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The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April 2015. Analysis and Characterization of Partial Discharge Signals From Instrument Transformer in Noisy Environment Chong Yu Ping. Email: [email protected] Chua Chun Piau. Email: [email protected] Yasmin Hanum Md Thayoob. Email: [email protected] Syed Khaleel Ahmed. Email: [email protected] College of Engineering, Universiti Tenaga Nasional, Malaysia. Yogendra A/L Balasubramaniam Department of Transformer Diagnosis, TNB Research SDN. BHD, Malaysia. Email: [email protected] Abstract—Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-services high voltage electrical equipment. On-site PD detection methods are preferable compared to others method because non-intrusive sensors are easy to install and have no interruptions on operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in PD. The PD signals are hard to distinguish from the stochastic noise pulses. Therefore, the characteristics of PD signals and interferences signals are studied in Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD signals. Keywords—partial discharge; instrument transformer; inteferences; phase resolved partial discharge pattern I. INTRODUCTION Instrument transformers (IT) are high accuracy electrical devices that designed to transform voltage or current from the high values to low values in the transmission and distribution systems that can be utilized by low voltage metering devices [1]. It also can be used to isolate the utilization current or voltage from the supply voltage for safety to both the operator and the end device in use [2]. IT primary functions are used to metering for energy billing and transaction purposes and protection control for system protection and protective relaying purposes in transmission and distribution system. IT plays an important role in electrical system. Therefore, condition assessment of in-service high voltage instrument transformer should be carried out to determine failures in IT. Partial detection (PD) detection is one of the important methods used to monitor and evaluate the condition of in-service high voltage electrical equipment. Online partial discharge testing is performed during real operating condition with normal operating voltage. This is a non-destructive test; therefore nonintrusive sensors which are easy to install and have no interruptions on operation are preferred for on-site PD detection. An electrical PD test utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the instrument transformer was used to detect partial discharge activities [3]. As defined by IEC60270, ‘Partial discharge is a localized electrical discharge that only partially bridges the insulation between conductors and which can or cannot occur adjacent to a conductor [4]. PD usually started within voids and cracks. Since PDs are limited to only a portion of the insulation, the discharges only partially bridge the distance between electrodes. PD can also occur along the boundary between different insulating materials. The electric field across the void is significantly higher than that across an equivalent distance of dielectric due to the dielectric constant of the void is considerably less than the surrounding dielectric. If the voltage stress across the void is increased above the corona inception voltage (CIV) for the gas within the void, PD activity will start within the void [5]. PD detection is using to monitor the status of IT; however, PD measurement performed on-site and under noisy condition is a challenging task. The measurement often lacks of accuracy due to interference in PD signals [6]. The PD signal is superposed with stochastic noise pulses or even multiple PD sources, which leads to a complex phase resolved PD patterns that is not easy to analyse and distinguish. Therefore, characteristics of PD and noise are studied in order to differentiate by using phase resolved partial discharge (PRPD) pattern. According to IEEE standard (IEEE Std C57.113-2010) that the possible noise could be encountered from on-site measurement such as harmonics, radio frequency (RF) noise, corona and sparking that can be generated by several kinds of sources [7]. Analysis of possible interferences pattern is essential to distinguish PD pattern from interferences pattern by using PRPD pattern. II. PHASE RESOLVED PARTIAL DISCHARGE (PRPD) In PRPD, the phase of occurrence, apparent charge and number of PDs which has the same phase and magnitude can ISBN 978-967-5770-63-0 104 The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April 2015. be recorded. For data recording purpose, usually an m × n matrix is used where m is the number of phase channels and n is the number of charge levels [8]. Each element of this matrix shows the number of PDs with a particular magnitude and phase. This method can provide different patterns such as the phase of occurrence (φ) versus the number of PD (n), the phase of occurrence (φ) versus the maximum apparent charge(qm), the phase of occurrence (φ) versus the average apparent charge (qa) and the apparent charge (q) versus the number of discharge (n). Furthermore, the second typical characteristic to review is that PD activity is increasing at beginning of each half cycle followed by decreasing like bell-shaped curve as shown in Fig. 2. There is no article or journal draw conclusion about the pattern of PD shall increase at beginning followed by decreasing in PRPD pattern. However, it is easily to see this trend in many published papers about PD in [6, 