This paper presents early results from an experimental study of electrical treeing on commercially available pre-formed silicone samples. A needle-plane test arrangement was set up using hypodermic needles. Partial discharge (PD) data was captured using both the IEC 60270 electrical method and radio frequency (RF) sensors, and visual observations are made using a digital microscope. Features of the PD plot that corresponded to electrical tree growth were assessed, evaluating the similarities and differences of both PD measurement techniques. Three univariate phase distributions were extracted from the partial discharge phase-resolved (PRPD) plot and the first four statistical moments were determined. The implications for automated lifetime prediction of insulation samples due to electrical tree development are discussed

Binti Ab Aziz, Nur Hakimah

Catterson, Victoria

Judd, Martin

English

Name not available

Identifying Prognostic Indicators for Electrical Treeing in Solid Insulation through PD Analysis

University of Strathclyde Institutional Repository

Strathprints Institutional RepositoryBinti Ab Aziz, Nur Hakimah and Judd, Martin and Catterson, Victoria (2013) Identifying PrognosticIndicators for Electrical Treeing in Solid Insulation through PD Analysis. [Proceedings Paper]Strathprints is designed to allow users to access the research output of the University of Strathclyde.Copyright c© and Moral Rights for the papers on this site are retained by the individual authorsand/or other copyright owners. You may not engage in further distribution of the material for anyprofitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, ornot-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to Strathprints administrator:mailto:strathprints@strath.ac.ukhttp://strathprints.strath.ac.uk/Identifying Prognostic Indicators for Electrical Treeing in Solid Insulation through PD Analysis  N.H. Aziz*, M.D. Judd, V.M. Catterson University of Strathclyde, Glasgow, U.K *hakimah.aziz@strath.ac.uk    Abstract—This paper presents early results from an experimental study of electrical treeing on commercially available pre-formed silicone samples. A needle-plane test arrangement was set up using hypodermic needle.  Partial discharge (PD) data was captured using both the IEC60270 electrical method and radio frequency (RF) sensors, and visual observations are made using a digital microscope. Features of the PD plot that corresponded to electrical tree growth were assessed, evaluating the similarities and differences of both PD measurement techniques. Three univariate phase distributions were extracted from the partial discharge phase-resolved (PRPD) plot and the first four statistical moments were determined. The implications for automated lifetime prediction of insulation samples due to electrical tree development are discussed. Keywords—Silicone rubber (SiR); electrical treeing; partial discharge; prognostics. I.  INTRODUCTION Prognostic is defined in [1] as “the prediction of the future state of health based on current and historical health conditions”. In solid insulation, the ultimate goal of prognostics is to predict the advent of failure, i.e., insulation breakdown, in terms of remaining useful life (RUL). This paper takes a step towards a prognostic system for insulation breakdown, by investigating features in data which correspond to insulation ageing. Electrical treeing is a common degradation mechanism in solid insulation with progressive faults leading to potentially catastrophic failure. Electrical treeing can be initiated from a water tree or insulation defects that can give rise to large local field concentration. Such defects are metallic protrusions, micro voids and contaminants [2].  The presence of electrical treeing can be detected through partial discharge (PD) monitoring [3]. Automated PD data analysis systems have been shown to correctly diagnose the defect causing PD [4]. This paper aims to extend this concept towards predicting the evolution of the defects by investigating the features of the PD plot which correspond to electrical tree growth; moving from diagnostics (identifying the presence of a tree) to prognostics.    II. EXPERIMENTAL SETUP Various polymers have been used in electrical treeing experiments, e.g. epoxy resin, cross-linked polyethylene (XLPE), ethylene vinyl acetate (EVA), and silicone rubber (SiR). In this paper, SiR is chosen as the test specimen as it requires a very short time to initiate electrical treeing even at low applied voltage.  This preliminary study uses commercially available pre-formed silicone samples, which ensure consistency and eliminate the need for mixing, degassing and heating in sample preparation. The sample is tested using a needle-plane arrangement by inserting a fresh hypodermic needle (50 mm length, 1mm diameter, 10 µm tip radius) into the sample with 2mm gap between the needle tip and the plane electrode. The needle is pulled slightly backward to create a needle-shaped void. The sample is placed against the earth plate inside an acrylic container and immersed in mineral oil to suppress extraneous PD. The dimension of the test sample is illustrated in Fig 1. Both IEC60270 and radio frequency (RF) detection methods were applied for PD monitoring. The phase-resolved partial discharge (PRPD) patterns from both techniques were recorded through computer aided PD measuring system, i.e. LDS-6 (Doble Lemke) and PortSUB (Qualitrol) respectively. The apparent charge magnitude from LDS-6 is recorded manually every 5 minutes followed by a 2-minute downtime. PortSUB on the other hand, records the corresponding level of detected signal automatically in 64 phase buckets per cycle for 255 cycles. The growth of electrical treeing is monitored simultaneously using a digital microscope (AM2011 Dino-Lite Basic) and the image of the test sample is captured every 5 minutes.  