14 IEEE Electrical Insulation Magazine

F E A T U R E A R T I C L EF E A T U R E A R T I C L EF E A T U R E A R T I C L EF E A T U R E A R T I C L EF E A T U R E A R T I C L E

The results reported in this articleprovide evidence that the PDassessment procedure presented canbe very effective for on-site cablediagnosis.

TIntroduction

he acceptance of the concept of an electrical powersystem as an asset, and thus the need to manage such

an asset with cost-saving and quality criteria [1]–[3], is raisingthe issue of applying quality control and diagnostic techniquesto electrical network components to increase reliability (avail-ability) and reduce direct and indirect costs. Such techniquesconstitute the basis for an efficient asset management, as theycan eventually reduce the failure rate.

HV cable systems are expensive and delicate components ofthe energy network. Polymeric cables are an important innova-tion with respect of paper-oil cables, but their cost, in terms ofoutages and availability, can be high. Electrical energy net-works may experience cable failures because of bulk degrada-tion, particularly water treeing, or local defects, mostly in jointsand terminations.

The major and most effective tool to detect local damage,defects, and/or localized aging processes in extruded cable sys-tems is, as is well known, the measurement and analysis of par-tial discharges (PDs) [4]–[8]. However, this technique is not aswell accepted as one might expect on the basis of the previousdiscussion. Actually, just the detection of PDs is often not enoughto carry out effective diagnosis, risk assessment, and condition-based maintenance (CBM). In fact, the degree of harmfulnessassociated with PDs can be evaluated only if the kind of sourcegenerating the PDs can be identified. Large PDs occurring atthe surface of an insulation system, for example, are in generalless harmful than low amplitude PDs triggered in cavities inter-nal to the insulation. In addition, PDs are often hidden by, ormistaken for, noise and disturbances. Therefore, a misinterpre-tation of data can induce a lack or excess of maintenance. Even-tually, PDs themselves might not be the fastest cause of degra-dation, nor the property that covers diagnosis of all insulationdefects.

Summarizing, it can be speculated that, when PDs are themost appropriate diagnostic property for a certain insulationsystem, the identification of the defects causing PDs is funda-

mental for risk assessment, because only the correct attributionof PD pulses to their source(s) can allow a consistent evaluationof the harmfulness of the defects generating PDs. Therefore, mea-surement systems that provide enhanced tools for PD identifica-tion can constitute a significant step forward to optimize CBMprocedures by increasing efficiency and reducing costs [9]–[12].

This article describes the application of an innovative PDdetection and analysis approach to the diagnosis of HV poly-meric cable systems. The results of PD measurements performedoff-line on cable systems [13], [14], rated from 220 to 400 kV,just after laying or after some time in operation, are presented.PD-generating defect identification and location are discussed,also in relation to forensic observations.

PD Detection and Identification

The separation of PD pulses from different sources and of PDpulses from noise is based on a clustering technique that relies

A New Approach to Partial DischargeTesting of HV Cable SystemsKey Words: Partial discharges, cable systems, after-laying tests, diagnosis, reliability, riskassessment, quality control.

G. C. Montanari and A. CavalliniDIE-LIMAT, University of Bologna, Bologna, Italy

F. PulettiTechImp, Bologna, Italy

January/February 2006 — Vol. 22, No. 1 15

on a time frequency, T-W, transformation and on a Fuzzy Classi-fication, FC, engine applied to each recorded pulse. Two quanti-ties (the equivalent time length, T, and the equivalent band-width, W) are extracted from each detected pulse according tothe following expressions [15]–[17]

(1)

(2)

(3)

(4)

where s, s~ , and S~ are the pulse (length = T), the normalized pulse

(Euclidean norm = 1), and the Fourier transform of the normal-ized pulse, respectively. Quantities τ and f are integration con-stants in the time and frequency domain. From a geometricalpoint of view, t

0 is the gravity center of the pulse, whereas T and

W can be regarded as the standard deviation of the pulse in thetime and frequency domains.

