Wind turbine fault detection based on the transformer model using SCADA data
Engineering Failure Analysis,
Год журнала:
2024,
Номер
162, С. 108354 - 108354
Опубликована: Апрель 27, 2024
The
growth
of
installed
wind
power
worldwide
and
its
significant
contribution
to
the
energy
market
is
mainly
due
evolution
turbines
(WTs)
their
ability
withstand
a
wide
range
dynamic
loads.
WT
failures
can
be
costly
lead
extended
downtime.
Early
detection
such
critical
in
reducing
costs
associated
with
operation
maintenance
(O&M)
tasks
unscheduled
shutdowns
WTs.
This
paper
applies
two
Deep
Learning
(DL)
models
based
on
Transformer
model
predict
IGBT
module
WTs
at
an
onshore
farm
Ecuador.
To
this
end,
SCADA
(Supervisory
Control
Data
Acquisition)
operational
alarm
data
are
used,
together
record
(MR).
These
analyzed
processed,
applying
different
feature
selection
methods.
results
show
that
proposed
perform
well,
high
accuracy
approximate
prediction
4.25
months
before
failure
occurrence.
promising
possibility
using
for
early
accurate
identification
faults
components
Язык: Английский
InfoCAVB-MemoryFormer: Forecasting of wind and photovoltaic power through the interaction of data reconstruction and data augmentation
Applied Energy,
Год журнала:
2024,
Номер
371, С. 123745 - 123745
Опубликована: Июнь 20, 2024
Язык: Английский
Integration of atmospheric stability in wind resource assessment through multi-scale coupling method
Applied Energy,
Год журнала:
2023,
Номер
348, С. 121402 - 121402
Опубликована: Июль 3, 2023
Язык: Английский
A Hybrid Approach to Wind Power Intensity Classification Using Decision Trees and Large Language Models
Renewable Energy,
Год журнала:
2025,
Номер
unknown, С. 123388 - 123388
Опубликована: Май 1, 2025
Язык: Английский
Identifying and understanding how critical landscapes for carbon sequestration respond to development for low carbon energy production: Insight to inform optimal land planning and management strategies
Journal of Environmental Management,
Год журнала:
2025,
Номер
385, С. 125063 - 125063
Опубликована: Май 10, 2025
Язык: Английский
Evaluation of the topology anisotropy effect on wake development over complex terrain based on a novel method and verified by LiDAR measurements
Energy Conversion and Management,
Год журнала:
2024,
Номер
322, С. 119154 - 119154
Опубликована: Окт. 25, 2024
Язык: Английский
A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle
IEEE Transactions on Sustainable Energy,
Год журнала:
2024,
Номер
15(4), С. 2663 - 2674
Опубликована: Июль 30, 2024
Язык: Английский
Neurocontrolled Prediction of Blade Position in Wind Generators
Lecture notes in networks and systems,
Год журнала:
2024,
Номер
unknown, С. 466 - 481
Опубликована: Янв. 1, 2024
Язык: Английский
An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map
Journal of Intelligent & Fuzzy Systems,
Год журнала:
2023,
Номер
45(6), С. 11425 - 11437
Опубликована: Окт. 3, 2023
The
wake
effect
of
wind
farm
can
reduce
the
incoming
speed
at
turbine
located
in
downstream
direction,
resulting
decrease
global
output.
WRF
model
adopts
a
three-layer
two-way
nested
grid
division
scheme
to
simulate
upper
atmospheric
circulation,
obtain
speed,
direction
and
other
data
that
truly
reproduce
fluid
characteristics
regional
group.
boundary
conditions
solution
CFD
are
set,
computational
dynamics
region
is
obtained.
coupled
with
CFD,
Fitch
introduced
into
it.
By
introducing
drag
coefficient
calculation
turbulent
kinetic
energy
CFD-WRF
coupling
model,
field
simulated
online.
Monte
Carlo
sampling
method
used
random
resource
then
sampled
calculate
group
output
farms,
evaluate
impact
on
treatment.
experimental
results
show
this
effectively
analyze
characteristic
field,
time
RANS
about
3
s.
Due
effect,
overall
efficiency
will
be
significantly
reduced.
Язык: Английский