ACOUSTIC MEASUREMENTS OF PARTIAL DISCHARGE SIGNALS
B.T. Phung, T.R. Blackburn and Z. Liu
School of Electrical Engineering and Telecommunications
University of New South Wales, Australia
Abstract:
The acoustic pressure waves associated with partial discharges in HV power
transformers can be detected with external piezoelectric sensors. The propagation time and the
waveshape of the received signals are affected by factors such as the sensor position, internal
barriers and sensor type. These are investigated in this paper. In particular, it is shown that the
commonly-used resonant sensor gives a cleaner signal. On the other hand, the wideband sensor
enables comparison between low and high frequency components. It was found that as the sensor is
closer to the normal, the higher frequency components become more dominant. This can be utilised
as a new diagnostic method for discharge location. The wavelet transform is used to show changes
in the frequency distribution via the time-scale plot and also to de-noise the signal.
1. INTRODUCTION
Electrical insulation plays an important role in the
performance of high-voltage apparatus as it has to
withstand high electrical stress during operation. Most
power equipment failures are caused by breakdowns of
the insulation. This in turn is often the consequence of
gradually and cumulatively damaging effects of partial
discharges (PD) on the insulation over the years.
Partial discharges are indicative of some defects within
the insulation. Hence, their early detection can
facilitate appropriate repair and prevent costly
breakdowns. In HV oil-filled power transformers, PDs
generate high-frequency electrical pulses and
ultrasonic pressure waves. The latter can propagate
through the oil volume inside the transformer and
eventually reach the tank wall. Such a pressure
disturbance can be detected with external piezoelectric
sensors. The relative difference in the propagation
times of the signals from several sensor positions (or
with respect to the electrical signal) can be used to
determine the discharge location. This provides a
simple and convenient on-line diagnostic method for
locating the discharge site [1-2].
In practice, the location accuracy is often poor due to
the complex nature of the acoustic signals which travel
from the source to the sensor via various paths through
the oil and the tank wall with different propagation
velocities. Complications also arise due to the effects
of signal attenuations, reflections, refractions,
mechanical noise or reverberations, multiple discharge
sources and the presence of internal solid barriers
(transformer core, windings, pressboards). As such,
correct location results are limited to situations where
there is a single dominant discharge source occurring
near the surface or outside the transformer windings.
Extensive research has been carried out with the aim
of improving the location accuracy, e.g. [3-7].
This paper provides further insight about some of the
above-mentioned problems which influence the
waveshapes of the detected acoustic signals and
propagation time. Using a real transformer tank setup
in the laboratory, measurements are carried out with
different types of sensors and internal barriers. The
sensor position in relation to the discharge is varied.
The possibility of recognising different modes of
signal propagation based on their Fourier spectrum
characteristics is explored. The wavelet transform
(WT), a new technique for analysing transient signals,
is utilised to characterise the acoustic waveforms. The
use of WT to denoise the signal is also investigated.
2. ULTRASONIC SIGNAL PROPAGATION
There are two types of pressure waves: longitudinal
and shear. For the longitudinal waves, the motion of
the medium is purely in the direction of propagation.
With the shear waves, the motion is transverse to the
direction of propagation.
Liquids only support the longitudinal waves. For
transformer oil under normal operating conditions, the
propagation velocity is v l ≅ 1400m / s . Transformer
tanks are usually made of steel for which the
longitudinal waves travel faster than the shear waves:
v l ≅ 5900m / s and v s ≅ 3200m / s .
A PD pulse creates a spherical pressure wave of a
longitudinal nature in the oil which then excites a
longitudinal wave and a shear wave in the transformer
tank. Analogous to ray optics, this can be treated as
equivalent to an infinite number of ‘beams’ originating
from the discharge site and propagating equally in all
directions, each arriving at the tank wall with a
different incident angle θ. Thus, apart from the direct
path (straight line between the PD source and the
sensor), there are many other indirect paths that the
ultrasonic signal can travel before reaching the sensor.
