Mathematical Model and Regression Analysis of Acoustic Emission Signals Generated by Partial Discharges
Applied and Computational Mathematics
Volume 3, Issue 5, October 2014, Pages: 225-230
Received: Sep. 10, 2014;
Accepted: Sep. 19, 2014;
Published: Sep. 30, 2014
Views 2542 Downloads 191
Daria Wotzka, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, Poland
An improved mathematical model describing acoustic emission (AE) signals generated by different types of partial discharges (PD) that occur in electric power transformer insulation system is presented in the paper. AE signals are analyzed within the AE method as applied for power transformer failure detection due to occurrence of PD. There are several types of basic defects, which are characterized by different types of PD. The mathematical model presented here is crucial for numerical analyses and simulations, where it acts as the function describing the acoustic source in an acoustic model of power transformer insulation system. The regression procedure was performed based on empirical AE signals, registered in a laboratory experiment. The AE signals are described by a mathematical model being a multi-parameter function, which involve both the time domain and the frequency domain. Goodness of the model was evaluated based on analysis of 480 data samples in the time, frequency and time-frequency domains. Also coherence between the registered and modeled signals was calculated. It was stated that the improved model fits very well to the real data, although, due to high level of noise embodied in signals registered in experiments, the coherence values remain low. Moreover, analyses of the estimated data were performed and some example results are presented in this paper. Based on the achieved outcomes a collection of parameter values was prepared for each of the eight considered PD basic types. One can simple use it now in a numerical model for simulation of AE signal source generated by specified type of PD, what corresponds to a particular power transformer insulation system failure. Furthermore, the regression procedure presented in this paper can be easily transferred to any other types of AE sources including processes of compression, tension and cracking.
Mathematical Model and Regression Analysis of Acoustic Emission Signals Generated by Partial Discharges, Applied and Computational Mathematics.
Vol. 3, No. 5,
2014, pp. 225-230.
S. Borucki, “Diagnosis of technical condition of power transformers based on the analysis of vibroacoustic signals measured in transient operating conditions”, IEEE Trans. Pow. Del. Vol. 27, pp. 2012, 670–676. DOI: 10.11000009/TPWRD.2012.2185955
L. E. Lundgaard, “Partial discharge - part XIV: Acoustic PD detection – practical application”. IEEE El. Insul. Mag., Vol. 8, No. 5, 1992, pp. 34–43.DOI: 10.1109/57.156943
P. Frącz, “Influence estimation of the voltage value on the measurement results for the optical radiation generated by partial discharges on bushing isolator”, Acta Phys. Pol. A, Vol. 120, 2011, pp. 604-607,
A. Cichoń, P. Frącz, D. Zmarzły, “Characteristic of acoustic signals generated by operation of on load tap changers”, Acta Phys. Pol. A, Vol. 120, 2011, pp. 585-589.
J. Gubio-Serrano, M.V. Rojas-Moreno, J. Posada, J.M. Martinez-Tarifa, G. Robles, J.A. Garcia-Souto, „Electro-acoustic detection, identification and location of PD sources in oil-paper insulation systems”, IEEE trans. Dielectr. Electr. Insul., Vol. 19, No. 5, 2012, pp. 1569-1578, DOI: 10.1109/TDEI.2012.6311502
M. Pompili, R. Bartnikas, “On PD measurement in dielectric liquids”, IEEE Trans. Dielectr. Electr. Insul, Vol. 19, No. 5, 2012, pp. 1476-1481. DOI: 10.1109/TDEI.2012.6311489
M. Shibata, “A theoretical evaluation of AE signals - the rise time effect of dynamic forces”. Mat. Eval., Vol. 42, No. 1, 1984, pp. 107–115.
L. Bolin, “A model for estimating the signal from an acoustic emission source”. Ultrason., Vol. 17, No. 2, 1979, pp. 67–70.
T. Boczar, D. Zmarzły, “The application of correlation analysis to acoustic emission pulses generated by partial discharges”, Mat. Eval., Vol. 62, No. 9, 2004, pp. 935-942.
T. Boczar, D. Zmarzły, “Multiresolution analysis of the AE pulses generated by PD”, INSIGHT, Vol. 45, No. 7, 2003, pp. 488-492.
T. Boczar, A. Cichoń, S. Borucki “Diagnostic expert system of transformer insulation systems using the acoustic emission method”, IEEE Trans. Dielectr. Electr. Insul., Vol. 21, No. 2, 2014, pp. 854–865. DOI 10.1109/TDEI.2013.004126
S. Rudd, S.D.J. Mcarthur, M.D. Judd, “A generic knowledge-based approach to the analysis of PD data”, IEEE Trans. Dielectr. Electr. Insul., Vol. 17, No. 1, 2010, pp. 149 – 156. DOI: 10.1109/TDEI.2010.5412013
K.X. Lai, B.T. Phung, T.R. Blackburn, “Application of data mining on PD part I: predictive modelling classification”, IEEE Trans. Dielectr. Electr. Insul., Vol. 17, No. 3, 2010, pp. 846–854. DOI: 10.1109/TDEI.2010.5492258
T. Pinpart, M.D. Judd, “Differentiating between PD sources using envelope comparison of ultra-high-frequency signals”, IET Science, Measurement & Technology, Vol. 4 , No. 5, 2010, pp. 256–267. DOI: 10.1049/IET-SMT.2009.0064
A. Cichoń, S. Borucki, D. Wotzka, “Modeling of acoustic emission signals generated in on load tap changer” Acta Phys. Pol. A, Vol. 125, No. 6, 2014, pp.1396-1399. DOI: 10.12693/APhyspolA.125.1396
D. Wotzka, A. Cichoń, T. Boczar, “Modeling and experimental verification of ultrasound transmission in electro insulation oil”, Arch. Acoust., Vol. 37, No. 1, 2012, pp. 19-22, DOI: 10.2478/v10168-012-0003-x.