Science and Technology for Energy Transition,
Journal Year:
2024,
Volume and Issue:
80, P. 9 - 9
Published: Nov. 13, 2024
Wind
Power
Forecasting
(WPF)
has
gained
considerable
focus
as
a
crucial
aspect
of
the
successful
integration
and
operation
wind
power.
However,
due
to
stochastic
unstable
nature
wind,
it
poses
real
challenge
effectively
analyze
correlations
among
multiple
time
series
data
for
accurate
prediction.
In
our
study,
an
end-to-end
framework
called
Dynamic
Graph
structure
Spatio-Temporal
representation
learning
(DSTG)
is
proposed
achieve
stable
power
forecasting
by
constructing
graph
capture
critical
features
in
data.
Specifically,
Structure
Learning
(GSL)
module
introduced
dynamically
construct
task-related
correlation
matrices
via
backpropagation
mitigate
inherent
inconsistency
randomness
Additionally,
dual-scale
temporal
(DTG)
further
explore
implicit
spatio-temporal
at
fine-grained
level
using
different
skip
connections
from
constructed
Finally,
comprehensive
experiments
are
performed
on
collected
Xuji
Group
(XGWP)
dataset,
results
show
that
DSTG
outperforms
state-of-the-art
methods
10.12%
average
root
mean
square
error
absolute
error,
demonstrating
effectiveness
DSTG.
conclusion,
model
provides
promising
approach.
International Transactions on Electrical Energy Systems,
Journal Year:
2024,
Volume and Issue:
2024, P. 1 - 24
Published: May 22, 2024
The
integration
of
renewable
energy
sources
into
power
systems
has
increased
significantly
in
recent
years.
Among
various
types
energy,
the
use
wind
is
growing
rapidly
due
to
its
low
operating
cost,
wide
distribution
worldwide,
and
no
greenhouse
gas
emissions.
However,
integrated
with
may
face
stability
reliability
issues
intermittent
nature
power.
Therefore,
connected
farms,
it
usually
required
some
compensators
such
as
static
synchronous
series
compensator
(SSSC)
increase
system
performance
under
abnormal
conditions.
On
other
hand,
for
an
SSSC
be
effective
improving
performance,
must
equipped
a
suitable
controller.
In
this
paper,
fuzzy
logic
controller
(FLC)
used
because
advantages
over
conventional
controllers.
Extensive
research
been
conducted
turbines
which
or
FLC
used;
however,
their
simultaneous
application
received
less
attention.
article
aims
fill
gap.
proposed
method
implemented
on
two
simulation
results
are
analyzed.
both
systems,
dynamic
behavior
three
different
farms
examined.
first
second
either
squirrel
cage
induction
generator
(SCIG)
doubly-fed
(DFIG)
used,
whereas
third
one
combined
farm
(CWF),
equal
number
SCIG
DFIG
employed.
DFIG,
also
utilized.
Furthermore,
employed
improve
efficacy.
A
proportional
integral
(PI)
considered
SSSC,
compared
results.
confirm
superiority
PI
Energies,
Journal Year:
2025,
Volume and Issue:
18(7), P. 1571 - 1571
Published: March 21, 2025
Wind
power
prediction
plays
a
crucial
role
in
enhancing
grid
stability
and
wind
energy
utilization
efficiency.
Existing
methods
demonstrate
insufficient
integration
of
multi-variate
features,
such
as
speed,
temperature,
humidity,
along
with
inadequate
extraction
correlations
between
variables.
This
paper
proposes
novel
multi-scale
method
named
variational
mode
decomposition
informer
(MSVMD-Informer).
First,
modal
module
is
designed
to
decompose
univariate
time-series
features
into
multiple
scales.
Adaptive
graph
convolution
applied
extract
scales,
while
self-attention
mechanisms
are
utilized
capture
temporal
dependencies
within
the
same
scale.
Subsequently,
feature
fusion
proposed
better
account
for
inter-variable
correlations.
Finally,
reconstructed
by
integrating
aforementioned
modules,
enabling
forecasting.
The
was
evaluated
through
comparative
experiments
ablation
studies
against
seven
baselines
using
public
dataset
two
private
datasets.
Experimental
results
that
our
achieves
optimal
metric
performance,
its
lowest
MAPE
scores
being
1.325%,
1.500%
1.450%,
respectively.