III. RESULTS AND DISCUSSION This paper reports results from one particular electrical treeing experiment in a pre-formed SiR sample. This sample was chosen for detailed study, as the morphology of the tree corresponds with other experiments reported in the literature. The test sample was aged for 18 hours and the growth evolution is highlighted below by referring to the voltage application regime outlined in Fig. 2. The voltage was periodically switched off as the experiment could not be left unattended, accepting that this may influence the tree growth.  This work is supported by Minister of Higher Education Malaysia and Universiti Teknikal Malaysia Melaka.     Fig. 1. Test sample. A. Optical Observation Stage A: Fig. 3a shows the image microscope before the experiment starts (poinvoltage was applied to the sample at 0.3kV/2.9kV (the initial voltage of the supply). (point A1), the PD started to show higbackground noise level. At this point, it iselectrical tree is initiated giving the partial dvoltage (PDIV) of 7.1kV. However, it is difelectrical tree from the captured image shoorder to speed up the tree growth, the appincreased to a value marginally above PDIV, Stage B: Constant voltage of 8kV was apfor nearly 5 hours to investigate the tree propInitially, the tree growth rate is very low at(measure of the tree length from the needlehours (point B1), the PD values showed signmagnitude and number of occurrence as thebush shape (Fig. 3c).  Stage C: The effect of higher voltage onshape was observed by increasing the voltagminutes (point C1), a new tree started to grotip as indicated by the darker point in Fig 3later (point C2), the tree became a trunk whentwo branches as illustrated in Fig. 3e. Thereaches the earth plate (Fig. 3f) was at pohours) with 8µm/min growth rate which is B. However, the breakdown did not occur awere created after that, e.g. at point C4. Threported in [5] with the assumption that the to cause breakdown. Over time, the applied obecame less reliable as the tree grew at erequires more than one camera to capture accB. PRPD Pattern  This paper employed the combination of technique as recommended in [6] as a podiagnostic tool. Fig. 4 shows the PRPD IEC60270 measurements corresponding to thFig. 5 for events occurred at points B1, C3  Overall, the RF technique gives the fairly simfor all three growth points.   In this experiment, electrical treeing wwhen PD measurement showed an increamplitude and repetition rate. These  from the digital t A0). First, ramp min starting from After 13 minutes her values than  assumed that an ischarge inception ficult to detect the wn in Fig 3b. In lied voltage was i.e. 8kV. plied at this stage agation behavior.  about 1.8µm/min  tip). Only after 4 ificant changes in  tree grew into a  the existing tree e to 9kV. After 26 w from the needle d. About an hour  it started to form  first branch that int C3 (after 4.5 greater than Stage nd more branches e same result was current is too low ptical observation very angle which urate images.  IEC60270 and RF tential successful pattern from the e RF technique in and C4 of Fig. 2. ilar PRPD pattern as first observed ase in both the values, however, fluctuated throughout the expetreeing activity. From observatvery high repetition rate, thsignificant change in tree growvalue of the discharges. The hwith high levels of PD activitytype tree is detected or an eleneedle tip (as described in SecC1 of Fig. 2). For instance, produced the simultaneous PD maximum discharge greater thacreation of tree branches from tFig. 2 results in a high numbemaximum discharge, i.e. less tand 5c. As the tree branches stouch the earth plate, fewer PDbranch to touch the ground ipatterns are depicted in Fig. 4blower than 15pC.  Fig. 2. Voltage application regim               (a)                            (b)                   (d)                            (e)      Fig. 3. Tree images captured from d(b) A1, (c) B1, (d) C1, (e) C2riment depending on electrical ion, when the PRPD showed a e recorded images showed a th regardless of the maximum igh repetition rate is associated  (50pC - 300pC) when a bush ctrical tree is initiated from the tion IIIA for points A1, B1 and the bush type tree in Fig. 3c pattern of Fig. 4a and 5a with a n 100pC. In a different case, the he existing trunk at point C4 of r of PD occurrences but lower han 15pC as shown in Fig. 4c top growing when the branches  pulses are detected. The first s at point C3 and the PRPD  and 5b with apparent charges  e for electrical treeing experiment.                         (c)                         (f) igital microscope at points of  (a) A0,   and (f) C3 referring to Fig. 2. (a) (b) (c) Fig. 4. PRPD patterns measured with IEC60270 st(a) B1, (b) C3 and (c) C4 referring to F C. Statistical Analysis  In order to identify features of the PD plothe tree growth, three univariate phase extracted from the PRPD patterns of both techniques: Pulse number distribution, Hn(?height distribution, Hqm(?), and mean pulse Hqn(?). Four statistical moments of medeviation, σ, skewness, Sk, and kurtosis, Ku,the positive and negative half cycles of those A number of features from the IEC60technique (Fig. 6 and 7 respectively) havcorresponding to the tree growth: 1) The mean and standard deviation negative half cycles are symmetrical for all in the IEC60270 data: This feature had bdetail in [8]. However, for RF techniquedistribution is symmetrical.  2) The mean and standard deviation of Hincrease in absolute value as the sample agepulses per cycle is generally increasing witree, but also the number of pulses per cyclecycle  to  cycle  as  the  insulation  ages.  Wh   andard at points of          ig. 2. t corresponding to distributions are PD measurement ), maximum pulse height distribution an, µ, standard  are calculated for distributions [7].  270 and the RF e been identified of positive and three distributions een discussed in , only the Hn(?) n(?) distribution s: The number of th the size of the  varies more from en comparing this  (((Fig. 5. PRPD patterns measured w(b) C3 and (c) C4 feature  between  IEC60270  atechnique has a wider range odifference in data range explain3) Skewness of all three  dipositive to negative: As the distributions tend to move fromthe nearer the end. This is clearthe RF technique, which transskew as the sample ages.  4) Kurtosis of Hqm(?) and zero: Most of the time, the nclose to the center of the half cyIV. CONCLUSIONIdentifying prognostic inpredicting RUL of the insulatioidentified in this paper througHqm(?), and Hqn(?) distributiolisted in Section III(C) wiprediction. As electrical treeingevolves over time, thus, onlycharacteristics over time are cdeviation of Hn(?) and skewHqn(?).     a)  b)  c) ith RF technique at points of  (a) B1,       referring to Fig. 2.  nd  RF  technique,  the  former f pulse numbers because of the ed in Section II. stributions tend to change from tree grows, the peaks of PD  the start of one half cycle to est in the Hn(?) distributions of itions from positive to negative Hqn(?) distributions is  almost umber of pulses clusters fairly cle.   AND FUTURE WORK dicators is the first step in n system. Those indicators are h statistical analysis on Hn(?), ns. However, not all features ll be considered in lifetime  is a degradation process which  features that have significant hosen, i.e. mean and standard ness of Hn(?), Hqm(?), and      Both IEC60270 and RF technique show fairly similar PRPD patterns. From statistical analysis, the RF technique shows larger variation because of the reduced phase resolution. This work will continue the identification of prognostic indicators using other promising methods, e.g. pulse shape analysis and pulse sequence analysis. In future, these features will be used for lifetime prediction of insulation samples. REFERENCES [1] N. M. Vichare and M. G. Pecht, “Prognostics and health management of electronics,” IEEE Transactions on Components and Packaging Technologies, vol. 29, no. 1, pp. 222–229, 2006. [2] R. Bozzo, F. Guastavino, and G. C. Montanari, “Electrical tree tests. Probabilistic inference and insulating material evaluation,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 5, no. 5, pp. 734–740, 1998. [3] Suwarno et al., “Partial discharge patterns of electrical treeing in polyethylene,” Proceedings of the 4th International Conference on Properties and Applications of Dielectric Materials,  vol. 1, pp. 379–382, 1994.  [4] E. Gulski, P. Morshuis, and F. Kreuger, “Conventional and time-resolved measurements of partial discharges as a tool for diagnosis of insulating materials,” Proceedings of the 4th International Conference on Properties and Applications of Dielectric Materials,  vol. 2, pp. 666–669, 1994. [5] R. Vogelsang, B. Fruth, T. Farr, and K. Frhlich, “Detection of electrical tree propagation by partial discharge measurements,” European Transactions on Electrical Power, vol. 15, no. 3, pp. 271–284, 2005. [6] A. J. Reid, M. Judd, R. Fouracre, B. Stewart, and D. Hepburn, “Identification of simultaneously active partial discharge sources using combined radio frequency and IEC60270 measurement,” Science, Measurement & Technology, IET, vol. 5, no. 3, pp. 102–108, 2011. [7] V. M. Catterson, S. Bahadoorsingh, S. Rudd, S. D. McArthur, and S. M. Rowland, “Identifying harmonic attributes from online partial discharge data,” IEEE Transactions on Power Delivery , vol. 26, no. 3, pp. 1811–1819, 2011. [8] S. J. Dodd, N. Chalashkanov, and J. C. Fothergill, “Statistical analysis of partial discharges from electrical trees grown in a flexible epoxy resin,” Annual Report Conference on Electrical Insulation and Dielectric Phenomena, CEIDP, pp. 666–669, 2008.                   (a)                     (b)         (c) Fig. 6. Statistical features calculated from IEC60270 measurement (a) Pulse number distribution, Hn(?), (b) Mean pulse height distribution, Hqn(?), and            (c) Maximum pulse height distribution, Hqm(?).             (a)        (b)         (c) Fig. 7. Statistical features calculated from RF technique (a) Pulse number distribution, Hn(?), (b) Mean pulse height distribution, Hqn(?), and                             (c) Maximum pulse height distribution, Hqm(?). 

http://dx.doi.org/10.1109/ICSD.2013.6619720

Identifying Prognostic Indicators for Electrical Treeing in Solid Insulation through PD Analysis

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