Intelligent learning approach for UHF partial discharge localisation in air-insulated substations

Autor: Quanfu Zheng, Lingen Luo, Hui Song, Gehao Sheng, Xiuchen Jiang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
kalman filters
regression analysis
partial discharge measurement
particle filtering (numerical methods)
wireless sensor networks
learning (artificial intelligence)
gas insulated substations
air insulation
uhf measurement
uhf detectors
computerised instrumentation
intelligent learning approach
uhf partial discharge localisation
air-insulated substations
power equipment
early fault warning
data-driven partial discharge source localisation method
particle filter
wireless sensor arrays
rssi-based methods
economical adaptability solution
shadowing effects
uhf signal attenuation
kalman filter
rssi signal
semiparametric regression model
rssi ranging model
mean pd source localisation error
insulation deterioration motoring
ultrahigh frequency received signal strength indicator
uhf time-difference-based techniques
uhf received signal strength indicator
size 1.16 m
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Electricity
QC501-721
Zdroj: High Voltage (2020)
Druh dokumentu: article
ISSN: 2397-7264
DOI: 10.1049/hve.2019.0342
Popis: 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.
Databáze: Directory of Open Access Journals
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