New Signal Processing-Based Methodology for Optimal Feature Selection of Corona Discharges Measurement in HVDC Systems

• Autor(a) principal: David, Gabriel Augusto [UNESP]
• Data de Publicação: 2023
• Outros Autores: Junior, Pedro Oliveira Conceicao, Dotto, Fabio Romano Lofrano, Santos, Benedito Roberto Dos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TIM.2023.3260879; http://hdl.handle.net/11449/248622
• topic: Corona discharge; high voltage direct current (HVDC); instrumentation and measurement; signal processing

This article presents a new method based on the combination of digital signal processing parameters for the selection of optimal characteristics of corona discharges in high voltage direct current (HVDC) systems, particularly for linearization of the discharge model for applications that require a simplified computational approach. The proposed method implements a new metric from the coefficient of variation (CV), CV $_{\mathbf {STFT}}$ , based on the short-time Fourier transform (STFT) and the Hinkley criterion to measure the spectral variability and determine the corona discharge profile in different situations. An experimental analysis was performed by applying voltages between ±30 and ±100 kV in a conductor, and electrical current signals proportional to the corona effect were collected through a data acquisition system. The results indicated that the application of the new method was successful in quantifying, in a simple way, the percentage of growth of corona discharges as a function of the voltage applied within the range of 40-80 kHz. Moreover, it showed 90%, 91%, 92%, 97%, 89%, 92%, and 93% of reliability in calculating the root-mean-square deviation (RMSD) based on approximation by a linear model. The frequency band resulting from this study proved to be favorable to establishing a threshold for the percentage of corona discharge growth according to its profile or condition of application, indicating this information may be useful in the construction of mobile devices with low consumption and computational performance, meeting the demands of Industry 4.0 and the Internet of Things.