431 1 Building a Transformer Defects Database for UHF Partial Discharge Diagnostics P. Agoris, P. Cichecki, S. Meijer, and J. J. Smit, Member, IEEE In general, for the condition assessment of a power Abstract— In the case of a defective transformer, when a transformer using the UHF PD measuring technique three partial discharge is detected and recorded, critical information steps have to be performed. First of all, detection of any can be deduced from its pattern, such as the type of defect, its partial discharge activity that indicates harmful insulation criticality or even information on the level of degradation of the insulation. This information can help to determine the defects, must be accomplished. After the detection and remaining life of the transformer and thus provide criteria for recording of the PD has taken place, the analysis of the raw its maintenance and operation. In this paper different artificial data would give the appropriate information in order to PD patterns will be recorded in the laboratory, representative of identify the defect in the transformer The identification of the specific transformer defects, in order to build a database for defect can be done by finding the location of the insulation comparison purposes when measuring on-line. This can greatly defect and by comparing its pattern with other known defects improve the recognition and identification of the defect and thus help take some important life assessment conclusions on the from a reference database. All information then has to lead to transformer. a certain assessment of the risk for an insulation failure of the power transformer. Index Terms—Diagnostics, Identification, Pattern In this paper we are interested in the third stage of the Recognition, Partial Discharges, Power Transformer, UHF assessment: that is recognizing the recorded pattern and then classifying the defect in a defects’ database . Finding the location of the defect can also hint to its identification, I. INTRODUCTION however location requires at least three UHF sensors which is O NE of the key components in the electrical power network is the power transformer. A transformer failure not the case in older equipment. On the other hand, a recording of a pattern can be performed by only one UHF implicates considerable costs either due to outage or the sensor, sensitive enough to detect it. Identification is possible refurbishment or replacement of the transformer. As a result, in all transformer cases as long as a proper pattern is the reliability of power transformers is considered to be recorded. Still, however, in this case, assessment of the PD important. A significant number of failures of power nature is very hypothetical if there is no reference pattern of a transformers occur in the dielectric system, mainly due to known defect to compare it with. It is thus necessary to build insulation deterioration and defects. To avoid such failures, a UHF pattern database of transformer defects. periodical assessment of the condition of the transformer Correct analysis of the pattern of the PD, in order to obtain insulation is needed. One of the ways of monitoring the its discriminating characteristics, is necessary. In this paper condition of the insulation is by means of partial discharge three different distinct defect groups are examined. The measurement. Although research until now has not patterns and some statistical features obtained will be progressed enough so that a partial discharge (PD) presented and discussed and will then be used in a database. measurement itself could provide information regarding the Some independent data will then be used to test the remaining life of a transformer, it does give information identification capabilities of the appropriate software. about the quality of insulation, which can help further decision making processes. II. EXPERIMENTAL SET-UP AND PROCEDURE The experiments were performed on a 1MVA transformer Manuscript received October 9, 2001. (Write the date on which you (see Fig. 1). The purpose of the experiments was to build a submitted your paper for review.) This work was sponsored by Tennet. database of the most common transformer defects, which can P. A. Author is with the Delft University of Technology, Delft, 2628CD, The Netherlands (phone: +31 (0)15 27 89042; fax: +31 (0)15 27 88382; e-mail: later be used in the field for the identification of any detected email@example.com). defects. For this purpose, a number of patterns generated from P. C. Author is with the Delft University of Technology, Delft, 2628CD, The Netherlands (e-mail: firstname.lastname@example.org). certain artificial defects inside the 1MVA transformer had to S. M. Author is with the Delft University of Technology, Delft, 2628CD, The be recorded. Therefore, a small opening was made at the top Netherlands (e-mail: email@example.com). of the tank were the artificial defects’ setup could be inserted. J. J. S. Author is with the Delft University of Technology, Delft, 2628CD, The Netherlands (e-mail: firstname.lastname@example.org). The artificial models that were used, were built with the 431 2 idea that they should be the most basic and most cavities. The cavity was placed in the middle of two-brass representative partial discharge defects in power electrodes cast in polyester mass and was mounted in a plastic transformers. Thus the following partial discharge models frame (see Fig. 2). The diameter of the cavity was 3 mm. were designed and prepared: cavity discharges, surface With this model two measured voltages were investigated. The first was at the inception voltage level (at 14kV); In this voltage pulses the discharge ranged from about 11nC to 15nC. Pulses were stable and did not disappear for the next 20 minutes so a measurement could be carried out. In the next step the voltage was increased to 18 kV and another measurement was performed. In this case the PD energy ranged from 11nC to 20nC; the pulses were quite stable and random very high peaks occurred. The maximum discharge Fig. 1. 