Shetty, Pradeep Kumar and Srikanth, R and Ramu, TS (2004) A PPCA based non-parametric modeling and retrieval of PD signal buried in excessive noise. In: 2004. CEIDP '04. 2004 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, 17-20 Oct. 2004.
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The problem of on-line recognition and retrieval of relatively weak industrial signals such as partial discharges (PD), buried in excessive noise, has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) due to the overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, onsite, PD measurement is hardly possible in conventional frequency based DSP techniques. The observed PD signal is modeled as a linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and the pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled instituting non-parametric methods, based on smooth FIR filters, and a maximum aposteriori probability (MAP) procedure employed therein, to estimate the filter coefficients. The classification of the pulses is undertaken using a simple PCA classifier. The methods proposed by the authors were found to be effective in automatic retrieval of PD pulses completely rejecting PI.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering
Division of Electrical Sciences > High Voltage Engineering (merged with EE)
|Date Deposited:||29 Dec 2011 04:53|
|Last Modified:||29 Dec 2011 04:53|
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