A Partial Discharge-Based Health Index for Rotating Machine

A Partial Discharge-Based Health Index for Rotating Machine Condition Evaluation Gian Carlo Montanari Department of Electrical Energy and Informatics Engineering University of Bologna, Italy and Center for Electromechanics University of Texas, USA Paolo Seri Department of Electrical Energy and Informatics Engineering University of Bologna, Italy Key words: rotating machines, partial discharges, asset management, insulation conditions, health index, partial discharge source, condition based maintenance The article provides a health-index approach for rotating machines taking into account the level of harmfulness of each PD phenomenon, its time evolution, age of the machine, maintenance records and loading history, which is synthetized into a single number giving the health condition and an indication of the type, time, and advantage of a maintenance action.

Introduction It is well known that partial discharge (PD) is a threat to electrical insulation in all electrical apparatus, but we also know that PD harmfulness, and thus damage, depends on the type of defect generating the PD. As an example, having corona PD in switchgear boxes, rather than surface PD on bushings or internal PD on cable terminations, will trigger completely different actions from maintenance managers, even if the amplitude of the first (corona PD) is much larger than those of the last (internal PD). Indeed, corona PD may not need any medium-short term action, surface PD can just require some careful periodic check, while internal PD in cable joints/terminations will have to be addressed promptly. Regarding rotating machines, there are various PD typologies, as summarized in 60034-27-1, which also rank PD sources in terms of harmfulness Errore. L'origine riferimento non è stata trovata.. According to this standard, PD is classified as internal discharges, slot discharges, end winding gap and surface discharges, and foreign conductive materials discharges. In spite of this, it is still a common practice to perform PD measurements, store data in a more or less large database without making any distinction among PD sources, and retrieve data, on the basis of which insulation system condition is evaluated. For such evaluation, reference is made, in general, just on PD amplitude and sometimes on PD repetition rate [2],[3]. It is often claimed the reason for this approach is the customer wants to have a simple indication as to the required maintenance action (type and time), residual life estimation of the electrical apparatus, and does not want to deal with pattern recognition, colored phase-resolved PD (PRPAD) patterns, noise and disturbance concerns. However, in many cases this approach might not be helpful deciding and performing maintenance actions. It can be speculated that giving simple and clear messages is not in contrast with delivering correct and useful information that are associated with PD type, its harmfulness, or the need to resort to maintenance in short or long times [4]-[7]. Indeed, it is the duty of those involved in PD testing to provide such information through straightforward outputs, as numbers or traffic lights.

The purpose of this article is to suggest a health-index based approach that can be applied to rotating machines. Information from PD monitoring or spot testing, taking into account the level of harmfulness of each PD phenomenon, as well as of its time evolution, and the machine maintenance, age, and loading history, all of which is synthetized into a single number giving the health condition of the machine, and an indication of the type, time and advantage of a maintenance action. The Health Index Concept The health index (𝐻𝐼) concept has the purpose of providing an indication of the health condition of an electrical apparatus. The HI is based on the measurement of diagnostic properties and on various information on age, environmental conditions, operating conditions and on previous maintenance of the apparatus [8]]-[Errore. L'origine riferimento non è stata trovata.. If, for the sake of simplicity, a single diagnostic marker 𝑋 is considered (in a real case 𝑋 would be a function of a number of elementary diagnostic markers), HI can be defined as 𝐻𝐼 = 1 − Pr(𝐹|𝑋) where Pr(𝐹|𝑋) is the conditional probability of failure given a level of the diagnostic marker 𝑋. Therefore, the higher the probability of failure, the lower 𝐻𝐼 becomes. However, if the diagnostic properties are permanently or intermittently measured as a function of time under operation, then another definition can be given and that is, the dynamic health index (𝐷𝐻𝐼) 𝐷𝐻𝐼 (𝑡) = 1 − Pr 𝐹 𝑋(𝑡) where the diagnostic marker 𝑋 would be defined having an initial value 𝑋 at 𝑡 = 0 and ageing 𝐴 = 0. The limiting value for X, or XL, which corresponds to the end of useful life (not necessarily breakdown, but of high risk to be able to withstand operating or transient stresses) is reached when 𝐴 = 𝐴 . The system should arrive at this limiting value in correspondence to the design life, 𝑡 = 𝐿 ; if the limiting value is reached for t < 𝐿 , maintenance is required. Depending on the value of 𝐴 , or the 𝐷𝐻𝐼, in correspondence of 𝑡 = 𝐿 , life extension plans can be developed, increasing the return of investment (ROI) for the asset. There are often misunderstandings about the fundamental definitions of age and ageing for electrical apparatus [12], [13]. While age is time under stress, or operation time (hours/days/years), ageing is the irreversible change of insulation properties which can affect service operation and reliability due to service stresses. Therefore, DHI will be function of time under stress (age), but it will be correlated with aging, A, rather than with time. In other words, while age is an useful indication to establish HI, one can expect to achieve correlation between HI and A, and not necessarily with age. Old rotating machines can still have a very high HI, if designed and stressed properly, while relatively new machines can experience low HI if overstressd or not designed properly.

