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Proc. of Int. Conf. on Control, Communication and Power Engineering 2010

Application of Gabor Wavelet for Analysis of Partial Discharge Signal in High Voltage Power Equipment T. Sudarshanam1, K.V. Ramprasad2, H.S.N. Murthy3, A. Govardhan4 and B.P. Singh5 1

Tirumala Engg. College, Dept. of ECE, Hyderabad, India Email: 2 TKR College of Engg. & Tech., Dept. of ECE, Hyderabad, India Email: {,, and} analysis has been carried out for one typical discharge of float type. The methods of detection and wavelet calculation for wedge type of partial discharge are discussed by Sudarshanam et al. [1].

Abstract— This paper describes the method of measurement of various types of partial discharges (PD) in transformer oil. Special samples are prepared and subjected to high voltage. The partial discharge generated are measured with Ultra High Frequency (UHF) antenna and recorded on digital oscilloscope. A typical signal is subjected to FFT analysis. The contribution of each dominant frequency is considered to determine the time ~ frequency characteristic. Finally the signal is reconstructed by eliminating noise.


In order to generate PD data for floating type of discharge a chamber made of steel is used. A PD generating model as shown in Fig. 1.a. to Fig. 1.d. is used for all experimental studies. In order to simulate a float type discharge a small insulating material is introduced between inter-electrode gap. A voltage is applied at H.V electrode marked C and gradually increased until PD starts. In order to ascertain that PD is from floating material and not from the electrode, a calibration is done by applying voltage in the absence of insulating material and gradually increasing until a discharge appears. It is observed that a set of discharge starts at much higher voltage than when a floating material is present. Different size of insulating material for generating floating type discharge was used for obtaining a number of signals. The analysis was carried out for one typical signal of float type Fig. 2.a.

Index Terms—partial discharge, float, FFT, wavelet, UHF antenna, transformer

I. INTRODUCTION High voltage equipment are to be subjected to partial discharge PD test in accordance with the respective standard before being sent to site. The test equipment used for laboratory test as per the standard is not suitable for site, due to the presence of strong electromagnetic radiation in the neighborhood. Since any equipment shifted to site undergoes vibration during transport, it is likely that either a portion of insulation would have been affected or inter-gaps between live parts would have altered. In order to ascertain the reliability of operation, it is important that proper diagnostic be carried out at site. Thus, a suitable diagnostic technique is required to be evolved. The latest method has now been used by several researchers which fall in the category of partial discharge diagnostic (PDD) using acoustic or ultrasonic method. These methods are used at site for detection of PD inside transformer. However, the confidence level of proper detection including location and identification of various types of PD are rather low. The present paper deals with the diagnostic of floating type discharge in a model simulation experiment. The ultra high frequency discharge emanating from the partial discharge is detected by suitable UHF antenna and amplified to obtain measurable pulse. The recorded pulse is analyzed for its dominant frequency. The time ~ magnitude characteristic of pulse for every dominant frequency is calculated using wavelet transform. In order to ascertain the accuracy of computation the pulse is reconstructed. The ratio of the peak magnitude of each pulse for every frequency is calculated to obtain a relationship for similar comparison with other floating type of discharge for future use. However, the present

Fig. 1.a. Test tank with antenna and simulator

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Proc. of Int. Conf. on Control, Communication and Power Engineering 2010

223, 338, 415) MHz. A similar result and analysis for wedge type discharge has been reported by Sudarshanam et al. [1].

Fig. 1.b. Discharge simulator Assembly

Fig. 2.a. UHF signal from floating type of PD by big antenna

Fig. 1.c. UHF Dual arm spiral antenna

Fig. 2.b. UHF signal from floating type of PD by small antenna

Fig. 1.d. UHF Signal measurement systems

III. EXPERIMENTAL RESULTS The acoustic signal from PD is detected using UHF frequency detector and amplified before recording on a storage oscilloscope. One typical signal recorded by an UHF detector of big and small is given in Fig. 2.a. and Fig. 2.b. respectively. The total time of recording is limited to one micro second with 1000 samples. It is found that duration of 102.4 Âľsec is adequate to represent signal up to 500 MHz. The frequency response obtained from PD signal of Fig. 2.a. is given in Fig. 3[2]. The dominant frequencies seen from the FFT analysis are (7, 14, 26, 37, 42, 54,151,

Fig. 3. FFT of the UHF signal corresponding to Fig. 2.a.

235 Š 2009 ACEEE

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010


of the above hypothesis. Finally, all the component signals have been added with reference to time to obtain the reconstructed signal. The reconstructed signal is shown in Fig. 5. It can also be stated that the wavelet transform can be used for eliminating any signal if it is considered noise. The component signals for each of dominant frequencies have been calculated and are shown in Fig. 4. (a) to (d). Some of the signals have not been shown due to space constraint. From Fig. 3, which is the FFT of main signal, it is observed that there are several lower frequencies which are not distinctly clear. However, they do exist. They also contribute to the main signal. It is rather complex to determine if the related frequency is due to a true signal or caused due to windowing [4, 8]. In this process, there is every likelihood that a true signal can be ignored with the assumption that they are noise. On the other hand, a noise due to truncation signal may have been considered as true signal. Thus, a difference exists between original signal and reconstructed signal.

