PD Analysis in Monitoring

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Index Terms--Asset management, Monitoring, On-line tests,

Partial discharges, Reliability, Risk assessment.

I. I NTRODUCTION

WO fundamental drivers must be taken into account when

dealing with electrical asset management: the need of costreduction and that of availability (and reliability)

maximization. Actually, asset management would greatly

benefit, from both an economical and a technical point of

view, as well as from the opportunity to plan maintenance

actions according to the real state of the asset. Under these

premises, Condition Based Maintenance (CBM) practices

seem to be the most promising and performing tool to support

asset management decisions. However, in order to carry out

proper CBM actions, effective diagnostic techniques aimed at

providing reliable information for the assessment of ageing

and performance (conditions) of the electrical asset must be

employed.

As a matter of fact, failure risk estimation for a certainelectrical apparatus is, in general, a complex task. For

example polymeric insulation cable systems have been

observed to experience sudden failures even if their overall

A. Cavallini is affiliated with the Department of Electrical Engineering,

University of Bologna, Italy (e-mail: [email protected]).

G. C. Montanari is affiliated with the Department of Electrical

Engineering, University of Bologna, Italy (e-mail:

[email protected]).

F. Puletti is affiliated with TechImp Systems, Bologna, Italy

([email protected])

condition had been considered satisfactory (i.e. weak bulk

ageing or no ageing at all). This is mainly to be traced back to

the presence of weak points (defects) able to trigger localized

degradation phenomena. Therefore, the possibility to verify

the presence of localized defects is fundamental for electrical

insulation systems availability and reliability improvement.

Partial Discharge (PD) testing and analysis have proven to be

the most effective way to evidence the presence of local

degradation mechanisms. Nowadays, PD testing issuccessfully employed (a) during quality control of electrical

systems to check for manufacturing problems, (b) during

commissioning tests to verify installation quality, (c) during

equipment life as a condition based maintenance tool to derive

information about the state of an electrical asset. In this paper

an innovative and improved approach to partial discharge

detection and analysis is presented, and an example of its

application to the continuous monitoring of an electrical asset,

i.e., a high voltage cable system is reported and commented.

II. PD I NVESTIGATION APPROACHES: PROS AND CONS

PD measurements have historically been employed in thelaboratory for quality control of electrical apparatus, with the

aim of highlighting the presence of defects due to

manufacturing processes. Lately, thanks to improved detection

and analysis capabilities of PD diagnostic instruments and

procedures, PD have started to be widely applied on site, with

the purpose of both detecting installation problems

(commissioning tests) and assessing the condition of electrical

systems by inferring the presence of on-going local

degradation mechanisms (diagnostic tests). As far as on site

PD investigation is concerned, an evaluation must be carried

out about the possible different testing approaches in order to

highlight their effectiveness and applicability as well as their

cost/performance ratio.

As regards on-site PD investigations, these can be carried

out by on line or off line procedures. Off line tests are carried

out by disconnecting the asset from the grid and energizing it

through a mobile test set. This offers several advantages but

has also some drawbacks. If the capacitance of the asset is

large (typically, large cable systems, generators), it is very

difficult to carry out tests at industrial frequency. Therefore,

alternative energization methods have been introduced, such

Partial Discharge Analysis and Asset

Management: Experiences on Monitoring of

Power ApparatusA. Cavallini, Member, IEEE , G.C. Montanari, Fellow, IEEE , and F. Puletti

