A real-time processing system for denoising of partial discharge signals using the wavelet transform

The wavelet transform (WT) has been proved to be an efficient tool for partial discharge (PD) processing due its capability to stand out inhomogeneous and localized signal features. In previous work, some authors have investigated issues related to the ways to choose a mother wavelet, strategies to denoise PDs and its in field usability. This paper presents a realtime system for partial discharge signal denoising and processing using the discrete wavelet transform (DWT). Real-time processing is an important feature for PD analysis since it allows data acquisition for long periods, leading to a better statistical characterization. Moreover, real-time processing presents benefits for the investigation of new diagnostic procedures and the development of more specific PD applications, like on-line monitoring. The developed system can be used as a preprocessing unit coupled to an existing PD analyzer or a standalone unit employed both for PD acquisition and denoising. Continuous stream processing is carried out by an appropriate border treatment both during DWT processing and PD extraction. Deterministic behavior is guaranteed by a dedicated hardware architecture associated to a real-time operating system. Hardware based optimizations allowed acquisition rates comparable to commercial PD analyzers. The results were obtained with a focus on performance evaluation in terms of computational loads, storage requirements and noise removal for several wavelet filters, decomposition levels and denoising techniques.