Once projected in the so-called T-W plane, pulses comingfrom PDs or noise, as well as pulses belonging to different PDsource types, might form different clusters corresponding to pulsescharacterized by similar waveforms. Therefore, different clustersend up representing groups of pulses having a common source[see Fig. 1, where two types of pulses, one with high frequencycontent and short duration time (fast pulse) and the other withlow frequency content and long duration (slow pulse) are de-picted (different combinations are possible)]. The FC is appliedto this mapping tool to achieve separation of signals into classesthat are homogeneous in terms of PD pulse features [17]. Eventu-ally, typical representations, such as PD height-phase patterns,can be built up for the classes so that the acquired PD pattern canbe divided in subpatterns showing pulse height and phase com-ing from a single-source type and/or location (Fig. 2). It is note-worthy that pulses belonging to noise will differ, in general, frompulses generated by PD sources. With regard to cables, it hasbeen observed that pulses coming from different locations be-long to different clusters in the T-W map. In fact, even in caseswhere they are generated by the same type of defect, they facedifferent transfer functions between the source and the sensor[18]. In particular, pulses generated by different sources located

along a cable route tend to lose frequency content (lower Wvalues, larger T values) when they have to travel from longer andlonger distances to reach the measurement point [18]. The T-Wmap can constitute, therefore, a fundamental tool for noise rejec-tion, PD activity separation, and even defect localization in cablesystems, as discussed later.

Once different phenomena have been separated, a proper iden-tification, to single out the type of the defect generating PD, canbe achieved for each data subcluster. Identification is based onfuzzy logic algorithms [19]–[21] applied to statistical markersextracted from the distribution of the pulse quantities (e.g., height,phase, and repetition rate) of each separated class. In such a way,identification becomes highly effective, because it is applied tohomogeneous data sets. Noise can be recognized through statis-tical algorithms (for example based on correlation techniques)and then removed [19].

Identification of PD source for each subclass can rely on amulti-level procedure [20]. The first level provides a broad rec-ognition stage based on three fundamental categories, internal,surface, and corona, which are defined as follows.· Internal: PDs occurring in air gaps surrounded by solid di-

electric or solid dielectric and metallic electrodes involvingsignificant components of electric field orthogonal to elec-trode surfaces.

· Surface: discharges that develop on surfaces of solid insulat-ing materials, including solid-solid and solid-liquid materialinterfaces, which involve significant field components tan-gential to the surface.

· Corona: discharges produced in open air (gas) originatingfrom a metallic object.These categories are general enough to achieve identifica-

tion with good likelihood and success rate in most cases, but

Figure 1. Example of feature extraction of two different pulses(“slow” and “fast”). The differences, in terms of pulse shapes,result in different positioning of the pulses in the equivalenttime-length/equivalent bandwidth plane.

16 IEEE Electrical Insulation Magazine

they are also an initial aid for risk assessment. In fact, it is gener-ally true that internal discharges are more harmful than surfacedischarges, and both are more harmful than corona discharges.

According to the fuzzy nature of the inference system, a pat-tern may belong to all three categories, although with differentdegrees of likelihood, ranging from 0 to 100%. This means thata certain pattern can be traced back to a defect whose naturemight be considered intermediate among the three different cat-egories. Moreover, two further categories, noise and invalid data,have been devised to identify and filter out possible noise, tak-ing into account that some acquired data set may be inconsistentor not related to PDs [19].

The second identification level deals mainly with defect po-sition within the insulation, that is, the degree of closeness to HVor ground electrodes, and contains a routine that is able to detectthe presence of an electrical tree, once the first level has recog-nized the defect as internal [21], [22].

The third level is finally able to provide specific indicationson the nature of the defect generating PDs for a given family ofapparatuses, such as rotating machines, transformers, cables. Anexample of a third level application in rotating machines is shown,e.g., in [23]. For cables, the main output categories are internalinterface, external surface, and internal cavity.

Field Measurements and Data Interpretation

This section presents and discusses the results of on-site testsperformed off-line on three HV cross-linked polyethylene (XLPE)cable systems, using the PD measurement approach and instru-mentation just described.