Figure 1, taken from [8], shows, as an example, a correlation plot between HI and age (operation time) for transformers. Clearly, the regression line has near zero slope, which indicates no correlation between 𝐻𝐼 and age. The correlation, however, should exist between 𝐻𝐼 and the aging

property 𝑋, if the measured property is a diagnostic quantity. The expectation is that the generic diagnostic marker 𝑋 can be either increase or decrease, as a trend, with time and ageing. Examples could be PD amplitude and repetition rate which may increase with time in solid dielectric systems subject to electrical ageing, or space charge accumulated in insulation under DC stress, which would also increase with aging. Both PD and space charge would cause insulation breakdown, if appropriate and timely maintenance actions are not taken [13]]-[Errore. L'origine riferimento non è stata trovata..

Figure 1. Example of the correlation between the HI and age (operation time) for transformers with near zero regression line slope indicating no correlation between 𝐻𝐼 and age. Four different areas are highlighted: A, new transformers in good condition (high 𝐻𝐼); B, relatively new transformers in bad condition (low 𝐻𝐼); C, old transformers in good condition (high HI) and D, old transformers in bad condition (low HI). After [8]. Figure 2 shows an example of the ideal correlation of a diagnostic property, X, to ageing 𝐴. Here the relationship between X and A is assumed to be linear (this seldom occurs in reality). This is, however, not a fundamental aspect of this discussion, which remains still valid for other kinds of correlations occurring in real life. Based on experience, manuals and standards, threshold (limit) levels may be assigned to the diagnostic property and the relevant aging, such as “Good”, “Fair”, “Poor” and “Action”. Alternatively, to the linguistic attribute, a rank between 1 and 4 can be used, where 1 corresponds to Good and 4 to Action with possible various intermediate levels. Hence, experience and knowledge about the relation between the value of a diagnostic marker and the failure probability has to be used to define the different classes Errore. L'origine riferimento non è stata trovata.]-[Errore. L'origine riferimento non è stata trovata..

