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TSINGHUA SCIENCE AND TECHNOLOGYISSN 1007-0214 14/20 pp598-604Volume 10, Number 5, October 2005
On-Line Partial Discharge Monitoring and Diagnostic
System for Power Transformer*
LIN Du ( )1, JIANG Lei ( )1, LI Fuqi ( )1,
ZHU Deheng ( )1,**, TAN Kexiong ( )1, WU Chengqi ( )2,
JIN Xianhe ( )3, WANG Changchang ( )3 CHENG T. C. ( )3
1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China; 2. Electric Power Corp. (Group), Shenyang 110013, China;
3. University of Southern California Los Angeles, CA 90089-0271, USA
Abstract: This paper introduces a computerized on-line partial discharge (PD) monitoring and diagnostic
system for transformers. The system, which is already in use in a power station, uses wide-band active
transducers and a data acquisition unit with modularized and exchangeable components. The system
software is a power equipment monitoring and diagnostic system, which is based on the component object
model, and was developed for monitoring multiple parameters in multiple power supply systems. The
statistical characteristics of PDs in power transformers were studied using 7 experimental models for
simulating PDs in transformers and 3 models for simulating interfering discharges in air. The discharge
features were analyzed using a 3-D pattern chart with a three-layer back-propagation artificial neural network
used to recognize the patterns. The results show that PDs in air and oil can be distinguished. The model can
be used for interference rejection on-line monitoring of partial discharge in transformers.
Key words: on-line monitoring; partial discharge; diagnostic; transformer
Introduction
The reliability of power equipment influences the
stability of a power system. On-line monitoring is an
effective approach to prevent malfunctions of power
equipment. Various monitoring systems have been
developed, from monitoring a single parameter of a
single device to monitoring multiple parameters of
multiple component systems. Partial discharges (PDs)
are the major cause of deterioration and breakdown of
the insulation in power equipment. Significant progress
in PD monitoring systems has been achieved in the
past twenty years[1,2]
. Excellent quality A/D units are
now readily available for digital signal processing
(DSP) methods, e.g., digital filtering, to collect even
more signal information, even when the signal-to-noise
ratio (SNR) is rather low.
The system introduced in this paper has
high-frequency current transducers (HFCTs) to pick up
the PD signals, acoustic transducers used mainly for
discharge location, and a data acquisition unit (DAU)
with a program-controlled filter and a program-
controlled A/D unit to acquire real time PD data. A
power equipment monitoring and diagnostic system
(PEMDS), based on the component object model
(COM), controls the DAU, processes the original data,
and provides many other tools. The data is presented as
Received: 2004-02-03; revised: 2004-05-17
Supported by the National Natural Science Foundation of China,
the Northeastern Electric Power Corp. (Group) (No. 59637200),
and the National Foundation of USA
To whom correspondence should be addressed.
E-mail: [email protected]; Tel: 86-10-62782166
LIN Du et al On-Line Partial Discharge Monitoring and Diagnostic 599
a -q/q-n/ -n 2-D pattern chart and a -q-n 3-D
pattern chart, where represents the phase at which
the PD occurs, q stands for the discharge quantity,
and n denotes the density, or more exactly, the
number of PD occurrences within one second. In
consideration of the various effects of different
conditions, the whole system including hardware
and software is composed of modularized and
exchangeable components.
To predict the fault patterns and severity levels
from monitored discharge data, pattern recognition
methods are widely used. In this paper, experimental
models for simulating PDs in a transformer and a
group of three-layer back-propagation artificial
neural networks (ANNs) are used to recognize the
PD patterns.
The developed system was installed in a power
station in the Inner Mongolia Autonomous Region,
China to monitor three single phase transformers
(500 kV, 3 240 MW).
1 System Hardware
As shown in Fig. 1, the whole system consists of
transducers, a DAU, and a personal computer (PC) as
the main computer. Because of the large amount of
data, the DAU and the PC are connected via the
Ethernet. For safety reasons, the network is
implemented using optical fibers. In most cases, it is
unnecessary for the DAU to run uninterrupted. The
power supply for most of the DAU circuit can,
therefore, be turned on or off with a command from the
main computer.