A. On-Site Testing Experience on 220-kV XLPECable System

An off-line commissioning test was performed on a 5.5-kmXLPE, 220-kV buried cable circuit. The aim of the test was tocarry out a quality control of the installation work focusing onthe 14 sectionalized joints. The cable system (cable togetherwith sectionalized joints and terminations) was tested one phaseat a time using an HVAC variable frequency resonant test set(VFRTS) at 1.1 U

0 (phase to ground voltage = 146 kV) at 37 Hz

by means of a sequential procedure, i.e., energizing each phase ntimes (where n is the total number of accessories) and carryingout PD measurements for a specific time at each accessory. ThePD and synchronization signals were derived from inductivesensors installed in the link boxes (bandwidth of the sensor anddetector up to 40 MHz). All of the link boxes were suitably oper-ated to allow the test on a single phase to be carried out and tooptimize the detection of the HF signals flowing through thecable sheath.

PD measurements at each joint provided complex patterns,typically showing two different kinds of noise (correlated anduncorrelated) and PDs deriving from the connections betweenthe HV generator and the cable bushing. As a whole, the cablesystem proved to be PD free with the exception of a single joint(#5), where PD activity was detected below the noise level. Theresults relevant to this joint are shown in Fig. 3, 4, and 5. Fig. 3provides the whole PD pattern detected at joint #5, and Fig. 4shows the relevant classification (T-W) map. The subpatterns rel-evant to the four separated clusters (A, B, C, and D) together withexamples of characteristic pulses are reported in Fig. 5.

Figure 2. PD data processing. Example of two superimposed phenomena consisting of PD activity and noise. Feature extractionand separation (T-W map), pulse classification (Class.), and identification are carried out sequentially.

January/February 2006 — Vol. 22, No. 1 17

Clearly, the pattern reported in Fig. 3 is the result of the over-lapping of different activities. Specifically, Cluster A is relevantto HF background noise (uncorrelated with test voltage) becauseof the distribution and transmission cables installed in closeproximity to the cable system under test. Clusters B is due tonoise caused by electronic component switches in the resonanttest set (correlated with test voltage). Cluster C is associated withPD occurring along the connections between the HV bushingand the resonant test set (in fact, the equivalent frequency con-tent is quite low, see Fig. 4). It is worthwhile mentioning that the

phenomena corresponding to Clusters A, B, and C were detectedfrom all of the measurement points. In particular, the activitiesmarked as B and C were subjected to signal attenuation anddistortion the further the detection point was located from thepower source (in the case of detection from joint #5, the signalshad traveled almost 3 km); Cluster A was detectable in everylocation, almost unchanged. On the contrary, Cluster D was de-tected only at the link box connected with joint #5 and wascharacterized by PD pulses with a large equivalent bandwidth(thus likely located close to the measurement point). Therefore,the source of this PD contribution was attributed to a defectinside joint #5. The output of the fuzzy identification tool ap-plied to the pattern of Fig. 5(d1) is reported in Fig. 6.

As can be seen, the fuzzy algorithm provides a mixed re-sponse of “internal” and “surface” with a likelihood of 77 and23% respectively, but the tree alert, based on an algorithm aimedat recognizing the evolution to an electrical treeing from inter-nal discharges, did not provide any warning. This identificationis compatible with a discharge at the interface between a jointand the cable insulation. This kind of discharge can occur wherethere is an incorrect joint assembly. The quite low intensity (about15 mV, which corresponds to roughly 5 pC according to thecalibration carried out on the joint alone in the factory) and thelimited surface component of the identification response wouldsuggest that the discharge area be limited, thus the risk associ-ated with such PD phenomenon is not very high. It is interestingto observe that the PD pattern relevant to the activity originatedwithin the joint fully overlaps with the pattern associated withthe activity generated by the HV connections [compare Fig. 5(d1)and 5(c1)]. In other words, this activity would have not beendetected and identified properly if there was no separation toolbased on pulse shape features. In fact, the overall phase-resolvedPD pattern of joint bay #5 (Fig. 3) is qualitatively identical tothose acquired in the other measurement locations (see, for ex-ample, the pattern acquired from joint bay #6 shown in Fig. 7). Itis also important to note the low amplitude of the detected phe-nomena (5 pC), confirming the adequate sensitivity of the detec-tion system.