Figure 2. Example of a diagnostic property X verses aging level A, and determination of limit levels for A and X where 𝑋 is the initial value of the diagnostic property, 𝑋 is the diagnostic property limit and 𝐴 is the ageing limit. The first step to build up the 𝐻𝐼 or 𝐷𝐻𝐼 structure is to divide the equipment under test (EUT) in different subcomponents 𝑖, which may have different technologies and materials, and are affected by different failure mechanisms. For each subcomponent, a number of 𝑗 diagnostic indicators (𝑋 , ) are considered and associated to a score 𝑆 , . According to Figure 2, 𝑆 , = 1 ÷ 4, but any other range is acceptable. Then, a weight needs to be attributed to each diagnostic marker, depending on its harmfulness in terms of aging and breakdown processes. The more the specific diagnostic property is associated with an aging mechanism, which has a fast degradation rate, the higher will be its weight. For this purpose, weighting factors 𝑊 , are applied to each diagnostic marker and subsystem ranging between 1 and 10. A weighted average 𝑊 , can be used to account for score and weight of all markers considered for the specific sub-component of the EUT. Thus, ∑ 𝑆 , ∙ 𝑊, 𝑊 , = ∑ 𝑊, where 𝑀 is the number of diagnostic indicators monitored for the subcomponent 𝑖. The next step is to take into account all information coming from visual inspection, failure rate data available in an historical database, equipment loading, environmental conditions and age. Specifically, visual inspections can be carried out on electrical apparatus, in order to check major/minor problems and the likelihood of the information provided by a diagnostic marker. These inspections will result in an adjustment factor 𝐴𝐹 which is summed to the 𝑊 , bringing to the partial score 𝑃𝑆𝐶 of the subcomponent 𝑖 under test where 𝑃𝑆𝐶 = 𝑊 , + 𝐴𝐹 . As asset managers or electrical equipment manufacturers normally will have data on the failure rate contribution of each sub-component to the total failure rate of the equipment 𝐹𝑅 which will become another coefficient to be taken into account in the HI estimation. Using a weighted sum of the above coefficients, the 𝐻𝐼 or 𝐷𝐻𝐼 can be calculated according to 𝐻𝐼 = 1 −

𝐹𝑅 ∙ (𝑃𝑆𝐶 − 𝑆𝐶 ) 𝑆𝐶 , − 𝑆𝐶 ,

(1)

where N is the number of subcomponents of the apparatus under monitoring, 𝑺𝑪𝒎𝒂𝒙,𝒊 and 𝑺𝑪𝒎𝒊𝒏,𝒊 are respectively the maximum and minimum score values for the diagnostic indicators of the

subcomponent 𝒊 and 1 infers a good condition and 4 is a bad condition (action), or alternatively 1=green and 4=red as in Figure 2. Eventually, a final adjustment factor, 𝐴𝐹 , which considers equipment history (load, overvoltages, maintenance, etc.) and age can be summed to the 𝐻𝐼 value to obtain the final 𝐻𝐼. It is noteworthy that most of the above coefficients, that is, 𝑆 , 𝑊 , , 𝐴𝐹 , 𝑃𝑆𝐶 and 𝐴𝐹 are functions of time under operation, and thus will dynamically modify 𝐷𝐻𝐼.

PD sources and relevant harmfulness in rotating machines IEC 60034-27-1 addresses mixed organic-inorganic electrical insulations for rotating machines, referred to as Type II insulation in the standard, and Table 1 reproduced here shows that the severity of PD sources is an issue [20]. The purpose is clearly to address an obvious and common request from maintenance people, that is, how much PD is harmful, what is the machine condition, and which maintenance actions and when they have to be taken to assure the expected design life and reliability requisites. Other papers speculate on this aspect, and the approach to separate PD from noise, reject the noise and identify the PD sources will become essential to address the trend towards smart grid and self-diagnosing apparatus Errore. L'origine riferimento non è stata trovata.], [[20]]-[Errore. L'origine riferimento non è stata trovata.. Table 1 identifies, from laboratory testing and field experience, the harmfulness or impact on ageing of PD sources, indicating that the effect on aging of some PD sources is greater than others, even if the associated PD may be lower. For example, slot discharges may have lower PD amplitude than end-winding gap discharges, but their impact on aging rate can be significantly higher. Therefore, timely maintenance can be triggered when slot discharges reach a given level of amplitude/repetition rate, but action can be delayed until the next planned outage when bar-to-bar discharges (end winding discharges) are present, even if having much larger amplitude than the slot discharges. Table 1. Severity associated with the main PD sources in Type II insulated rotating machines (from Errore. L'origine riferimento non è stata trovata.). PD source Internal voids Internal delamination Internal debonding between conductors and insulation