T

1-4244-0288-3/06/$20.00 ©2006 IEEE

2006 IEEE PES Transmission and Distribution Conference and Exposition Latin America, Venezuela



as Damped AC Voltages (DAS), variable frequency resonant

test set, Very Low Frequency (VLF) generators [1]. Testing

with frequencies that differ too much from the industrial

frequency may however provide results that do not reproduce

what happens in real conditions, since the physics of PD is

strongly influenced by supply frequency [2]. In addition, off

line test circuits may provide inherently an electric field

profile inside the insulation system that is remarkably

different from that experienced in service. For instance,supplying a generator winding with open star connections

during an off line tests creates an electrical field distribution

which is very different from the stress present during machine

operation. Moreover, the overall condition of the system

(temperature and humidity profiles, vibrations, clearances,

etc.) during off line tests may be very different from those

experienced in service conditions. As a consequence, there is

a risk that off line testing activates phenomena that do not

occur during service (e.g., PD from cavities near the star point

in rotating machines). On the contrary, PD sources that are

active during service may not be activated during off line

tests.On the other hand, off line investigation proves usually to

be very effective as far as measurement sensitivity is

concerned. In particular, by stressing and testing one phase a

time, cross talk phenomena can be eliminated. By using test

ring electrodes and other suitable devices, corona and

interference from the grid can be minimized. Moreover,

trough off line testing procedures is it possible to measure

inception and extinction voltage of PD phenomena and

evaluate the PD behaviour at various voltage levels, thus

improving defect characterization. Therefore, it can be

speculated that off line tests pose less difficulties for noise

rejection and PD phenomena identification.

On line tests are characterized by advantages anddisadvantages that are, in some sense, complementary to those

relevant to offline testing. In particular, tests are carried out at

operating conditions; therefore, all and solely the criticalities

that may affect the asset during operations are detected and

analyzed. However, noise and pulsed interference from

neighbouring HV apparatus may affect PD readings,

complicating interpretation and diagnosis. In addition, PD

inception and extinction voltages cannot be evaluated.

Both kinds of investigations are nevertheless used and can

claim several successful applications. But while off line tests

are considered a necessary and ideal practice for the

commissioning of HV cable systems, the market is generallylooking at on line applications with growing interest,

particularly for periodic diagnostic investigations during

equipment life. In fact, off line testing are more expensive

than on line ones and force an outage of the asset. Actually,

diagnosis by on line measurements has already become a

common practice for rotating machine [3].

Another issue to be considered is the fact that insulation

systems are subjected to changes during time under stress. For

instance, thermal cycling and vibration during operation often

induce interface delaminations. Chemical contaminants,

nuclear radiation or lightning overvoltages may also initiate

defects. Particularly for organic insulation systems, stress-

induced defects may disrupt insulation system integrity in a

relatively short time. Hence, it is important to evaluate how

frequently PD assessments should be carried out in order to

minimize failure risk. In general, the more frequent the

assessment the more the chances to detect a failure mechanism

at an early stage. Thus, when dealing with a fundamental asset

of the grid, as well as when a failure would have unacceptableconsequences in terms of unavailability and costs, the option

of a continuous assessment of PD might be considered. In

other words, a correct and serious approach towards asset

management may call for the use of permanently installed

monitoring systems on those assets for which failure risk must

be minimized.

However, monitoring of electrical assets poses other

concerns. Costs are sometimes deemed to be relatively high,

particularly for cable systems, while efficient solutions are

needed to automatically reject noise and handle the large

amount of data generated by monitoring systems. In

particular, proper automatic identification devices must beused, able to provide suitable alerts in case internal PD

activities are detected. Finally, an ideal solution would also

include a combined evaluation of PD and other quantities

directly or indirectly related to aging and degradation of

electrical systems as, e.g., vibration patterns for rotating

machines, Dissolved Gas Analysis (DGA) for transformers

and core temperature for cables.

The above issues are addressed by the monitoring system

presented in this paper, which takes advantage of an

innovative detection and analysis approach which enables to

successfully reject noise and distinguish among different kind

of PD. These features enable to follow the evolution of those

activities which may pose a threat on the electrical system.