The corrective action following the discovery of such PD wasto replace the joint. After joint replacement, PD measurementswere carried out again on the newly installed joint, but there wasno indication of phenomena similar to that shown in Cluster D ofFig. 4 and to the pattern of Fig 5(d1); disturbances from thegenerator and noise were still present (Clusters A, B and C of Fig.4). The defective joint was analyzed in the laboratory, and foren-sic observations showed evidence of incorrect assembly, withthe edge of the HV cable electrode just outside the shielded zone(Fig. 8). This finding agrees with the indication of interfacedischarges provided by the identification system.

B. On-Site Testing Experience on 400-kV XLPECable System

An off-line commissioning test was carried out on a 12-kmXLPE, 400-kV cable circuit (tunnel installation). The systemwas composed of 17 cable spans, 16 joints, and two outdoorterminations and was tested at 260 kV (1.1 U

0), 33 Hz using a

Figure 3. Entire PD pattern acquisition relevant to themeasurements carried out on Joint #5 (220-kV XLPE cabletesting).

Figure 4. Classification map (T-W) relevant to the patternreported in Fig. 3. Four clusters can be separated: A, B, C andD (220-kV XLPE cable testing).

18 IEEE Electrical Insulation Magazine

Figure 5. Subpatterns and corresponding pulse waveforms relevant to the PD phenomena separated as a result of the clusterclassification operated in the map of Fig. 4. Patterns a1, b1, c1, and d1 correspond to Cluster A, B, C, and D, respectively (220-kV XLPE cable testing).

January/February 2006 — Vol. 22, No. 1 19

VFRTS and acquiring PD signals from a CT installed in the linkboxes. As in the previous case, in each measurement location,several phenomena were acquired; therefore, a careful analysiswas needed to avoid mistaking PD activity for external distur-bances and vice versa. The measurement results were qualita-tively similar at all measurement points. As an example, the re-sults obtained in correspondence of Joint #I are shown in Fig. 9.

As can be observed in Fig. 9, the overall pattern can be sepa-rated into several subpatterns according to the T-W classificationmap shown in Fig. 9b. Out of these five activities, four, corre-sponding to Clusters A, B, C, and D, can be attributed to distur-bances external to the cable system under test:· Phenomenon A: PD activity located on the HV connection

between the HVAC resonant test set and the cable under test atthe substation where the test set was installed

· Phenomenon B: Corona activity likely caused by the coronalocated in the same substation

· Phenomenon C: Disturbance caused by the electronic switch-ing devices of the HVAC resonant test set

· Phenomenon D: HF noise caused by external sources (noisenot correlated with the test voltage).Phenomena A, B, and C, which originate close to the termina-

tion fed by the resonant test set (T2), show both amplitude andfrequency content attenuation along the cable route (see Fig.10); phenomenon D was detectable in each location, almost un-changed.

Furthermore, in a few joints, including #I, additional signifi-cant PDs were detected and were attributed to PD activity lo-cated inside a defective joint. This corresponds to Cluster E inFig. 9b. The automatic identification output provided by thefuzzy tool is shown in Fig. 11. As can be seen, the identificationis 100% internal discharges and the treeing alert activated [19],[21], [22].

This phenomenon was detected in more than one joint; there-fore, an analysis of the amplitude and frequency of the signalsrecorded from different measurement points was necessary forlocalization purposes. At one joint bay (Joint #H), the frequencyand amplitude of the PDs were a maximum (see the bar plot ofFig. 12), indicating Joint #H as the most probable origin of thedefect detected along the cable route.

The fact that the identification response indicated a 100%likelihood of “internal” discharges, that the magnitude wasconsiderable, and that the “tree alert” was activated, stronglywarned that Joint #H should be replaced. Forensic observationson the replaced joint revealed the presence of a cavity close tothe inner semicon layer, showing a clear branched electrical treestarting from a mechanical break in the semicon. A photographof the defect, taken just after dismantling the joint, is shown inFig. 13.

