Izvorni znanstveni članak

https://doi.org/10.17559/TV-20210703124323

Fault Detection of Smart Electricity Meters Based on 1D Convolution Twin Network

Hao Xue ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China
Yiran Liu ; China Academy of Railway Sciences Corporation Limited, 2 Daliushu Road, Haidian District, Beijing, China
Linkun Zhou* ; Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Haidian District, Beijing, China

Sažetak

Timely detection and maintenance of smart electricity meter faults are essential for smart grid systems, but there is no high-accurate algorithm to detect the meter fault yet. So, in this paper, we propose a deep learning algorithm to detect the fault of the smart electricity meter. Our algorithm is based on a 1D convolution twin network, which can distinguish the meter data of different fault types with high precision. To realize the fault detection task, we design a twin classifier for counting the number of matches between the data to be predicted and each type of known data and select the type with the most counts as the predicted type. Our algorithm automatically detects the fault of the smart electricity meter while its accuracy reaches 94.52%, which can significantly improve the maintenance efficiency of the fault detection.

Ključne riječi

fault detection; smart electricity meter; 1D convolution twin network

Hrčak ID:

269498

URI

https://hrcak.srce.hr/269498

Datum izdavanja:

15.2.2022.

Posjeta: 1.318 *