HFCTs are installed at the neutral terminal, the
high-voltage bushing terminal, the core ground
terminal, and the bushing ground terminal of the
transformer. The HFCTs are active transducers using
Rogowski coils with ferromagnetic cores[3,4]. Their
bandwidth is 10 kHz-1.5 MHz.
Figure 2 shows the DAU block diagram. The
program controlled filter provides a skip function, a
150-450 kHz band-pass, and an 80-180 kHz band-pass.
When the interference is not serious, the skip option is
recommended, thereby allowing more frequency
components and less distortion in the signal. Most
units are modularized to give the system adaptability
and expandability.
Fig. 1 System block diagram
CT1, HFCT on clamp; CT2, HFCT on core; CT3, HFCT on tank;
CT4, HFCT on neutral; CT5, HFCT on HV bushing; Ac, acoustic
transducer; Ps, DAU power switch; Oc1, optical cable; Oc2,
optical cable; PC, main computer
Fig. 2 Data acquisition unit
The program controlled A/D convertor is a 12-bit
high sampling rate A/D card. The sampling rate ranges
from 100 kHz to 10 MHz, and each channel has a
512 KB cache. The A/D card supports synchronized
conversion of 4 channels and has some DSP methods.
The system can monitor multiple power
transformers. As shown in Fig. 3, each DAU monitors
a transformer with all of them connected to the main
computer via a hub or a switch. The main computer
can provide monitored data through its special TCP/IP
service so the LAN user can browse the main computer
if his terminal supports the special service. If the main
computer has a unique WAN IP, the WAN user can
also browse its data.
Tsinghua Science and Technology, October 2005, 10(5): 598 604600
Fig. 3 Monitoring system network topology
2 System Software
Monitoring systems have become so complex that
coordinating the development work is a serious
problem. More complex monitoring systems are more
difficult to develop and maintain. New methods and
new techniques must, therefore, be used to facilitate
development and maintenance.
The COM is a binary standard. As long as
applications follow the COM standard, different
applications from different sources can communicate
with each other across process boundaries. All access
to a COM object is through pointers to interfaces.
2.1 PEMDS structure
In PEMDS, the software system is divided into the
main framework, the data processing methods, and the
monitoring sub-systems which directly control the
DAUs. The data processing methods and monitoring
sub-systems are based on distributed component object
module (DCOM).
In PEMDS, a data processing method is considered
to be a black box that accepts data and then outputs the
result. The interface IDataProcessor is designed to
describe the method, and the interface IdataConfigure-
UI is designed to manage the parameters of the data
processing method.
Most monitoring sub-systems allow: installation,
parameter configuration, and data collection. In
PEMDS, these functions are the IDeviceSetup,
Idevice-Config, and IdeviceObject interfaces.
Figure 4 illustrates the PEMDS block diagram. In
PEMDS, the workspace is the core of the main
framework which calls the proper interfaces to deal
with customers requests. When the workspace receives
a data processing request from a customer, e.g., fast
Fourier transform (FFT), the workspace calls Idata-
ProcessorInfo to get the component. The workspace
also calls the IDataConfigureUI interface to configure
the parameters, e.g., FFTUI, and the IDataProcessor
interface to create an instance of the component, e.g.,
FFT to process the data. In the procedure, the
workspace does not call any concrete component
directly but instead calls standard interfaces to
manipulate the components. PEMDS uses a rather
similar method to schedule the monitoring sub-system.
Fig. 4 PEMDS block diagram
2.2 Programmed data processing tool
A mechanism is needed to dynamically manage a
procedure composed of a series of data processing
methods so that, the system does not need to be
redesigned whenever the procedure is changed.
PEMDS provides a programmed data processing tool
(PDPT) to support this mechanism. As with the data
processor interface and other related interfaces, the
PDPT supports this mechanism in a simple manner.
The procedure is divided into a series of individual
data processing methods, with the PDPT recording all
the identifiers of these methods according to certain
standards into a special table named the table of data
processing (TDP). Table 1 illustrates the TDP contents.
Every record includes the input data name, the
identifier of the data processing method, the method
configuration, and the output data name. The PDPT
can then record a data processing procedure and
restore it at anytime.