1MVA transformer with opening on top for artificial defect set-up insertion. The white circled areas indicate the places where the UHF sensors where located. discharges and corona in oil. Patterns for these discharge models were obtained both with the UHF and the IEC 60270 technique; in this paper only the UHF results will be presented and discussed. The measuring procedure was the following: • Increasing the voltage from 0 kV until PD inception voltage, where first PD activity is observed Fig. 3. Internal defect model phase resolved patterns. The corresponding supply • Increasing the voltage to a maximum possible level voltage and measuring frequency are also indicated. (below the breakdown voltage) to find out how the frequency amplitude was 28nC and it was near the breakdown voltage response and phase resolved patterns are changing with range of 25 kV. Results of the PD in the cavity model are voltage. • Recording the frequency spectra in all appropriate voltage levels. • Recording of several phase resolved patterns  at different frequencies of high PD energy to observe if and how Fig. 4. “Vertical point to plane” (3mm pressboard used for tests) and diagram indicating the possible path of the discharges model. presented in Fig. 3. B. Surface discharges Fig. 2. The internal defect model. Surface discharges are a result of a strong stress component the pattern changes with different center frequencies. parallel to the dielectric surface. Such a PD activity results in the deterioration of the insulating material and produces III. THE PARTIAL DISCHARGE MODELS different chemicals (e.g. nitric acid and ozone), which are also harmful for the oil insulation . In transformers, A. Internal defect surface discharges occur mainly in the composite oil-barrier The final result of PD activity due to cavities in the insulation but can also be a result of bubbles trapped on the insulation material, is erosion of the insulation material, insulation or the delamination of the pressboard layers. For chemical changes in material structure and thus local the laboratory measurement the surface discharge model used carbonization of the material and generation of electric is shown in Fig.4. treeing discharges. For this reason cavity discharge model has It was designed such as to create a high tangential field and been constructed to investigate. a high concentration of electric field at a certain part on the Since cavities appear due to various reasons (e.g. bad surface of the pressboard. Pulses occurred immediately at 16 impregnation process, mechanical distortion during kV and a measurement was performed. At this voltage range transporting and voltage overstresses etc) and thus have the maximum amplitude of the discharges was 2.2 nC and different characteristics (geometry, location, material) it is appeared in the negative voltage half of the sinus. difficult to find one representative model for all types of 431 3 The distance between the two electrodes could be set at different values. With this model the inception voltage was 28,5 kV with a pulse amplitude range of 1nC – 3nC; however the pulses were not stable and disappeared from time to time. After increasing Measuring Frequency: 1000MHz Supply Voltage: 18kV Number of Voltage Cycles: 1000 Fig. 7. The internal defect model phase resolved patterns. The corresponding supply voltage and measuring frequency are also indicated. Measuring Frequency: 1000MHz the voltage level to 35 kV pulses the phenomenon started to Supply Voltage: 18kV Number of Voltage Cycles: 300 increase and the PD magnitude was now between 8nC and 11nC. The pulses were stable and in this level measurements Fig. 5. The surface discharges defect model phase resolved pattern. The were performed. After about 20 minutes the voltage was corresponding supply voltage, the measuring frequency and the recording time (in voltage cycles of 50Hz, that is 20 ms) are also indicated. increased to 40 kV. In this range the magnitude of the pulses was from 12nC to 16nC. Recordings are shown in Fig. 7. After about 10 minutes the discharges at 16 kV disappeared and the voltage was increased to 18.4 kV and the IV. IDENTIFICATION measurement was performed again: the pulses ranged between 1.4 and 2.4 nC. The voltage was raised up to 25 kV A. Data analysis and 34 kV. It could be seen that increasing the voltage From the recorded patterns it is possible to perform generated more pulses but the magnitude of these pulses was Fig. 6. HV corona discharges model: (left) HV electrode (needle) with 4 cm distance to the ground electrode and (right) model in plastic frame. not changing considerably. For example at 34 kV the difference with the 19 kV measurement was only 0.5 nC. The patterns obtained are shown in Fig. 5. C. High voltage corona in oil In power transformers corona discharges can appear in the oil due to pollution caused by small conductive particles, which may result in the decrease of the breakdown stress of the oil. Such particles originate from the manufacturing process, from mechanical distortion, or from service operations like oil changing (small particles from pumps and other service equipment). Sometimes sharp edges of grounded parts in the transformer tank can also produce corona Fig. 8. The statistical distribution of a surface discharge phase resolved pattern. discharges. (Top to bottom): a) the maximum and b) the average magnitude of the For the production of corona discharges, a model was built discharges, and c) the number of discharges in each phase window of a sharp high voltage electrode (needle) as seen in Fig. 6. 