation
Top
Tower
Displacement
(TTD)
used
as
cost
function
GA
optimize
passive
Tuned
Mass
Damper
(TMD),
resulting
suppression
rate
34.9%
compared
reference
TMD.
Additionally,
semi-active
based
on
gain
scheduling
approach
proposed.
one
approaches,
TMD
parameters
optimized
amplitude
oscillations,
achieving
45.4%.
second
approach,
real
time
identified
wave
frequencies,
demonstrating
superior
performance
medium-high
frequencies
other
TMDs.
Язык: Английский