In order to analyze the signal using wavelet the following mother wavelet integral and wavelet equation has been chosen [3]. ∞ W(b,a) = 1/√a ∫ I(t) Ψ((t-b)/a) dt (1) -∞ a = 1/2πf (2) Where, a = scale parameter b = translation parameter f = frequency In the present study, the Gabor wavelet equation has been used and is given by the following equation Ψ(t) = exp(-t2/σ2) * Cos (t)


where, σ2 is a constant and controls the band of frequencies to be identified. The necessary and sufficient condition for a function to become wavelet is discussed in detail by Rao et al.[4]. Using the wavelet function defined in equation (1) and (2), the time ~ magnitude characteristic of the signal for all dominant frequencies are calculated for most appropriate values of signals. In order to evaluate the time varying current waveform at a particular frequency, it is essential to determine the multiplication factor (K). This factor converts the wavelet transform of the time waveform at a particular scale parameter ‘a’ into the transient current waveform for the corresponding frequency. For every dominant frequency there exists a signal in time domain. All signals are plotted and given from Fig.4 (a) to (d). The signals corresponding to each frequency are multiplied by a scale factor ‘K’ to obtain the signal with true magnitude. The reconstructed signal using the individual signal is shown in Fig. 5. A comparison between original signal and reconstructed signal suggests the extent of accuracy of calculation [5].


V. RESULTS AND DISCUSSION Frequency spectrum characteristic of float type discharge depicted in Fig. 3. shows a wide variation in spectrum. The area under each frequency represent the strength of the signal in terms of magnitude and duration. A dominant frequency[6,7] may suggest a high magnitude with short duration or a low magnitude with long duration existence. However, the response aids in defining the presence of a particular signal. It is this characteristic that is utilized by wavelet transform to identify a particular type of signal. Typically from Fig. 4 (a) to (d) show the time ~ magnitude value for all dominant frequencies. It is seen that higher contribution comes either from a few low frequency (7, 14 MHz) components or a few very high frequency (338,415 MHz) components. The remaining frequencies have very low contribution in the main signal. Such characteristic can be utilized for identifying the range of frequencies which contribute to a given type of discharge. A large databank of such measurement can be used to confirm the validity

(b) Fig.4. Component signal for various frequencies (a) 7 MHz (b) 14 MHz

236 © 2009 ACEEE

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010

CONCLUSIONS The paper deals with the analysis of PD signals from a float type of discharge. The UHF signal recorded in time domain is analyzed using FFT program. For every dominant frequency the time domain contribution is calculated using Gabor wavelet. All signals are added to obtain original PD signals. The wavelet method will be useful in eliminating noise. If large number of floating type UHF signals are available, it is possible to obtain an empirical relation to identify this type of discharge with internal PD, surface PD or corona. ACKNOWLEDGMENT The authors are thankful to BHEL R&D management for allowing to carry out the experimental work. The authors are also thankful to shri R.N.Parmar, S.Rengarajan, and A.Bhoomaiah of BHEL R & D for valuable discussions. The authors are thankful to Tirumala & TKR Engineering Colleges for permission to publish the work.



[2] [3] [4] (d) Fig. 4. Component signal for various frequencies (c) 54 MHz, (d) 338MHz





Fig. 5. Reconstructed signal of float type of PD.

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T. Sudarshanam, L. Nirmaladevi, and B.P. Singh, “Wavelet analysis of ultra high frequency signal generated due to partial discharge in transformer,” in Proc. Int. Conf. RACE-08, Paper No. R-166, Hyderabad, India, Dec. 2023, 2008. John G. Proakis and Dimitris G. Manolakis, Digital Signal Processing. New Delhi: Pearson Education, 2007. Robi Polikar, “The Wavelet Tutorial Part-I, II and III Multiresolution Analysis, the Continuous Wavelet Transform,” Second Edition, Ames, Iowa, 1996. M. Rajeshwara Rao, and B.P. Singh, “Using wavelet for the detection and localization of interturn fault in the high voltage winding of a power transformer,” IEEE Trans. Dielectrics and Electrical Insulation, vol. 8, no. 4, pp. 652 – 657, August 2001. P. Krishna Murthy, J. Amarnath, and B.P. Singh, “Reconstruction of HVDC converter Transformer neutral current using Gabor wavelet,” IEEMA Journal, pp 118121, June 2009. Shaik Riaz Babu, B.V. Sanker Ram, T. Sudarshanam, and B.P. Singh, “Fault detection in a generator transformer using coherence function,” in Proc. Eighth Int. Conf. on Transformers, Trafotech 2010, Session IV-paper 6, Mumbai, India, Jan. 18-19, 2010. A. Bhoomaiah et al., “Experimental detection and localization of fault in the winding of 220KV generator transformer using Gabor wavelet,” IEEMA Journal, pp 6869, July 2006. Pradeep M. Nirgude et al., “Investigation on detection of winding movements in transformer by frequency response analysis,” 14th ISH, Beijing, Aug. 2005.

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