431 4 recorded data (for each defect) comprised of measurements of 600 sweeps at i) two different voltage levels, ii) two different center frequencies (250 MHz and 1000 MHz); furthermore measurements of 1200 sweeps at again two different voltages and frequencies were also added in the database under the same defect label. The next step was then to calculate the statistical operators and then the standard deviation of each operator for each defect group. The results are shown in Table && It can be seen that some operators are very unstable (their standard deviation is much higher than 1) either for all three Fig. 9. The fingerprint is a set of statistical operators, which describe the defects or for a specific defect. For instance the skewness Hq, distributions shown in Fig. 8. is unstable for the HV Corona defect only, while the Kurtosis different statistic calculations that can describe the pattern of Hq and Hn+ (although it is mainly unstable for HV characteristics in more detail. Initially the statistical Corona) it can be said that it is slightly unstable for the other distributions of a) the maximum and b) the average defects as well (since the standard deviation exceeds 1); in magnitude of the discharges, and c) the number of discharges addition the Kurtosis Hn- is very unstable for the surface in each phase window can be determined as it is discharges. demonstrated in Fig 8. Then several operators are applied to Especially in the case of corona the pattern can really these distributions to describe their characteristic shape (such change with any increase in the supply voltage or the as the symmetry of the pattern, the skewness e.t.c.) . These measuring frequency. This happens because in the negative operators, when collected together will generate the specific cycle of the voltage (second half of the pattern) the discharges fingerprint of the defect, as seen in Fig. 9. Lastly, these are very low and the higher the measuring frequency the operators are used to compare the patterns with each other so smaller and less they become (even disappearing completely that they can be separated into three (or more in other cases) at a certain point due to the limited recording resolution as distinct groups, which will then be added to the database. well); the same applies for the different voltage levels. The comparison procedure was performed based on the Therefore the asymmetry (showing symmetry between the two centourscore algorithm . Since at least three fingerprints in halves of the pattern: i.e. the positive and the negative halves one group are necessary for the algorithm to do a proper of the voltage cycle) and Hq (represents the number of comparison, it was necessary to create the groups manually at discharges as a function of the discharge magnitude) first by copying the appropriate fingerprints to each defect operators will naturally give different results in each case. group. After this initialization the algorithm can be used to With similar logic the Hn operator (the number of pulses in compare the rest of the fingerprints and add them to the each phase window as a function of the phase angle) will proper group. generate big variance for the surface discharge, whose pulse B. Comparison and classification density increases with voltage. The same would apply for any other defect whose pulse rate is not constant and thus short The main purpose of this paper is to see if a correct recording times (thus a few voltage cycles) would always identification of the same defect can be performed with different external factors; these external factors are the supply voltage, the measuring frequency and the recording time of each measurement. Each factor affects differently the end result of a measurement (i.e. the phase resolved pattern). For example, as it was noticed before, during the surface discharge measurements, the higher the supply voltage the more dense the pulses become without however changing the (a) amplitude of the discharge; in this case any statistical operator which relates these two characteristics will generate a different result for each voltage case differentiating the fingerprints. For these reason the statistical operators have to be chosen with care to be independent of such cases. The statistical operators used in the current database software are listed in the left column of Table &&. Their (b) Fig. 10. The Hn distributions of the surface discharge model patterns in Fig. 5. definitions can be found in , however if it is required in the For the same voltage and measuring frequency the (a) 1200 sweeps and (b) 600 following discussion their definition will be stated also. The sweeps recording generate different Hn distributions. 