LIN Du et al On-Line Partial Discharge Monitoring and Diagnostic 601
Table 1 TDP contents
Input data
name
Data processing
method
Method
configuration
Output data
name
Step 1 Original FFT filter X A1
Step 2 A1 FFT filter X A2
Step 3 A2 Adaptive filter X Result
3 On-Line Measurement
The system has been installed and in operation in a
power station since 1999. On-line calibration was used
to quantify the apparent PD charges[5]
. Calibration
pulses (CPs) were injected from the tap terminal of the
high voltage bushing to simulate a PD inside the
transformer. The pulse amplitudes were 25 V with a
coupling capacitance of 730 pF, so the equivalent
apparent charge was 18 250 pC.
Typical results for the measured signals (with CPs)
at the neutral terminal and the spectral analysis of
noise (without CPs) are shown in Fig. 5. Figure 5a
shows that the noise amplitude at the neutral is too
high to distinguish the CPs.
As shown in Fig. 5b, the noise is mainly continuous
sinusoidal (narrow band) noise, with most of the
energy located in the range of 0-500 kHz. The
frequency bands for carrier communication systems
are also located at the range of 0-500 kHz (Table 2).
Therefore, the continuous sinusoidal noise is mainly
caused by carrier communications.
Fig. 5 Original signal and spectrum (10 M samples/s)
Table 2 Carrier frequency (kHz)
Transmitting Receiving
Line 1 428-432 476-480
Line 2 80-84, 152-156,
112-116
96-100, 136-140,
128-132
Various anti-interference techniques are used for
on-line PD monitoring[6]
. Spectrum analysis based on
fast Fourier transforms (FFT) can effectively suppress
narrow band interference[7,8]
, so it was used in the
system. Many studies have also shown that a high-pass
finite impulse response (FIR) filter with the Remez
algorithm based on the Chebyshev weighted
approximation can effectively suppress both narrow
band noise and repetitive pulses. The processed
waveforms are shown in Fig. 6. The used filters are 99
orders and their low limiting frequencies are 500 kHz
(Fig. 6a) and 1200 kHz (Fig. 6b). The higher-
frequency filter (Fig. 6b) is more effective than the
lower frequency (Fig. 6a); and the CPs can be
distinguished because of the significant attenuation of
noise. The frequency limit of the high-pass digital filter
can be raised only when the analog signal channel
bandwidth is very wide and the A/D sampling rate is
very fast.
Fig. 6 Results of digital high-pass filter
4 PD Pattern Recognition
The discharge process and its development are very
complex. Different methods have been developed not
only to analyze PDs but also to discriminate between
different discharges patterns. Neural networks are
widely used in this area. The following sections
Tsinghua Science and Technology, October 2005, 10(5): 598 604602
describe a method using the statistical characteristics
of the -q-n 3-D pattern chart to analyze the types and
severity levels of the PDs[9]
.
4.1 Laboratory tests
On-site data cannot be easily used to build a library for
identifying PD patterns. Moreover, the discharge
characteristics of real PDs are not easily identified.
Therefore, some models were built to simulate typical
transformer PDs in the laboratory. The main two forms
of PDs in transformers are gap discharges and surface
discharges. Therefore, the models were designed to
generate different forms of these discharges (Fig. 7 and
Fig. 8). The models are then referred to as Oil 1-5 and
Air 1-3.
Fig. 7 Oil discharge models
Fig. 8 Air discharge models
The characteristics of PDs of different severity
levels were measured by using three different voltages
for each test. For each voltage, 10 data samples were
obtained, each with a length of 50 power frequency
periods, to produce the -q-n 3-D pattern chart.
The tests were performed in a 2-coat shielding room.
A flexible conduit was used for the high voltage lead
wire to minimize the interference. All the tests were
verified to ensure that the discharge occurs at the
desired place.
4.2 Data processing
In the -q-n 3-D pattern chart, and n are both linear
and q logarithmic. Both (1 circle, 360 ) and q are
divided into 20 divisions. The density (n) is given by
1
( , )
( , ) 50,
M
k i jk
i j
n Qn Q
M
where M is the number of sampling periods. In these
calculations, M 23; since only data for 23 of the 50
power frequency periods was used. nk( i, Qj) is the
PD number of the k period in the area limited by ( i–1,
Qj–1 ), ( i–1, Qj ), ( i, Qj–1), and ( i–1, Qj–1).