12-13]. Therefore, these characteristics can be used to further analysis the PRPD pattern obtained from HFCTs. A. PD Pattern Characteristics There are some common phenomena or characteristics of partial discharge in PRPD diagram, such as PD pulses shall occur only in both first and third quadrants in PRPD diagram, PD pulses shall abrupt increase at beginning of first and third quadrants followed by rapid decrease and number of PD pulses in first and third quadrants shall not have big difference. Up till now, there is no article or journal that can draw any conclusion on the partial discharge on instrument transformer. However, it has been accepted that the partial discharge could appear on both first and third quadrants. It is not possible for PD to take place in either first or third quadrants only as PD shall take place in both first and third quadrants simultaneously [1-2, 5]. Furthermore, “characteristic traits of partial discharges occur only during the first and third quarter of each cycle”, as stated in [9]. Therefore this characteristic can be applied on partial discharge recognition analysis. In Fig. 1, PD occurs when voltage across the void, V1 reaches the breakdown voltage, Vi. Voltage V1 reduces until the discharge extinguishes. Then, rises again as applied voltage, V is rising until new breakdown occurs again. This situation repeats again and again, eventually the breakdown of insulation system [10]. Therefore, PD occurs only in positive and negative half cycles in PRPD pattern, where initial rising of positive signal and initial rising of negative signal. This behavior can use to further justify the occurrence of PD. Fig. 2. Typical PD pattern in Phase Resolved PD pattern B. PD Parameters and Determine the Extent of PD activity In PRPD analysis, PD parameters such as PD magnitude and PD count are very important to determine the extent of partial discharge activity. Apparent charge (q), repetition rate and number of pulses (n) with respect to the phase angle (θ) were recorded when measuring the PD activity. PD magnitude usually measured in picoCouloumb (pC) while PD count is the number of occurrence of PD in one second duration. There are no standard for PD magnitude and PD count to determine the extent of PD. However, there are concerns for the safety of the insulation system if the PD magnitude and PD count are high. Disturbance might have contributed to high value of the PD parameters. According to a journal conducted by V. R. Garcia-Colon and his partners that the apparent charge that over a reading, which 300 pC were considered as abnormal and over 1000 pC was considered as extremely high and immediate actions recommended [14]. Therefore 300 pC would be the trigger value used for apparent charge in this experiment. There might be some concerns when the PD magnitude is exceeds 300 pC but the transformer still can function properly. When the PD is exceeds 1000 pC, the IT needed to be test offline by using DGA that is a published method to be used to determine the extends of the partial discharge. III. Fig. 1. Sequence of cavity breakdown under alternating voltages: (a) voltage waveforms and (b) PD Pulses [11] NOISE IN E LECTRICAL PD DETECTION Noise can be classified into two groups, which are nonimpulsive noise and impulsive noise. Non-impulsive noise consists of white noise, which generated from any electrical equipment, such as amplifier and oscilloscope; Harmonic signal, which are generated from communication, wireless and electronics systems. However, impulsive noise can be divided into two groups, which are repetitive pulses and random pulse. Repetitive pulses are generated from Corona and switching ISBN 978-967-5770-63-0 105 The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April 2015. devices, such as AC/DC converter and rectifier. Random pulse is generated from lightning impulses, switching operations, welding, and sparking. According to IEEE standard (IEEE Std C57.113-2010) that the possible noise could be encountered such as harmonics, radio frequency (RF) noise, corona and sparking [7]. The characteristic noise signature was shown in Fig. 3. Fig. 3(a) refers to corona discharge; it can be easily being identified because corona discharge appears only in one half cycle of applied voltage after exceeding the inception voltage. Fig. 3(b) refers sparks between metallic parts; faulty metallic contact may happen inside or outside the CTs due to corrosion and this discharge pulse repetition rate is significantly higher. Signatures of external interference such as noise caused by power electronics and radio noises were shown in Fig. 3(c). The pulses caused by power electronics can easily being identified because the pulses have equally spaced and roughly same amplitude. site PD data has been tested with DGA test and it shows satisfactory condition, which are KL405, KL136 and Perak205. Fig. 4 shows the PD pattern of experimental data in PRPD pattern. It can be seen that the PD patterns of experimental data occur in first and third quadrant in Fig. 4. PD activity is increasing at beginning of first and third quadrant, and followed by decreasing trend. It is not clear to see this tendency in Fig. 4 (d), because PD signal has been covered up by noise signal, but it is easy to see this tendency in (a), (b) and (c). Therefore, the results from Fig. 4 have been proven that the occurrence of PD can be recognized by using PD pattern recognition, which recognize based on typical characteristics of PD in PRPD pattern. Fig. 3. PD pattern of experimental data in PRPD pattern: (a) exp132332 (b) exp150246 (c) exp154523 (d) exp153818 Fig.5 illustrates the possible noise identified from the PRPD measurement data from KL North Bay 405 which are in accordance with the possible noise that can exist in an on-site PD measurement that was stipulated by the IEEE Standard. The possible noises have been identified based on Fig. 3 that referred to IEEE Standard [7]. Hence, it is necessary to filter the noise in the on-site PD measurement data before any further diagnosis can be made on the severity of the PD occurrence and determination of the PD level. Fig. 3 Signatures of electromagnetic interferences encountered during PD tests [7] IV. RESULT AND DISCUSSION Data to be analyzed consist of four known experimental data (named as exp132332, exp150246, exp154523 and exp153818), three known and four unknown site data from onsite PD measurement (named as KL405, KL505, KL136, PenangL25, PenangL55, Seremban405 and Perak205). Four known experimental data are recorded under different situations, which are high PD activity without noise, low PD activity without noise, high PD activity with noise and low PD activity with noise accordingly. However, the three known on- Fig. 5 Noise identification on KL North Bay 405 Results from unknown defect site data are justified by PD pattern recognition. Hence, the results obtained have indicated that there are insufficient evidences to determine there are PD activities. As summary, three known and four unknown site ISBN 978-967-5770-63-0 106 The 3rd National Graduate Conference (NatGrad2015), Universiti Tenaga Nasional, Putrajaya Campus, 8-9 April 2015. data from on-site PD measurement (named as KL405, KL505, KL136, PenangL25, PenangL55, Seremban405 and Perak205) are failed to fulfill the criteria to prove that there are PD activities. V. [3] [4] [5] CONCLUSION In conclusion, partial discharge (PD) detection is an important method to evaluate the insulation condition of an instrument transformer (IT). Electrical partial discharge measurement using high frequency current transformer (HFCT) is an alternative technique for PD detection as PD measurement can be done on in-service IT and non-intrusive, without the IT to be offline. However, the limitation of electrical PD measurement is that it is highly susceptible to electromagnetic interference and noise found in any substations which may superposed with PD signals. Hence, this could also be the reason for high PD activities detected during on-site measurement conducted by TNBR before on eight units of the instrument transformers. DGA test carried out on three of the instrument transformers showed that the instrument transformers were in good or acceptable condition. [6] [7] [8] [9] [10] As such in this research, a study has been carried out to study the characteristics of PD to identify noise from on-site electrical PD measurement data obtained from MPD 600 PD measurement system. Initially, the electrical PD measurement data which was in the form of PRPD pattern were visualized and evaluated. Then, the types of noise that could interfere with the measured PD signals were identified. [11] ACKNOWLEDGEMENT This project is funded by TNB Research SDN. BHD. The research work is carried out in collaboration with UNITEN R&D SDN. BHD. [14] [12] [13] Guomin Luo, Daming Zhang, “Study on Performance of HFCT and UHF Sensors in Partial Discharge Detection,” 2010. IPEC 2010 on pp.630. IEC 60720, “Partial Discharge Measurements”, Third Edition, 2000-12. A. Mehta, R. N. Sharma, S. Chauhan, “Partial discharge study by monitoring key gases of power transformers,” Electronics Computer Technology (ICECT), 2011 3rd International Conference on, vol. 4, no., pp. 183, 186, 8-10 April 2011. A. Kraetge, S. Hoek, K. Rethmeier, M. Kruger, P. Winter, “Advanced noise suppression during PD measurement by real-time pulse-waveform analysis of PD pulses and pulse-shaped disturbances,” Electrical Insulation (ISEI), Conference Record of the 2010 IEEE International Symposium on, vol., no., pp. 1, 6, 6-9 June 2010. IEEE Standard C57.113, “IEEE Recommended Pratice for Partial Discharge Measurement in Liquid-Filled Power Transformers and Shunt Reactors,” 2010. Mohamad Ghaffarian Niasar, “Partial Discharge Signatures of Defects in Insulation Systems Consisting of Oil and Oil-impregnated Pape,” KTH Electrical Engineering, pp. 11-20, 27, 28, 2012. Gabe Paolettie, P.E. and Alex Golubev, PhD, “Partial Discharge Theory and Applications to Electrical Systems. Pulp and Paper,” 1999. Industry Technical Conference Record of 1999 Annual, pp.128, 138, 21-25 June 1999. M. S. Naidu, V. Kamaraju, High Voltage Engineering, Fourth Edition, The McGraw-Hill Companies, pp. 99-101. E. Kuffel, W. S. Zaengl, High Voltage Engineering Fundamentals, Pergamon Press, pp. 409. Y. R. Chaudhari, N. R. Bhosale, P. M. Kothoke, “Composite analysis of phase resolved partial discharge patterns using statistical techniques,” International Journal of Modern Engineering Research on, vol. 3, pp. 1947-1457, Jul-Aug 2013. F. H. Kreuger, E. Gulski, A. Krivda, “Classification of partial discharges,” Electrical Insulation, IEEE Transactions on, vol. 28, no. 6, pp. 917, 931, Dec 1993. C. G. Azcarraga, V.R. Garcia-Colon, and A. Nava G, “On-site Testing of Instrument Transformers,” 2006 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, pp. 39, 42, 2006. REFERENCES [1] [2] GE Digital Energy ITI, Instrument Transformer Basic Technical Information and Application, pp. 3. ABB, Instrument Transformers Technical Information and Application Guide, pp. 4. ISBN 978-967-5770-63-0 107