431 5 generate a different Hq and Hn value. An example of this case discharge patterns. Using all statistical operators the result is demonstrated in Fig. 10, where the Hn distributions of the was 20% surface discharge, 10% cavity and 5%HV Corona. patterns in Fig. 5 are shown. Although the patterns represent Removing all the operators related to the Hn parameter the same defect at the same supply voltage and measuring resulted in 30% surface, 20% cavity and 15% HV Corona. It frequency, the difference in the recording time generates seems to increase the likeness to surface discharges but it different Hn characteristics and therefore discriminates the however increases the likeness percentage of the other defect two patterns. groups as well. Although the increase is very small, it can be As far as the rest of the operators are concerned, which are quite decisive in other cases where the likeness is higher. also more amplitude related, they seem to be more stable. Trying different other 100 cycle surface patterns however This is mainly because the operators are calculated after a gave similar results. normalization of the discharge amplitudes (for each recording The main difference with the 100 cycle and 500 cycle separately) and thus is independent of any change in the measurements was the number of pulses recorded; depending amplitude due to the voltage or the measuring frequency. on the duration of the recording the pattern can also look different. Since the shape of the patterns is dependent to the total pulses recorded and since different defects have different Statistical Cavity HV Corona Surface pulse rates – in this case the cavity defect and the surface Operators 0.418 0.245 0.158 defect had quite a high pulse rate compared to corona as it Skewness Hqmax+ can be seen in the patterns of the defects in Fig. 3, 5 and 7) – Skewness Hqmax- 0.375 0.062 0.201 it is difficult to specify a standard measuring time. The pulse Skewness Hqn+ 0.403 0.322 0.163 rate per cycle has to be considered and then the recording Skewness Hqn- 0.375 0.047 0.137 time should be set appropriately to record the same total Skewness Hn+ 0.622 0.474 0.504 pulses as the patterns loaded in the artificial defects’ Skewness Hn- 0.422 0.103 0.535 database. Skewness Hq 0.734 5.607 0.421 V. CONCLUSIONS Kurtosis Hqmax+ 0.209 0.49 0.762 • The behavior of the different partial discharge models in Kurtosis Hqmax- 0.254 0.045 0.324 terms of patterns, repetition rate, and amplitude is Kurtosis Hqn+ 0.204 0.351 0.498 characteristic for each defect type only for the specific Kurtosis Hqn- 0.3 0.046 0.13 voltage, measuring frequency and measuring time that they Kurtosis Hn+ 0.976 2.206 1.258 were recorded. 0.587 0.315 2.424 • The PD measurements should thus be done under similar Kurtosis Hn- conditions and duration time in order to improve the Kurtosis Hq 1.056 113.668 1.251 possibilities for a proper match. Discharge asymmetry • The statistical operators used for the comparison of the 0.655 0.529 0.194 Q Hqmax different phase resolved patterns should be independent of the Discharge asymmetry Q Hqn 0.678 0.408 0.191 pulse rate and be more involved with the actual shape of the pattern (location on phase cycle, skewness of max and Cross Correlation 0.145 0.276 0.183 average normalized amplitudes e.t.c.). Studying the pulse rate Modified Cross Correlation 0.522 0.404 0.22 of a PD should require longer period of measurements in order to observe the pulse sequence in time which is also Peaks found Hqmax+ 0.311 0.354 0.279 characteristic for each defect. Peaks found Hqmax- 0.245 0.473 0.223 • For the comparison of the phase resolved patterns it is Peaks found Hqn+ 0.378 0.415 0.293 necessary that the recording parameters (measuring frequency Peaks found Hqn- 0.233 0.473 0.283 and time) be the same;. Otherwise results may vary since the patterns might be different due to the number of pulses Peaks found Hn+ 0.242 0.333 0.334 recorded which vary the shape of the pattern. Peaks found Hn- 0.21 0.575 0.404 Peaks found Hq 0.146 0.28 0.14 ACKNOWLEDGMENT Furthermore, a test of the classification procedure was The authors would like to acknowledge TenneT and Nuon performed if the unstable operators were removed from the N.V. for their support. classification procedure. The database was consisted with mainly 500 cycle patterns of all three groups. For this REFERENCES example a surface discharge pattern of 100 cycles was to be  P.D.Agoris, S.Meijer and J.J.Smit: “Evaluation of On-Line Insulation classified; the pattern was obtained at the same voltage and Condition Assessment Techniques for Power Transformers” International Symposium on High-Voltage Engineering (ISH), Beijing, China, (2005) measuring frequency as the rest of the 500 cycle surface 431 6  F. Massingue, S. Meijer, P.D. Agoris, J.J. Smit, J. Lopez-Roldan: “Partial Discharge Pattern Analysis of Modeled Insulation Defects in Transformer Insulation” IEEE International Symposium On Electrical Insulation, (ISEI ’06), Toronto, Canada, 2006  A Krivda, “Recognition of discharges discrimination and classification” Delft University press, 1995, Delft  F. H. Hreuger, E. Gulski, A. Krivda, “Classification of Partial Discharges”, IEEE Transactions on Electrical Insulation, Vol. 28 No. 6, December 1993.
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