The density (n) of the PD was normalized before
further processing using "local normalizing", where the
normalization of each sample was based on the
maximum value of sample. The 20 20 tabular -q-n3-D pattern charts were then transformed into a 400-
dimensional input vector for the ANN.
To recognize both types of PD and the severity level
requires a very complex ANN. The whole recogni-
tion process is therefore divided into several steps with
three tiers of ANNs used as illustrated in Fig. 9.
Fig. 9 Block diagram of three tier ANN networks
4.3 Recognition results
The recognition results are shown in Tables 3, 4, and 5.
The relatively high discrimination rate may occur since
only one model was used to simulate each type of PD.
If several different models were used to simulate the
same type of PD with all the signals mixed together for
training, the discrimination rate would probably be
LIN Du et al On-Line Partial Discharge Monitoring and Diagnostic 603
lower.
Table 3 Recognition result of the first tier (air/oil)
Context
type
Number of
samples
(Trained/Total)
Samples correctly
recognized/Total
to be recognized
Discrimination
rate (%)
Air 25/99 68/74 92
Oil 41/142 99/101 98
Table 4 Recognition result of the second tier (type)
Type Number of samples
(Trained/Total)
Samples correctly
recognized/Total
to be recognized
Discrimination
rate (%)
Air 1 8/33 24/25 96
Air 2 9/33 24/24 100
Air 3 9/33 24/24 100
Oil 1 6/20 14/14 100
Oil 2 9/31 22/22 100
Oil 3 9/31 22/22 100
Oil 4 8/30 22/22 100
Oil 5 9/30 21/21 100
Table 5 Recognition result of the third tier (severity level)
Type Number of samples
(Trained/Total)
Samples correctly
recognized/Total
to be recognized
Discrimination
rate (%)
Air 1 12/33 21/21 100
Air 2 12/33 21/21 100
Air 3 12/33 21/21 100
Oil 1 8/20 11/12 92
Oil 2 12/31 19/19 100
Oil 3 12/31 19/19 100
Oil 4 11/30 18/19 95
Oil 5 12/30 21/21 100
4.4 ANN module in PEMDS
The trained ANN was then embedded into the PEMDS
to identify the type of PD and its severity. The ANN is
then a data processing method which receives the data,
processes the data, and then outputs the recognition
result. In PEMDS, the ANN module was designed as a
data processing module supporting the IDataProcessor
interface. The relationship between the ANNs in the
different tiers was based on cluster concept where a
cluster is a container containing all ANNs connected to
an ANN in the previous tier. As shown in Fig. 9, the air
cluster contains the ANNs for Air 1, Air 2, and Air 3.
Before installation, the ANNs were trained with
experimental data. With the accumulation of on-site
data, the ANNs can also be trained with real data to
improve the ANN modules.
5 Conclusions
PD signals are rather weak in the field of high
frequencies such as 500 kHz-1 MHz with higher SNR,
than lower frequencies such as 0-500 kHz. A
wide-band transducer, an A/D unit of high sample rate
and appropriate DSP methods were used in a
monitoring system to extract the weak signals from the
intensive interference to more accurately describe the
PD signals.
The tabular -q-n 3-D chart data was used as the
ANN input vector for pattern recognition with the
discrimination rate of experimental tests exceeding
90%. A multiple tier ANN was used to decompose the
task and simplify the training and recognition
processes. By using the multiple tier ANN group as
mentioned in this paper, PDs in air and oil could be
effectively distinguished. The model provides a good
method of interference rejection in on-line PD
monitoring of transformer.
With the models used for power equipment, any
monitoring sub-system or data processing method
which is designed according to the PEMDS standards
can be easily and dynamically embedded in the system,
even while the PEMDS is running. This component-
based approach lets developers build and test
applications much more quickly than they could with
earlier approaches.
Acknowledgements
The authors thank Dr. Gao Wensheng and Mr. Gao Shengyou in
the Department of Electrical Engineering of Tsinghua University,
Beijing, China for their valuable advice and helpful discussions.
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