C. On-Site Testing Experience on 220-kV CableSystem

The third example is of an on-site test carried out off-line onan aged 4-km long buried 220-kV XLPE cable. Several failures

Figure 6. PD source identification relevant to the pattern ofFig. 5(d1) (Phenomenon D of Fig. 4). As can be seen, the fuzzyalgorithm provides a mixed classification internal and surface(with likelihoods of 77 and 23%, respectively). The tree alertdid not activate.

Figure 7. Entire PD pattern acquisition relevant to measurements carried out at Joint Bay #6 (same cable system as in Fig. 3). Itmust be noted that the pattern appears very similar to that reported in Fig. 3, where also PDs from a joint are present, but theclassification map evidenced only three clusters corresponding to those already mentioned (A, B, and C) in the map shown inFig. 4.

20 IEEE Electrical Insulation Magazine

Figure 8. Representation of the defect generating PDs within Joint #5.

Figure 9. Entire pattern acquisition (a) obtained in defective Joint #I, together with its classification map (T-W; b). Thesubpatterns relevant to the separated Clusters A, B, C, D, and E are also given.

January/February 2006 — Vol. 22, No. 1 21

had occurred on the cable system, and after some maintenanceactions, the utility decided to investigate the PD behavior of thecable link. Therefore, a sequential PD test was carried out on alleight joints at 1.1 U

0 (146 kV), 36 Hz using a VFRTS. As in the

previous cases, the measurements were taken from a CT installedin the link boxes.

While increasing the voltage on one of the three phases, PDinception occurred well below the rated voltage (90 kV), and onreaching 100 kV, breakdown occurred. The information acquiredjust before the breakdown of the failed joint is shown in Fig. 14and 15.

As can be observed, internal PD activity was detected beforebreakdown. Two aspects must be carefully evaluated. First, thetreeing alert tool had provided a serious warning, in particularthe level of the warnings increased when the voltage was raisedfrom 90 to 100 kV. Second, the amplitude of the detected dis-charges is quite low (up to 20 mV). This suggests that internaldischarge activity (particularly in the presence of a treeing alert)might be associated with significantly high risk assessment, evenif the PD amplitudes are quite low [4]. The failed joint was re-

Figure 10. Bar chart relevant to the maximum amplitudes andaverage frequency content of PD pulses in Cluster A of Fig. 9,as detected along the whole cable route.

Figure 11. Automatic identification output for Subpattern E ofFig. 8. As can be seen, identified are internal discharges witha likelihood of 100%. The treeing alert activated (warningwith Likelihood 1).

Figure 12. Bar chart showing the maximum amplitudes andaverage frequency content of PD pulses in Subpattern E ofFig. 8, which are detected in various joint bays of the HVcable of Fig. 9 and 10. Measurements on Joint bays #G, #L,and #M (nonsectionalized straight joints) had lowersensitivity than the other (sectionalized) joints, which explainsthe missed observation of PDs relevant to Subpattern E.

Figure 13. Photograph of the defect generating PDs withinJoint #H. The burning caused by electrical treeing activitycan be easily recognized.

22 IEEE Electrical Insulation Magazine

placed and the test repeated, proving that, after the maintenanceaction, the entire cable link was PD free.

Conclusions

The reported results provide evidence that the PD assessmentprocedure discussed in this article can be very effective for on-site cable diagnosis. Actually, the method of separation based onthe deterioration of the pulse frequency and time characteristicsallows enhanced noise rejection and PD separation to beachieved; artificial intelligence methods applied to homoge-neous PD data sets prove to be very effective for PD source iden-tification.

Thus, the combination of “separation and identification” leadsto improved reliability of PD evaluation and, therefore, providesinformation that can be exploited to enhance maintenance pro-cedure plans. This is fundamental in reducing operational costsin a utility cable network. Moreover, not only did the time-fre-quency analysis prove to be effective for the separation of differ-ent sources of PDs, but also for PD location. Finally, the treeingalert recognition tool seems very promising for risk assessment.

As a result, the investigations carried out not only contributed toan improved cable system reliability and availability, but alsotranslated into considerable savings. Several of the diagnosedproblems would likely have evolved into breakdowns duringoperation.