III. PDDETECTION AND A NALYSIS

For several reasons, interpreting PD phenomena in power

apparatus can become an hard task. On one side, pulses due to

noise and/or multiple PD phenomena can be recorded,

providing a pattern that is the superposition of several

contributions. On the other side, PD phenomena are

sometimes complex to identify since several parameters that

affect PD (e.g., the ratio between applied and inception

voltage, cavity size, humidity, etc.) are unknown. To

overcome these difficulties, the diagnostic system proposed in

this paper is realized through (a) an innovative signal

processing hardware aimed at pulse separation, capable of

separating PD from noise, disturbance and PD due to different

sources, (b) fuzzy logic artificial intelligence (FLAI) routines

that provide an information about the nature of PD sources. In

the following, a brief description of the system will be

provided, while a thorough description can be found in [4-7]

Signal processing approach: separation

The pulses arriving at a PD detector input are normally

characterized by a large variance in waveforms. Indeed, noise



pulses are, generally, completely different from PD pulses.

Moreover, due to attenuation and dispersion phenomena, PD

pulses coming from different sources can be remarkably

different. To exploit this information, normally lost in

conventional detectors, an innovative ultra bandwidth

hardware was developed. The hardware is capable to acquire

PD pulses with a bandwidth of 40 MHz, and sampling them at

a rate of 100 MS/s. Pulse acquisition is based on trigger

conditions: as soon as the signal at the detector input exceedsa predefined level, acquisition is started. Acquisition is

stopped after a predefined time.

T

F

T

F

Fig. 1. Example of projection of detected pulses into the T-F map (one “fast”

pulse with high frequency content and one “slow” pulse with small frequency

content).

After completing an acquisition, while the input stage

resets for a new acquisition, a digital signal processor (DSP)

unit evaluates the features of the most recently-recorded pulse

and stores it for further processing. In particular, the DSP unit

evaluates, for each acquired pulse, polarity, peak value, and

equivalent timelength, T , and bandwidth, W . These two latter

quantities provide a synthetic description of the pulse

waveform in the time and frequency domain, and can be used

to separate pulses having different shapes [4]. As an example,

Fig. 1 shows how “fast” and “slow” pulses are characterized by different T and W values.

Using this signal processing scheme, a large amount of PD

pulses can be recorded and processed, thus allowing for

denoising and for separation of PD coming from different

sources. In particular, TW values for each pulse are plotted on

a Cartesian plane (TW map). Groups of pulses having

different shapes can be separated by recognizing different

clusters in the TW map. Clustering routines can provide an

unsupervised classification of pulses, thus performing

separation automatically. An example of pulse separation is

shown in Fig. 2: after acquisition, it is possible to recognize

that there exist two cluster of pulses characterized by differentTW values. If separation according to TW values is performed,

noise can be removed from the pattern, focusing on PD pulses

only. In a similar way, pulses due to different PD sources can

be separated, allowing to obtain several sub-patterns [6].

Fuzzy logic artificial intelligence routines: identification

Separation is of paramount importance for PD source

recognition since it enables to focus on the pattern created by

a single PD source at a time. However, PD identification still

remains a complex task since incomplete information is

generally available on several parameters that directly affect

PD activity. As an example, PD activity is very sensitive to

the overvoltage ratio (i.e., the ratio between applied and

inception voltage), which is generally unknown during online

tests. In order to cope with these limitations, the identification

system was designed using fuzzy logic techniques. Fuzzy

logic can deal with inherently ambiguous information by

assigning, with different likelihoods (or membership values),

a PD source to several categories simultaneously [7].

a) Recorded PD pattern

c) T-W map

b1)

b2)

Cluster A

Cluster B

Pulse type B

Pulse type A

Sub-Pattern B

Sub-Pattern A

d1)

d2)

Fig. 2. Example of a typical PD pattern (containing peak, phase and number ofPD), detected on a cable system, where noise and PD pulses are overlapped

(a), relevant pulses (b1 and b2, for PD pulse and noise, respectively), clusters

in the T-W map (c: B and A group PD and noise, respectively), and sub-

patterns obtained through separation of pulses in the T-W map (d1 and d2)

achieved by means of a fuzzy classification algorithm.

