Statistical Analysis of Partial Discharges in SF6 Gas via Optical Detection in Various Spectral Ranges

Abstract

Partial discharge (PD) detection is essential to the operation of high-voltage systems. In this context, we investigate the basic characteristics of light emission during PDs in SF 6 gas from the perspective of insulation diagnosis. A synchronous system is constructed using three optical photoelectric instruments with separate wavelength responses in the ultraviolet (UV, 189-352 nm), visible (VIS, 381-675 nm), and near-infrared (NIR, 737-920 nm) spectral ranges and a wide-band PD current pulse detector with a response of 1 pC. The results indicate that light emission depends upon the type of insulation defect and discharge energy. An increase in PD charge gives rise to more components in the spectral range from UV to VIS, and the presence of an insulator surface in discharges yields a more complex VIS-to-NIR spectrum. The phase-resolved partial discharge pattern (PRPD) of UV light pulses can reasonably reflect the electroluminescence process in the presence of the insulator surface and weak corona at negative voltage points. The PRPD of VIS light describes the features of the actual PD pattern in most cases. In comparison with the other two spectral ranges, light intensity in the VIS range is more sensitive to changes in gas-pressure-normalized voltage (V rms /p). The linear fitting analysis of the relationships between the light intensity and PD charge shows that UV light detection has a greater sensitivity to the PD charge and that UV detection exhibits a greater degree of linearity. NIR detection is applicable only to severe PDs. We believe that our findings can significantly aid in application of optical PD diagnosis in SF 6 gas insulated systems.

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

“…Presently, scholars [4,7,23] suggested adopting the PRPD mode for feature extraction to abstract PD information, namely the mode of analyzing UHF PD signals by gathering statistics of the The experiment simulated the PD development by externally applying step voltage to four kinds of typical artificial insulation defects (hereafter referred to as N defect, P defect, G defect, and M defect) in GIS [22], as shown in Figure 2. The experiment simulated the PD development by externally applying step voltage to four kinds of typical artificial insulation defects (hereafter referred to as N defect, P defect, G defect, and M defect) in GIS [22], as shown in Figure 2.…”

“…Presently, scholars [4,7,23] suggested adopting the PRPD mode for feature extraction to abstract PD information, namely the mode of analyzing UHF PD signals by gathering statistics of the fluctuation distribution characteristics of the discharge pulse count n and discharge charge magnitude q with the power frequency phase angle ϕ. To guarantee the reliability of PD development law research and discharge severity evaluation, this study collected a large number of discharge waveforms in the power frequency cycle and then structured the ϕ-u-n three-dimensional distribution under four typical defects.…”

“…Presently, scholars [4,7,23] suggested adopting the PRPD mode for feature extraction to abstract PD information, namely the mode of analyzing UHF PD signals by gathering statistics of the The applied voltage shown in Table 1 increased gradually from the initial discharge stage to the discharge stage close to breakdown or at breakdown. In the test, the applied voltage process was repeated thrice.…”

“…In monitoring the condition of SF 6 gas-insulated equipment, the PD signal is the most effective evaluation information for interior insulation condition in GIS [6,7]. However, the project site requires accurate collection of PD signals and extraction of feature information that can accurately reflect the severity of insulation defects from PD signal.…”

Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges

“…These systems must integrate multiple and distributed sensors for on-line diagnosis of the different components associated with the power grid and the knowledge of their operating conditions. However, in some cases the monitoring of a grid to make predictive maintenance is a very challenging task due to the complexity of gathering and understanding all the different types of signals delivered by sensors, systems and devices associated with the size of the grid and the different nature of the signals [3,4].…”