Modeling and on-line recognition of PD signal buried in excessive noise

Abstract

The problem of on-line recognition and retrieval of relatively weak industrial signal 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, overlapping broadband frequency spectrum of PI and PD pulses. Therefore, on-line, on-site, PD measurement is hardly possible in conventional frequency-based DSP techniques. We provide new methods to detect, estimate and classify the PD signal. The observed PD signal is modeled as linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled using both parametric and non-parametric methods. A Gaussian model is incorporated in parametric modeling and smooth FIR filter method is used in non-parametric modeling and the parameters of the models are estimated using maximum-likelihood (ML) estimation technique. The methods proposed by the authors are able to recognize and retrieve the PD pulses, completely automatic without any user interference.