Automatic analysis of faulty low voltage network asset using deep neural networks

The Journal of Engineering

Marcello Mastroleo¹  Roberto Ugolotti² 

[1] Camlin Italy , Strada Budellungo 2, Parma , Italy;Camlin Technologies , 31 Ferguson Drive, Lisburn , Northern Ireland

关键词: automatic faulty LV network asset analysis;    damaged network fast recovery;    variational autoencoder;    heat pumps;    electrical distribution network;    low-voltage cables;    cable fault probabilty;    VAE;    deep neural network;    data analysis;    recording devices;    power system;    automatic failure source identification;    electric vehicles;    distribution network operators;   

DOI  :  10.1049/joe.2018.0249

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

Electrical distribution network is constantly ageing worldwide. Therefore, probability of cable faults is increasing over time. Fast recovering of damaged networks is of vital importance and a quick and automatic identification of the failure source may help to promptly recover the functionality of the network. The scenario we are taking into consideration is a vast number of recording devices spread across a network that constantly monitor low voltage cables. When the current of a cable reaches a very high value, data is sent to a central server which analyses it through a variant of a Variational Auto Encoder (VAE), a deep neural network. This VAE has been trained by using historical data collected from several hundreds of faults recorded, but in which only a handful of them has been labelled by an on-site analysis of the fault. Data used for training is simply the recorded levels of voltages and currents, after a simple pre-processing step. The final goal is to let the network distinguish if the fault occurred in a point of the cable, on a joint, or at the pot-end located at the termination. A preliminary evaluation of its ability to generalise over the non-labelled samples shows encouraging results.

【 授权许可】

CC BY