Abstract
Partial discharge measurements taken online are severely corrupted by noise due to external disturbances. In this paper a powerful noise reduction technique, based on a wavelet packet denoising algorithm, is employed to isolate the signals from the noise. This methodology enables the denoising of partial discharges that are heavily corrupted by noise without assuming any a priori knowledge about the partial discharge features. A brief description of the wavelet packet theory as an extension of the multi-resolution analysis is given. Results of the application of this algorithm to simulated data of low signal-to-noise ratio are presented, demonstrating substantial improvement in signal recovery with minimum shape distortion. Finally, the capability of this technique is highlighted by applying it to experimental field data taken from three-phase 11 kV cables.
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