The FLAI routines used in the identification system are

designed according to a three-stage tree-like structure. At the

1st level, identification routines assign the defect to three PD

source macrocategories (internal, surface, and corona), to

noise or invalid data (e.g., PD not acquired correctly). This

identification is very reliable and provides, in most cases, the

fundamental information to carry out (if necessary)

maintenance operations. By proceeding to further levels, morespecified (but, sometimes, less reliable) information can be

found. Thus, the 2nd identification level provides additional

information about internal defects, i.e., defect position with

respect to electrodes, presence of electrical treeing [8]. The

information about treeing activity is particularly important to

carry out maintenance/replacement operations in organic

insulations as, e.g., polymeric cable systems, since after

treeing initiation the insulation system residual life will be

very short. Eventually, the 3rd level identification routines

assign the PD activity to macrocategories that are specific to a



given type of apparatus. As an example, for rotating

machines, the following defects are recognized by the

identification system: PD in microvoids or delaminations, slot

PD, stress grading PD, bar-to-bar or bar-to-ground PD [9]

IV. MONITORING OF A POWER CABLE SYSTEM

In the following, an example of installation and operation

of a monitoring system on a HV cable link is presented. A

permanent monitoring system was installed in order to assess

PD activity and its possible time evolution in a 400 kV cable

system including two terminations (one outdoor and one

indoor) and two joints. PD signals were detected from the

terminations by means of High Frequency Current

Transformers (HFCT) installed around the grounding leads of

the two terminations and from the joints through internal

capacitive taps. The monitoring system consisted of:

1. a PD detector working according to the approach

described in Section III,

2. an industrial PC aimed at supervising the acquisition

and analysis phase, while providing connectivity tools.

Fig. 3. Example of a typical PD pattern relevant to surface/corona PD

occurring on the external surface of the outdoor termination.

Connectivity is an important issue in PD monitoring systems.

Indeed, improperly designed data streams are prone to be too

large to be transmitted and checked accurately. For the system

described here, the PC could be controlled through a

telephone line modem and send warning SMS through a GSM

modem. After an initial training phase, the system was

normally operating unsupervised, being warning SMS the

preferred way to get information in real time. If desired, e.g.,

upon receiving a warning SMS, the operator could also

connect to the PC in order to download historical data, check

the obtained results and, if needed, change acquisition settings

and warning strategies to improve detection and processing

effectiveness.

As regards test results, during the monitoring period

surface/corona phenomena occurring on the outdoor

termination were detected. An example of the pattern relevant

to this phenomenon is reported in Fig. 3. By analyzing pattern

quantities (phase, amplitude variance, etc.) the time behaviour

of this phenomenon it can be observed that it was related to

atmospheric conditions. In fact, it incepted almost regularly

during nights and raining days.

As can be observed from Fig. 4 this activity was

characterized by pulses having an average equivalent

frequency of roughly 4.5 MHz and reached very high values

of amplitude (more than 1 V). Moreover, its phase position

was typical of discharges occurring at insulator/air interface

(i.e., centred around 90 and 270 degrees) [10]. Thus, the

system was trained to recognize this phenomenon on the basis

of criteria based mainly on PD phase and amplitude values

and provide the system manager with a “Yellow” (not severe)

alert, since this phenomenon was obviously not harmful forthe insulation system. It is noteworthy that, given the marked

dependence of this phenomenon on weather conditions,

appropriate interaction between the PD detector and a weather

station could further help in strengthening identification,

avoiding false positive detections.

Monitoring time (days)

0 1 2 3 4 5 6 7

A m p

l i t u d e ( m V )

250

300

350

400

450

500

550

F r e q u e n c y ( M H z )

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Amplitude

Frequency

PD detector full scale = 500 mV

Fig. 4. Behavior of PD pulse magnitude and frequency content for a PD

phenomenon (surface/corona) external to the cable.

