Open AccessIntelligent learning approach for UHF partial discharge localisation in air‐insulated substations
Author(s) -
Zheng Quanfu,
Luo Lingen,
Song Hui,
Sheng Gehao,
Jiang Xiuchen
Publication year - 2020
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve.2019.0342
Subject(s) - partial discharge , ultra high frequency , kalman filter , signal (programming language) , electronic engineering , attenuation , particle filter , engineering , transmission (telecommunications) , parametric statistics , computer science , filter (signal processing) , electrical engineering , voltage , artificial intelligence , mathematics , statistics , physics , optics , programming language
To achieve comprehensive insulation deterioration motoring of power equipment and early fault warning in air‐insulated substations, a data‐driven partial discharge (PD) source localisation method employing noisy ultra‐high frequency (UHF) received signal strength indicator (RSSI) and particle filter is proposed in this study. Compared with the existing UHF time‐difference‐based techniques, UHF wireless sensor arrays and RSSI‐based methods provide an economical and high‐adaptability solution. However, owing to the multi‐pathing and shadowing effects, UHF signal attenuation cannot be modelled. Therefore, a Kalman filter was employed to smoothen the RSSI signal. Furthermore, a semi‐parametric regression model is proposed to achieve a more accurate relationship between the RSSI and the transmission distance. Finally, in contrast to traditional localisation algorithms directly based on the RSSI ranging model, a particle filter was used to achieve higher accuracy. It predicted the best distribution of the position of PD by learning and considering all the system states of the previous moment. The laboratory test was performed within an area of 6 m × 6 m, and the results demonstrate that the mean PD source localisation error was 1.16 m, which gives a potential application for the identification of power equipment with insulation deterioration in a substation, while the accuracy is still needed to be verified further by field tests.