Slot discharges

End-winding gap and surface discharges

Contamination

Insulation condition Internal PD generated in the pockets of air or gas trapped in inner parts of the main insulation. Those defects arise particularly during the manufacturing process. Under normal circumstances, internal discharges do not lead to remarkable ageing. Internal PD generated in regions of air or gas elongated in longitudinal direction and trapped in inner parts of the main insulation. They are caused from thermal cycling, overheating or from mechanical stress, leading to separation of large areas between insulation layers. Debonding PD between conductors and insulation material are generated in regions of air or gas elongated in longitudinal direction and trapped between the main insulation and the field grading material. They often arise from long term thermal cycling/overheating or from mechanical stress that can lead to separation of large areas between insulation layers. Poor or missing contact between the conductive slot coating and the stator slot wall is the cause of slot discharges. A too high local resistance, or vibration of loose coils and bars may damage the conductive coating. Vibration from electromagnetic forces on too loose bars or coils can also trigger sparking when the conductive coating is too conductive, causing slot discharges. The insulation surface in the proximity of the end-winding section of a machine is generally affected by PD. They often result from conductive contamination (carbon, oily dust, abrasion, etc.) or from damaged field grading materials. Being a phenomenon affecting mostly the surface of the insulation, they normally do not lead to significant ageing. However, the presence of other factors such as high ozone concentration or surface contamination, can accelerate ageing. In the presence of particles contaminating the insulation surface in the proximity of the endwinding section of a machine, PD are generally formed. They often result from conductive contamination (carbon, oily dust, abrasion, etc.) or from damaged field grading materials. Being a phenomenon affecting mostly the surface of the insulation, they normally do not lead to significant ageing. However, the presence of other factors such as high ozone concentration or surface contamination, can accelerate ageing.

Influence on ageing Low Medium Bar winding: Medium Coil winding: High Tight winding: Medium Loose winding: High

Medium

Medium

Figure 3. shows an example of PD measurements on a 15 kV/120 MW gas-turbine air-cooled generator. Various PD phenomena are separated and identified, as well as noise and disturbance

(cross talk). Separation is done through the TF map approach (classification map), which allows sub-patterns, pertinent to a single phenomenon, to be extracted from the global PD pattern recorded by the detector [5]], [Errore. L'origine riferimento non è stata trovata.]-[Errore. L'origine riferimento non è stata trovata.. In this way, PD identification is made easier, and it can be done also automatically without the need of an expert, for example through artificial intelligence techniques such as fuzzy logic as illustrated by Figure 4 [5]], [Errore. L'origine riferimento non è stata trovata.. In Errore. L'origine riferimento non è stata trovata., two sub-patterns from Figure 3. are taken to show how the identification of the PD is achieved through fuzzy logic. Referring to Table 1, both distributed microvoids and end-winding gap discharges are of low or moderate impact on generator insulation aging. Thus, action would not be required even if the latter has considerable PD intensity, of the order of 700 mV. The effect of end-winding discharges could be assessed by visual inspection at the next planned outage. It is noteworthy to indicate that, as reported in Errore. L'origine riferimento non è stata trovata., there are various methods for PD identification and noise rejection, besides using the TF map approach.

Figure 3. Example of PD measurements on a 15 kV/120 MW gas-turbine air cooled generator showing the entire pattern acquisition, the classification map, and the sub-patterns from which five individual sources are identified, two of which are major PD sources identified using the fuzzy identification tool described in Figure 4; namely, distributed internal microvoids and bar-to-bar or bar-to-ground end-winding gap discharges.