Fig. 5. Example of a typical PD pattern relevant to the internal PD

phenomenon occurring in the defective joint.

Besides the surface activity described above, an additional

PD intermittent activity incepted during the monitoring period

in one of the two joints. An example of the typical pattern of

this activity at a very early stage detected from the capacitivetap of the defective joint is reported in Fig. 5.

This activity was characterized by pulses having an

average equivalent frequency of roughly 8-9 MHz and low

values of amplitude. This pattern was recognized as a defect

internal to the insulation system by the expert system of the

monitoring tool (FLAI). Therefore, it activated the warning

system that broadcasted a Red (severe) alert signal, indicating

the presence of PD in the cable system. The differences in the

frequency content of the discharge pulses relevant to the two

phenomena (internal PD in the joint and external PD in the



outdoor termination) allowed to identify two separated areas

of the classification map, so that the system could

automatically separate the two activities and process them

accordingly. An example of the simultaneous presence of the

two activities and the corresponding separation is reported in

Fig. 6.

As mentioned before, the internal PD activity was

characterized by an intermittent occurrence in the first days.

Then, it stabilized and started to increase in amplitude, until breakdown of the joint occurred. The time behaviour of

amplitude and frequency content of the pulses of the internal

phenomenon is reported in Fig. 7

It might be noted that the amplitude of the internal PD

activity in the period close to breakdown reached a significant

level, which might have warned the asset manager to take a

corrective action. However, PD magnitude levels prior

breakdown were also comparably lower than those associated

to external surface/corona phenomena (i.e., disturbance),

providing a further evidence that adequate processing tools

are needed to remove phenomena that are not harmful while

focusing on those that are a real threat to insulation systemreliability.

Complete PD patternClassification map

Cluster A Cluster B

Fig. 6. Separation of external discharges (cluster A) from internal discharges

in cable joint (cluster B) based on the characteristics of PD pulses.

Monitoring time (days)

1 2 3 4 5 6 7

A m p l i t u d e ( m V )

0

100

200

300

400

500

F r e q u e n

c y ( M H z )

6

8

10

12

14 Amplitude

Frequency

Fig. 7. Behavior of PD pulse magnitude and frequency content for a PD

phenomenon internal to the cable system.

V. CONCLUSIONS

Partial discharge monitoring of power apparatus can

provide useful information for condition-based maintenance

practices only if the monitoring system is (a) able to reject

noise and disturbance from PD measurements, (b) separate

contributions to the overall pattern due to different PD sources

and, eventually, (c) provide a reliable indication about the

nature of the PD phenomena recorded, thus enabling

intelligent warning systems to be devised. All these features

are necessary to develop a system that is able to provide the

final user with a summary of the apparatus conditions, withoutthe need to process the large amount of data usually provided

by monitoring system.

The system described in this paper provides effective

solutions to implement a monitoring scheme that is effective,

i.e., able to provide timely warning messages, and viable, thus

allowing for maintenance cost reduction and

reliability/availability maximization. At the present stage, the

system is also able to collect quantities from other sensors as,

e.g., temperature and/or vibration monitors. Further

development will focus on the apparatus-specific strategy to

exploit these additional information in order to reinforce PD-

based diagnosis of insulation conditions.

VI. R EFERENCES

[1] IEEE Guide for Partial Discharge Testing of Shielded

Power Cable Systems in a Field Environment, draft 11,

2005.

[2] A. Cavallini and G. C. Montanari, “Effect of supply

voltage frequency on testing of insulation system”, IEEE

Trans. on Dielectrics and Electrical Insulation, vol. 13, n.

1, pp. 111-121, February 2006.

[3] G. C. Stone, “Tutorial on Rotating Machine Off-line and

On-line Partial Discharge Testing”, EPRI/CIGRE

Colloquium on Maintenance of Motors and Generators,

Vol.3, Florence (Italy), April 1997[4] A. Contin, A. Cavallini, G.C. Montanari, G. Pasini, F.