Because of the higher propagation velocitity in steel,
the direct path – althought the shortest - is not
necessarily the quickest path.
Discharge
source
Fig.2: Method of measuring the propagation time.
ψ
Y
θ
Tank wall
x
Sensor
X
Fig.1: Model for ultrasonic wave propagations.
Consider Figure 1 where the sensor is at an angle ψ
from the normal. The time it takes for the signal which
follows the path shown in the figure to reach the
sensor is:
x2 +Y 2 X − x
+
(1)
v oil
v steel
The quickest path can be found by setting
dt / dx = 0 and solve for x. This corresponds to the
case where θ is equal to the critical incidence angle:
 v

α = sin −1  oil 
(2)
 v steel 
and the quickest propagation time is:
Y
X − Y tan α
ts =
+
(3)
v oil . cos α
v steel
Equation 3 is valid only if ψ > α . If ψ ≤ α , the direct
path (i.e. θ = ψ ) is the quickest path. The propagation
time for the direct path is:
t=
td =
different paths. However, the signal from the quickest
path is the most critical as it constitutes the wavefront
of the composite signal. The start of this wavefront is
used in the measurement of the propagation time.
X 2 +Y 2
v oil
(4)
In the above equations, v oil and v steel are the
longitudinal propagation velocities in oil and steel
respectively.
The resultant composite signal picked up by the sensor
is an overlapping of signals travelling through many
There are two main techniques for locating the
discharge source. One common method is to
simultaneously record the electrical and ultrasonic
signals. Figure 2 is an example using a digital storage
oscilloscope. By taking the electrical signal as the
reference, the propagation time of the ultrasonic signal
can be determined. This in turn can be used to
calculate the distance between the discharge source
and the sensor (assuming straight line propagation).
Hence the locus of possible discharge locations would
be part of the sphere lying inside the transformer tank
boundary with the sensor as the centre. Measurements
at other sensor positions would give additional loci
and their intersections would provide the discharge
location.
The assumption of straight-line propagation would
result in error in the measurement of the propagation
time. The absolute error is:
(5)
∆t = t s − t d
For ease of demonstrating the magnitude of this error,
assume that the PD source is close to the tank wall so
that X >> Y . Substitute Eqs.3&4 into Eq.5:
 1
1 
. X
(6)
∆t ≈ 
−

 v oil v steel 
The relative error is:
∆t v steel − v oil
≈
(7)
td
v steel
Thus the absolute error tends to increase linearly with
the distance between the sensor and the normal. On the
other hand the relative error remains constant but is
large (76%).
3. SETUP AND MEASUREMENT RESULTS
The experiment was set up using an actual transformer
tank but with the core and windings removed (Figure
3). The dimension of this tank, made from 10mm thick
steel, is 900mm (H) x 1100mm (W) x 600mm (D).
Tank wall
barrier
35cm
PD source
O
C
sensor
30cm
50cm
B
sensor
A
sensor
TOP VIEW
Fig.5: Locations of the discharge source and sensors.
Fig.3: Transformer tank.
The test circuit is shown in Figure 4. A point-to-plane
electrode system, suspended in the oil, was used as the
discharge source. The discharge level, measured with
a conventional PD detector, was about 1000pC at
20kV applied voltage. The position of the discharge
source was fixed for all the measurements taken.
The positions of the PD source and the sensors are
shown in Figure 5. A number of different sensors were
tested. The PAC (type R15I) are resonant sensors with
built-in 40dB pre-amplifier. The typical operation
range is from 100kHz to 450kHz and the resonant
frequency is ~160kHz. The B&K (type 8312) are
broad-band sensors operating over a wider frequency
band. The response is flat within 10dB over the range
100kHz to 1MHz. The PAC (type D9241A) is a lowfrequency sensor. The typical operation range is from
30kHz to 70kHz.