(a)

(b)

(c)

(d)

Figure 3. Sub-patterns from Figure 3 (a and c) and identification of the source generating PD by fuzzy logic (b and d). Health Index for rotating machines Subclass 𝑖

Subcategory 𝑗

𝑆,

𝑊,

𝑊 𝐴𝐹

,

𝑃𝑆𝐶 𝐹𝑅

𝐻𝐼 𝐴𝐹

𝐻𝐼

Figure 4. Structure for HI calculation for Type II rotating machine insulation. Four sub-classes are chosen, merging some of the PD sources of Table 1, each one relevant to one or more PD categories. The first step in structuring HI is to choose appropriate diagnostic properties and subcomponents. The approach here is to focus on PD as the diagnostic property, X, and to consider its

impact on aging as the criterion for the selection of sub-classes, rather than referring to physical sub-components of a rotating machine. In this way, 𝐻𝐼 which is relevant to the PD effect on rotating machine reliability could be obtained and the consequent maintenance action decided. Thus, Table 1 can be used as a reference to define a number of sub-components or sub-classes, 𝑖 = 4, which differ for the type of source category. The internal discharges include three categories; namely, distributed microvoids, internal delamination, and debonding, as well as end-winding gap and surface discharges (see the summary in Figure 4). The rationale is that each type of PD source has a number of sub-categories,𝑗, which differ for property values, or score, (amplitude and repetition rate) and weight. The score, 𝑆 , , can range between 1 and 4 (according to

Figure 1), and the weight factor, 𝑊 , , between 1 and 3 (low as internal void discharges, moderate as end-winding gap discharges, and high as slot discharges). Then, 𝑊 , can be calculated for each sub-class, 𝑖. At each outage, inspections can be carried out of the slots or the overhang to check for the effect of PD, thus obtaining a value for the adjustment factor 𝐴𝐹 , which sums to the 𝑊 , , bringing to the partial score, 𝑃𝑆𝐶 of the sub-class 𝑖. If asset managers or electrical equipment manufacturers have data on the failure rate contribution, 𝐹𝑅 , of each sub-class of PD typology to the total failure rate of the equipment, this can be added. Then, the 𝐻𝐼 can be calculated, using (5), and a final correction can be implemented based on historical information on the loading profile, transients, and the age of the machine, that is, coefficient 𝐴𝐹 .

Discussion and conclusion 𝑇ℎ𝑒 𝐻𝐼 calculated based on (5) is a number ranging between 0 and 1, which can be associated with a simple traffic light information, such as red when 0 < 𝐻𝐼 < 0.25, yellow when 0.25 ≤ 𝐻𝐼 < 0.75 and green when 0.75 ≤ 𝐻𝐼 < 1, which is immediately recognized by the asset manager and it can be implemented in any SCADA management software. The information coming from this approach is no longer only the maximum PD amplitude and repetition rate, but a weight of PD source harmfulness obtained all the PD sources which can be active in a rotating machine at a certain operation time. Thus, this seems to be an approach which provides a very simple and straightforward output, but being able to take into account the different PD typologies occurring in an insulation system, with their intensity and harmfulness. As an example of the application of the proposed algorithm, the measurements summarized in Figure 3. used in accordance with the scheme proposed in Figure 5. There are two sub-classes which are involved in the machine under test, that is, internal discharges (sub-category distributed microvoids) with score 3 and weight 1, and end-winding gap discharges (sub-category bar-to-bar discharges) with score 2 and weight 2. Assuming 𝐴𝐹 = 0.5 for the latter (visual inspections shows significant surface erosion), failure statistics 𝐹𝑅 = 15% and 20% for the internal and endwinding sub-classes, respectively, eq. (5) provides 𝐻𝐼 = 0.81 at the time the PD measurements were taken. Then, an adjustment factor, 𝐴𝐹 of -10% can be considered, due to medium age of the machine and loading profiles during operation from time zero adequate to machine design,

