A RSSI-AOA-Based UHF Partial Discharge Localization Method Using MUSIC Algorithm
Quanfu Zheng, Lingen Luo, Hui Song, Gehao Sheng, Xiuchen Jiang
Abstract
To monitor the insulation deterioration of power equipment and realize prompt fault warning systems in air-insulated substations, in this study, we propose a multiple signal classification (MUSIC) algorithm-based partial discharge (PD) localization method with an angle of arrival (AOA) and ultrahigh frequency (UHF)-received signal strength indicator (RSSI). Compared with traditional UHF time-difference-based techniques, this RSSI-based AOA localization method is a more economical solution. In addition, by comparing the measured RSSI vector to a prebuilt reliable reference data set, the MUSIC method can effectively locate the direction of the PD source with high accuracy. Compared with the method that directly determines the smallest RSSI values by several sensors, this method can accomplish localization by fewer sensors without impeding accuracy. Furthermore, the interpolation method was adopted to improve the precision of the relationship curve of AOA/RSSI, which it did with a limited number of sensors. Laboratory tests were conducted to verify the accuracy of the proposed method, and most of the localization errors were less than 1°, which indicates its potential application in the prompt identification of faults regarding the insulation deterioration of power equipment in substations.