A high-frequency CT clipped onto the earth lead picks
up the electrical PD signal. This is used as the time
reference to determine the propagation time of the
acoustic signals.
55kV
Transformer
30kΩ
Voltmeter
DSO
Bushing
ch2
Oil level
0.5m deep
Point-plane
PD source
ch1
Fig.6: Resonant sensor at point A.
Pressure
wave
Ultrasonic
sensor
HF CT
Fig.4: Test circuit arrangement.
The ultrasonic pressure waves created by the discharge
are detected by the piezoelectric sensors. These
sensors were clamped onto the outer walls of the
transformer tank using magnets. To enable better
coupling of the pressure waves, a thick layer of grease
was applied to the surface of the sensors before they
are attached to the tank wall. The PD source and the
sensors are placed on the same horizontal plane which
is 35cm above the bottom of the tank.
Fig.7: Resonant sensor at point B.
Fig.6 shows the time-domain waveform of the acoustic
signal using the resonant sensor at point A. As the
sensor is at the normal, the direct path is the quickest.
The predicted propagation time is 214µs as compared
to the measured value of 216µs. Note that in this case,
the acoustic signal has a sharp wavefront with the
magnitude reaching its maximum almost at the
beginning.
Fig.8: Resonant sensor at point C.
Fig.9: Resonant sensor at point C, metal barrier.
Fig.10: Resonant sensor at C, cylinder barrier.
Fig.7 corresponds to the resonant sensor at point B.
From Eq.2, the critical incidence angle is 13.73o. This
translates to a distance of 7.33cm from point A. Hence
point B is well outside the critical angle. Compared to
the previous case (Fig.6), there is a marked difference
in the wavefront. The signal is relatively small for
some time before its magnitude changes suddenly. The
latter corresponds to the arrival of the direct path
signal. Using Eq.3, the propagation time for the
quickest path is 293µs. This agrees well with the
measured value of 300µs. The calculated propagation
time for the direct path is 417µs. The time difference
is significant.
Thus for discharge location, it is important to
distinguish between direct and indirect path
propagation. As demonstrated in Figs.6 and 7, this can
be achieved by carefully examining the time-domain
waveforms of the received signals. For Fig.7, the
arrival of the direct path signal can be located by
ignoring the smaller oscillations in the wavefront. This
gives a value of 380µs as compared to the expected
value of 417µs. If the measurement is based on the
largest oscillation, the result is 450µs.
Internal barriers increase the complexity of the
received signals. Fig.8 shows the results for the
resonant sensor on the normal at point C without the
barrier (a flat 10mm thick steel plate). The predicted
propagation time is 250µs which agrees with the
measured value. As expected, the waveform is similar
to that of Fig.6 with a sharp and large wavefront. With
the barrier present, the result is shown in Fig.9.
Although there is no noticeable time shift, the relative
magnitude of the wavefront is reduced. The waveform
is somewhat similar to Fig.7. Thus, although the
sensor is on the normal, its time-domain waveform
tends to indicate otherwise.
Instead of the metal plate barrier, the discharge source
was put inside a cylindrical metal barrier (30cm
diameter). The waveform, shown in Fig.10, is still
somewhat similar to Fig.9. However, there is a
noticeable increase in the propagation time, 275µs as
compared to the expected value of 250µs. Although
the cylindrical barrier has an open top, it can be seen
that it is more effective in blocking the propagation of
the pressure waves.
For comparison with Figs.6 and 7, the results using the
wideband sensor are shown in Figs.11 and 12. The
waveforms are much noisier due to the wider
bandwidth. Also shown are the corresponding
frequency spectra. The higher frequency components
in the 400-500kHz range are clearly dominant in
Fig.11 but become negligible in Fig.12. This suggests
a new discharge location technique. By using a
wideband sensor, one can vary the sensor location
until the higher frequency components in the detected
signal are maximised. This would correspond to the
sensor at the normal. Note that the commonly used
resonant sensor is not suitable as the frequency of
interest is outside its range.
transformation of the signal from the time domain to
the frequency domain causes its time information to be
lost. This is undesirable, particularly when the signal is
non-stationary and its transitory characteristics are
important. The Wavelet Transform produces a timescale view of the signal. In essence, the technique
decomposes a signal into shifted and scaled versions
of an original wavelet. The mathematical details of
wavelet analysis can be found in numerous textbooks,
e.g. [8]. The computations in this paper made use of
the Wavelet Toolbox [9] which is a collections of
functions run under the MatLab environment.