obtaining 𝐻𝐼 = 0.71. This is in the yellow-light category, which indicates attention from the maintenance manager. The conclusion seems to be that, based on this approach, the need expressed by asset and maintenance managers to have simple and clear output from partial discharge measurements performed on rotating machines can be addressed successfully. As a final note, such an approach could be extended to any electrical apparatus where PD measurements are a fundamental diagnostic property and where more sources of PD can be active, or ambiguities in PD identification exist. References [1] Rotating Electrical Machines. Off-line partial discharge measurements on the winding insulation, IEC 60034-27-1 Standard, CD: 2017. [2] I. Blokhintsev, J. Kozusko, B. Oberer, D. Anzaldi, “Simple statistical approach presenting Partial Discharge data in large pool of MV motors in continuous and remote PD monitoring system”, 2017 Electrical Insulation Conference (EIC), 2017, pp. 339-343. [3] G. C. Stone, C. Chan and H. G. Sedding, "On-line partial discharge measurement in hydrogencooled generators," 2016 IEEE Electrical Insulation Conference (EIC), Montreal, QC, 2016, pp. 194-197. [4] C. Hudon, N. Amyot, M. Lévesque, M. Essalihi and C. Millet, "Using integrated generator diagnosis to perform condition based maintenance," 2015 IEEE Electrical Insulation Conference (EIC), Seattle, WA, 2015, pp. 341-345. [5] A. Cavallini, M. Conti, A. Contin, G. C. Montanari, "Advanced PD inference in on-field measurements. Part 2: Identification of defects in solid insulation ", IEEE Trans. Dielectr. Electr. Insul., vol. 10, pp. 528-538, 2003. [6] M. Cacciari, A. Contin and G. C. Montanari, "Use of a mixed-Weibull distribution for the identification of PD phenomena [rotating machines]," in IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, no. 6, pp. 1666-1179, Dec 1995. [7] A. Contin, A. Cavallini, G. C. Montanari and F. Puletli, "A novel technique for the identification of defects in stator bar insulation systems by partial discharge measurements," Conference Record of the 2000 IEEE International Symposium on Electrical Insulation (Cat. No.00CH37075), Anaheim, CA, 2000, pp. 501-505. [8] A. Jahromi, R. Piercy, S. Cress, W. Fan, "An approach to power transformer asset management using health index," IEEE Electrical Insulation Magazine, vol.25, no.2, pp.20,34, March-April 2009. [9] F. O. Fernández, A. Ortiz, F. Delgado, I. Fernández, A. Santisteban , A. Cavallini “Transformer health indices calculation considering hot-spot temperature and load index”, IEEE Electrical Insulation Magazine, Vol. 33, no.2, pp.35-43, March-April 2017. [10] G. C. Montanari, "Condition monitoring and dynamic Health Index in electrical grids," 2016 International Conference on Condition Monitoring and Diagnosis (CMD), Xi'an, 2016, pp. 8285. [11] G.C. Montanari, G. Mazzanti., “From thermodynamic to phenomenological multi-stress models for insulating materials without or with evidence of threshold”, J. Phys. D: Appl. Phys., Vol. 27, pp. 1691-1702, 1994. [12] G.C. Montanari, G. Mazzanti, L. Simoni, "Progress in electrothermal life modeling of electrical insulation during the last decades", in IEEE Trans. on Dielectrics and Electrical Insulation, Vol. 9, n. 5, pp. 730-745, Oct. 2002. [13] M. Tozzi, A. Cavallini, G.C. Montanari, “Partial discharge monitoring for LV and MV motors fed by adjustable speed drive electronics”, in Proceedings of the INDUCTICA, CWIEME Berlin (2011), pp. 1-7, Berlin, May 2011.