Puletti, “Digital detection and fuzzy classification of

partial discharge signals”, IEEE Trans. on Dielectrics and

Electrical Insulation, vol. 9, n. 3, pp. 335-348, June 2002

[5] A. Cavallini, G. C. Montanari, A. Contin, F. Puletti, “A

new approach to the diagnosis of solid insulation systems

based on PD signal inference”, IEEE Electr. Insul. Mag.,

vol. 19, no. 2, pp. 23-30, April 2003.

[6] A. Cavallini, A.Contin, G.C. Montanari, F. Puletti,

“Advanced PD inference in on-field measurements. Part

I. Noise rejection”, IEEE Trans. on Dielectrics and

Electrical Insulation, vol. 10, no. 2, pp. 216-224, April

2003..[7] A. Cavallini, M. Conti, A. Contin, G.C. Montanari,

“Advanced PD Inference in On-Field Measurements.

Part.2: Identification of Defects in Solid insulation

Systems”, IEEE Trans. on Dielectrics and Electrical

Insulation, vol.10, no. 3, pp. 528-538, June 2003.

[8] A. Cavallini, M. Conti, G.C. Montanari, C. Arlotti, A.

Contin, “PD inference for the early detection of electrical

tree in insulation systems”, IEEE Trans. on Dielectrics

and Electrical Insulation, Vol. 11, n. 4, pp. 724-735,

August 2004

A

B



[9] J. Borghetto, A. Cavallini, A. Contin, G.C. Montanari, M.

de Nigris, R. Passaglia, G. Pasini, "Partial discharge

inference by an advanced system: analysis of online

measurements performed on hydrogenerators", IEEE

Trans. on Energy Conversion, Vol. 19, n. 2, pp. 333-339,

June 2004

[10]A. Cavallini, G. C. Montanari, S. Chandrasekar and F.

Puletti, “A Novel Approach for the Inference of Insulator

Pollution Severity”, IEEE ISEI, Toronto, Canada, June2006

VII. BIOGRAPHIES

Andrea Cavallini (M’1995) received from the University

of Bologna the master in Electrical Engineering in 1990

and the PhD degree in Electrical Engineering in 1995. He

was researcher at Ferrara University from 1995 to 1998.

Since 1998, he is associate professor at Bologna

University. His research interests are: diagnosis of

insulation systems by partial discharge analysis, reliability

of electrical systems and artificial intelligence. Since 2004,

he is the Italian representative of Cigrè SC D1.

Gian Carlo Montanari (M’87-SM’90-F’00) took the

Master degree in Electrical Engineering at the University of

Bologna. He is currently Full Professor at the University of

Bologna. He has worked since 1979 in the field of aging and

endurance of solid insulating materials and systems, of

diagnostics of electrical systems and innovative electrical

materials. He has been also engaged in the fields of power

quality and energy market, power electronics, reliability and

statistics of electrical systems. He is IEEE Fellow and

member of Institute of Physics. Since 1996 he is President

of the Italian Chapter of the IEEE DEIS. He is convener of the Statistics

Committee and member of the Space Charge, Multifactor Stress and Meetings

Committees of IEEE DEIS. He is Associate Editor of IEEE Trans. on DEI. He

is founder and President of the spin-off TechImp, established on 1999. He is

author or coauthor of about 450 scientific papers.

Francesco Puletti was born in Città di Castello (PG), on

08/12/1974. He graduated in Electrical Engineering in

19/03/99. He has carried out research activity in the topic

of insulating system diagnosis by means of innovative

partial discharge measurement techniques in the

Laboratory of Material Engineering and High Voltage of

University of University of Bologna. He is now CEO of

TechImp, spin-off of Bologna University operating in the

field of the diagnostics of electrical systems

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