Absolute Values of Ca,b Coefficients for a = 2 4 6 8 10 ...
122
114
106
98
90
scales a
82
74
66
58
50
42
34
26
18
Fig.11: Wideband sensor at point A.
10
2
0.5
1
1.5
time (or space) b
2
2.5
4
x 10
(a)
Absolute Values of Ca,b Coefficients for a = 2 4 6 8 10 ...
122
114
106
98
90
scales a
82
74
66
58
50
42
34
26
18
10
2
0.5
1
1.5
2
2.5
3
time (or space) b
3.5
4
4.5
5
4
x 10
(b)
Fig.13: Wavelet transforms.
Fig.12: Wideband sensor at point B.
4. ANALYSIS USING WAVELET TRANSFORM
The well-known Fourier transform decomposes a
signal into consituent sinusoids of different
frequencies (fundamental and harmonics). Such a
The time-scale plots of the wavelet coefficients for the
wideband signals (Figs.11 and 12) are shown in
Fig.13. The x-axis represents position along the signal
(time) and the y-axis represents scale. The colour at
each point on the plot represents the magnitude of the
wavelet coefficient. The darker shades correspond to
smaller coefficients. Note the large coefficients
occurring at the wavefront in Fig.13(a).
The received signal such as that in Fig.11 is noisy and
hence it is difficult to recognise the wavefront
associated with the quickest path. Wavelet
decomposition can be used to remove the highfrequency noise from the signal. Successive
approximations become less noisy as more high
frequency information is filtered out. Thus this
provides a simple method to de-noise the signal.
The signal of Fig.11 was de-noised using level-5
approximation and Daubechies db3 wavelet. It was
found that in comparison to the original, the de-noised
signal is much cleaner but the fast changing features of
the original signal is lost. In particular, the smoothing
effect on the wavefront would reduce the accuracy of
the measurement of the propagation time.
60
give a low noise signal. For propagation time
measurements, this is advantageous. Although the
wideband sensors give a noisier signal, the sensor
position in relation to the discharge source can be
determined by analysing the higher frequency
components of the spectra. This can be utilised as a
new method for discharge location.
To a certain extent, it is possible to distinguish
between direct and indirect path propagations by
examining the time-domain waveforms. However, it
was shown that the presence of internal barriers could
alter not only the received waveforms but also the
propagation time.
40
The time-scale plot of the wavelet transform is an
interesting and informative way to view the signals.
The technique can also be utilised to de-noise the
signals and thus enhance the detection sensitivity.
20
0
-20
-40
6. REFERENCES
-60
-80
0
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
(a)
60
40
20
0
-20
-40
-60
-80
0
1000
2000
3000
4000
5000
6000
7000
8000
9000 10000
(b)
Fig.14: Original and de-noised signals.
An alternative to overcome such a problem is the
technique called thresholding whereby the details are
discarded only if the magnitudes exceed a certain
limit. The procedure is to examine the details vectors
of the wavelet decomposition, select the appropriate
threshold coefficients and reconstruct the new details
signals. The Matlab toolbox provides two calling
functions: one to calculate the default threshold
parameters and the other to perform the actual denoising. Applying these functions, the result is shown
in Fig.14(b) which clearly reveals the smaller
oscillations at the wavefront.
5. CONCLUSIONS
In this paper, the acoustic signals produced by partial
discharges in oil-filled transformers were studied. It
was shown that the commonly used resonant sensors
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