An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform

This paper proposes an algorithm that uses wavelet level adaptive decision-making for detecting high-voltage direct current (HVDC) discharge in wavelet transform cognitively. The identification and detection of HVDC discharge is an essential area of investigation, which contributes to ensuring pipeline safety and the optimal operation of an electrical power system. The proposed algorithm overcomes the wavelet packet transform’s disadvantage of needing to determine the level in advance. The decomposition level of wavelet packet transform is controlled by calculating relative wavelet energy change to decide its wavelet level. Our proposal extracts richer features of HVDC discharge by comparing other feature extraction algorithms. To select the best-suited mother wavelet function, we also design a selection method based on quantitative and qualitative approaches. An additional objective of this study is to detect the phenomenon of HVDC discharge using CP time-series data to assess the corrosion of energy pipelines. Moreover, a third primary discovery is that a wavelet-based application framework is designed to detect the HVDC discharge and further protect the energy pipeline. These discoveries can be valuably applied to the protection of power systems. They also provide brighter perspectives on future opportunities to expand on studies-to-date on the detection and classification of time-series data.