Denoising and feature extraction in PD based cable condition monitoring systems

There has been increasing application of on-line partial discharge (PD) based cable insulation condition monitoring among utilities worldwide due to the ability of on-line PD monitoring to allow incipient insulation faults to be detected and aged cable replacement program to be prioritised. However the application is also accompanied with a number of challenges. Data from on-line PD monitoring systems shows presence of higher levels of interference, including sinusoidal RF noise, switching pulses, PD from local plant, radio and power line carrier communication systems, etc. The biggest challenge associated with on-line cable PD monitoring is to distinguish PD generated in cable insulation from noisy raw data, which requires not only application of data denoising techniques but also feature extraction techniques to differentiate signals coming from different sources based on their characteristics. This paper aims to overcome the above-mentioned challenge. Following a brief introduction the paper introduces an effective denoising technique involving the adaptive second generation wavelet transform (ASGWT). To describe the various PD pulses, which the authors have observed from on-line cable PD monitoring systems, methods for PD feature extraction are discussed. These include analysis of raw PD signal, phase-resolved PD pattern, etc. Finally, based on data denoising and feature extraction, signal classification for an on-site PD testing experiment is introduced.