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Partial Discharge Analysis in High Frequency Transformer Based on High Frequency Current Transducer.pdf


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Energies 2018, 11, 1997

4 of 13

non-intrusive detection method can realize online monitoring of PD. The output characteristic of this
HFCT 2018,
is shown
in Figure
3.
Energies
11, x FOR
PEER REVIEW
4 of 13

Figure 3.
3. Output
Outputcharacteristic
characteristic of
of an
an iHFCT‐54
iHFCT-54 sensor.
sensor.
Figure

3.
3. Denoise
Denoise Processing
Processing of
of PD
PD Signal
Signal
A
A typical
typical PD
PD current
current signal
signal is
is shown
shown in
in Figure
Figure 44 when
when the
the power
power supply
supply exerted
exerted aa peak‐to‐peak
peak-to-peak
amplitude
output
amplitude
of of
HFCT
is
amplitude of
of 20
20 kV
kVand
andfrequency
frequencyofof44kHz
kHzsinusoidal
sinusoidalvoltage.
voltage.The
The
output
amplitude
HFCT
U
HFCT
.
is UHFCT .
PD
PD activity
activity was
was detected
detectedon
onboth
bothpositive
positiveand
andnegative
negativeaxes
axesin
inaaperiod,
period,according
accordingto
toFigure
Figure4.4.
U
attained
a
peak‐to‐peak
value
of
1.61
V
with
noise
of
0.232
V,
which
reduced
the
PD
magnitude
UHFCT
attained
a
peak-to-peak
value
of
1.61
V
with
noise
of
0.232
V,
which
reduced
the PD
HFCT
accuracy.
An
improved
signal‐to‐noise
ratio
(SNR)
of
PD
signal
is
required.
Consequently,
empirical
magnitude accuracy. An improved signal-to-noise ratio (SNR) of PD signal is required. Consequently,
mode
decomposition
(EMD) was
usedwas
to improve
the SNR the
in this
becausebecause
of its merits
empirical
mode decomposition
(EMD)
used to improve
SNRmanuscript
in this manuscript
of its
on
processing
nonlinear
and
non‐stationary
signal.
merits on processing nonlinear and non-stationary signal.

Figure 4.
4. Typical
partial discharge
discharge (PD)
(PD) current
current and
and voltage
voltage signal
signal under
under ff =
= 44 kHz
= 20 kV.
Figure
Typical partial
kHz and
and U
Up‐p
p-p = 20 kV.

3.1.
3.1. Process
Process of
of Empirical
Empirical Mode
Mode Decomposition
Decomposition
EMD
dealing
with
nonlinear
non‐stationary
signals
because
it hasitgreat
self‐
EMDhas
hasan
anadvantage
advantageinin
dealing
with
nonlinear
non-stationary
signals
because
has great
adaptability.
EMD
is
based
on
the
Hilbert–Huang
transform.
The
Hilbert–Huang
transform
assumes
self-adaptability. EMD is based on the Hilbert–Huang transform. The Hilbert–Huang transform
that
all data
simple internal
modes called
intrinsic
mode functions
(IMFs)
assumes
thatcontain
all datadifferent
contain different
simple oscillation
internal oscillation
modes
called intrinsic
mode functions
[29].
In [29].
this way,
data are
superimposed
by many
different
IMFs IMFs
whose
amplitude
and
(IMFs)
In thiscomplex
way, complex
data
are superimposed
by many
different
whose
amplitude
frequency
vary
as
a
function
of
time.
Based
on
such
an
assumption,
the
process
of
EMD
to
process
and frequency vary as a function of time. Based on such an assumption, the process of EMD to process
signals is shown in Figure 5.
signals is shown in Figure 5.