References

[1] G.J. Anders, J. Endrenyi, and C. Yung, “Risk-based planner forasset management,” IEEE Comput. Appl. Power, vol. 14, no. 4, pp.20–26, Oct. 2001.

[2] Generic Guidelines for Condition Assessment OH HV Assets andRelated Knowledge Rules, CIGRE WG D1.17, 2nd draft, 2005.

[3] W. Sweet, “Restructuring the thin-stretched US electricity supplyindustry,” IEEE Spectrum, vol. 37, no. 6, pp. 43–39 Jun. 2000.

[4] Partial Discharge Detection in Installed HV Extruded Cable Sys-tems, CIGRE Tech. Brochure 182, CIGRE WG 21.16, Apr. 2001.

[5] J. Densley, “Aging mechanisms and diagnostics for power cables—An overview,” IEEE Elect. Insul. Mag., vol. 17, no. 1, pp. 14–22,Jan. 2001.

[6] G. Katsuta, A. Toya, K. Muraoka, T. Endoh, Y. Sekii, and C. Ikeda,”Development of a method of partial discharge detection in extra-

Figure 14. PD pattern and identification response acquired for the failed joint at 90 kV. The yellow treeing alert activated, seecircle (Identification: Internal discharge with Likelihood 1).

Figure 15. PD pattern and identification response acquired for the failed joint at 100 kV. The red treeing alert activated (themost severe one; see circle).

January/February 2006 — Vol. 22, No. 1 23

high voltage cross-linked polyethilene insulated cable lines,” IEEETrans. Power Delivery, vol. 7, no. 3, pp. 1068–1074, 1992.

[7] M.S. Mashikian, F. Palmieri, R. Bansal, and R.B. Northrop, “Loca-tion of partial discharges in shielded cables in the presence of highnoise,” IEEE Trans. Elect. Insul., vol. 27, no. 1, pp. 37–43, Feb.1992.

[8] T. Heizmann, T. Aschwanden, H. Hahn, M. Laurent, and L. Ritter,“On-site partial discharge measurements on premoulded cross-bond-ing joints of 170 kV XLPE and EPR cables,” IEEE Trans. PowerDelivery, vol. 13, no. 2, pp. 330–335, Apr. 1998.

[9] E. Gulski, P.H.F. Morshuis, and J. Janssen, “Recognition of defectsin high voltage cables using statistical tools,” in Proc. IEEE ICSD,Sestri Levante, Italy, Jun. 1992, pp. 129–144.

[10] S. Boggs and J. Densley, “Fundamentals of partial discharge in thecontext of field cable testing,” IEEE Elect. Insul. Mag., vol. 16, no. 5,pp. 13–18, Oct. 2000.

[11] N.H. Ahmed and N.N. Srinivas, “Partial discharge severity assess-ment in cable system,” IEEE/PES Trans. Distrib. Conf. and Expo.,Atlanta, GA, Oct. 2001, pp. 849–852.

[12] H. Suzuki and T. Endoh, “Pattern recognition of partial discharge inXLPE cables using a neural network,” IEEE Trans. Dielect. Elect.Insul., vol. 27, no. 3, pp. 543–549, Jun. 1992.

[13] M. Albertini, A. Cavallini, G.C. Montanari, F. Ombello, and F. Puletti,“Improved diagnostic tools for cable accessories by digital PD detec-tion systems,” Conf. on High Voltage Plant Life Extension, Linkebeek,Belgium, Nov. 2000. pp. 2.2.1–2.2.9.

[14] A. Cavallini, G.C. Montanari, F. Puletti, S. Franchi Bononi, F.Ombello, and J. Butt, “Experience of testing polymeric HV cablesystems by an innovative partial discharge measurement approach,”IEEE POWERCON, Singapore, Nov. 2004, pp. 1–5.

[15] L.E. Franks, Signal Theory, Prentice-Hall, 1975.[16] A. Cavallini, G.C. Montanari, A. Contin, and F. Puletti, “A new

approach to the diagnosis of solid insulation systems based on PDsignal inference,” IEEE Elect. Insul. Mag., vol. 19, no. 2, pp. 23–30,Apr. 2003.