[14] L. Dissado, G. Mazzanti and G. Montanari, "The incorporation of space charge degradation in the life model for electrical insulating materials", IEEE Transactions on Dielectrics and Electrical Insulation, vol. 2, no. 6, pp. 1147-1158, 1995. [15] G.C. Montanari, “Time behavior of Partial Discharges and life of Type II turn insulation specimens under repetitive impulse and sinusoidal waveforms”, to be published in IEEE Electrical Insulation Magazine, 2017. [16] G. C. Montanari, P. Morshuis and L. Paschini, "A Smart Grid approach to condition based maintenance of renewable energy assets," 2014 Saudi Arabia Smart Grid Conference (SASG), Jeddah, 2014, pp. 1-7. [17] A. Cavallini, G. C. Montanari and F. Ciani, "Diagnosis of EHV and HV Transformers Through an Innovative Technique: Perspectives for Asset Management," Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, Vancouver, BC, 2008, pp. 287-290. [18] G. C. Montanari, "Envisaging links between fundamental research in electrical insulation and electrical asset management," in IEEE Electrical Insulation Magazine, vol. 24, no. 6, pp. 7-21, November-December 2008. [19] Rotating electrical machines - Part 18-42: Qualification and acceptance tests for partial discharge resistant electrical insulation systems (Type II) used in rotating electrical machines fed from voltage converters, IEC 60034-18-42 Standard, CV: 2016. [20] G. Stone, E. Boulter, I. Culbert, H. Dhirani, Electrical Insulation for Rotating Machines: Design, Evaluation, Aging, Testing, and Repair, Wiley-IEEE Press, 2004. [21] C. Hudon, M. Belec and M. Levesque, "Study of slot partial discharges in air-cooled generators," in IEEE Transactions on Dielectrics and Electrical Insulation, vol. 15, no. 6, pp. 1675-1690, December 2008. [22] A. Cavallini, G. C. Montanari, F. Puletti and A. Contin, "A new methodology for the identification of PD in electrical apparatus: properties and applications," in IEEE Transactions on Dielectrics and Electrical Insulation, vol. 12, no. 2, pp. 203-215, April 2005. [23] A. Contin, G. C. Montanari, M. Conti and M. Cacciari, "An invariant diagnostic marker for the identification of partial discharge sources in electrical apparatus," Solid Dielectrics, 2001. ICSD '01. Proceedings of the 2001 IEEE 7th International Conference on, Eindhoven, 2001, pp. 287-290. [24] A. Cavallini, A. Contin, G. C. Montanari and F. Puletti, "Advanced PD inference in on-field measurements. I. Noise rejection," in IEEE Transactions on Dielectrics and Electrical Insulation, vol. 10, no. 2, pp. 216-224, April 2003. [25] A. Cavallini, M. Conti, A. Contin, G. C. Montanari and F. Puletti, "A new algorithm for the identification of defects generating partial discharges in rotating machines," Conference Record of the 2004 IEEE International Symposium on Electrical Insulation, 2004, pp. 204-207. Gian Carlo Montanari (M’87-SM’90-F’00) is currently Professor of Electrical Technology in the Department of Electrical Engineering at the University of Bologna, and teaches courses on technology, reliability and asset management. He has worked since 1979 in the field of aging and endurance of insulating materials and systems, diagnostics of electrical systems, and innovative electrical materials (magnetics, electrets, super-conductors, nanomaterials). He has been engaged also in the fields of power quality and energy market, power electronics, reliability and statistics of electrical systems, and smart grid. He has been recognized with the IEEE Ziu-Yeda, Dakin and Whitehead awards, as well as with the 1906 IEC award. He is a member of AEI and the Institute of Physics. He has been an IEEE DEIS Ad Com member and President of the Italian Chapter of the IEEE DEIS. He is an Associate Editor of IEEE Transactions on Dielectrics and Electrical Insulation, and founder and President of the spin-off company, Techimp, established in 1999. He is author or co-author of about 750 scientific papers.

Paolo Seri (M’17) was born in Macerata, Italy, on June, 4th 1986. He received the Master’s Degree in energy engineering in 2012 and PhD in electrical engineering in 2016, both from the University of Bologna. From 2012 to 2016 he was involved in the activities of the laboratory of Magneto-fluid-dynamics and Plasmas (LIMP) at the University of Bologna, in the field of plasma medicine, plasma sterilization and plasma interaction with liquids. In 2017 he became part of the High Voltage research laboratory (LIMAT) of the University of Bologna as a research fellow, currently working on the topic of dielectric materials electrical characterization.