[17] A. Contin, A. Cavallini, G.C. Montanari, G. Pasini, and F. Puletti,“Digital detection and fuzzy classification of partial discharge sig-nals,” IEEE Trans. Dielect. Elect. Insul., vol. 9, no. 3, pp. 335–348,Jun. 2002.

[18] S. Boggs, A. Pathak, and P. Walker, “Partial discharge XXII: Highfrequency attenuation in shielded solid dielectric power cable andimplication thereof for PD location,” IEEE Elect. Insul. Mag., vol.12, no. 1, pp. 9–16, Jan./Feb. 1996.

[19] A. Cavallini, A. Contin, G.C. Montanari, and F. Puletti, “AdvancedPD inference in on-field measurements. Part I. Noise rejection,”IEEE Trans. Dielect. Elect. Insul., vol. 10, no. 2, pp. 216–224, Apr.2003.

[20] A. Cavallini, M. Conti, A. Contin, and G.C. Montanari, “AdvancedPD inference in on-field measurements. Part.2: Identification of de-fects in solid insulation systems,” IEEE Trans. Dielect. Elect. Insul.,vol.10, no. 3, pp. 528–538, Jun. 2003.

[21] A. Cavallini, M. Conti, G.C. Montanari, C. Arlotti, and A. Contin,“PD inference for the early detection of electrical tree in insulationsystems,” IEEE Trans. Dielect. Elect. Insul., vol. 11, no. 4, pp. 724–735, Aug. 2004.

[22] A. Cavallini, G.C. Montanari, and F. Puletti, “A fuzzy logic algo-rithm to detect electrical trees in polymeric insulation systems,” ac-cepted for publication in IEEE Trans. Dielect. Elect. Insul., vol. 12,Apr. 2005.

[23] A. Cavallini, M. Conti, A. Contin, G.C. Montanari, and F. Puletti, “Anew algorithm for the identification of defects generating partial dis-charges in rotating machines,” in Proc. IEEE ISEI, Indianapolis, IN,Sep. 2004, pp. 204–207.

Gian Carlo Montanari (M’87-SM’90-F’00) was born on November 8, 1955. Hereceived the Masters degree in electricalengineering at the University of Bologna.He is currently a full Professor of ElectricalTechnology at the Department of Electri-cal Engineering of the University of Bolo-gna and teaches courses on technology andreliability. He has worked since 1979 in thefield of aging and endurance of solid insu-lating materials and systems, diagnostics

of electrical systems, and innovative electrical materials (mag-netics, electrets, superconductors). He has also been engaged inthe fields of power quality and energy market, power electronics,reliability, and statistics of electrical systems. He is IEEE Fellowand a member of AEI and the Institute of Physics. Since 1996, hehas been President of the Italian Chapter of the IEEE DEIS. He isconvener of the DEIS Technical Committee on Statistics and amember of the Technical Committees on Space Charge and Mul-tifactor Stress Aging. He is an Associate Editor of IEEE Transac-tions on Dielectrics and Electrical Insulation. He is founder andPresident of TechImp, established on 1999. He is author or coau-thor of about 450 scientific papers.

Andrea Cavallini (M’1995) receivedfrom the University of Bologna a Master’sdegree in electrical engineering in 1990 anda Ph.D. degree in electrical engineering in1995. He was researcher at Ferrara Univer-sity from 1995 to 1998. Since 1998, he hasbeen an associate professor at Bologna Uni-versity. His research interests are diagnosisof insulation systems by partial dischargeanalysis, reliability of electrical systems,

and artificial intelligence. Since 2004, he has been the Italianrepresentative of CIGRE SC D1.

Francesco Puletti was born in Città diCastello (PG) on December 8, 1974. Hegraduated with a degree in electrical engi-neering on March 19, 1999. He has carriedout research activity on the topic of insu-lating system diagnosis by means of inno-vative partial discharge measurement tech-niques in the Laboratory of Material Engi-neering and High Voltage of the Universityof Bologna. He is now CEO of TechImp, a

spin-off of Bologna University, which is involved in the field ofdiagnostics of electrical systems.