@ARTICLE{ iet:/content/journals/10.1049/joe.2018.0249, author = {Marcello Mastroleo}, author = {Roberto Ugolotti}, author = {Luca Mussi}, author = {Emilio Vicari}, author = {Federico Sassi}, author = {Francesco Sciocchetti}, author = {Bob Beasant}, author = {Colin McIlroy}, keywords = {automatic faulty LV network asset analysis;heat pumps;damaged network fast recovery;power system;data analysis;deep neural network;automatic failure source identification;electric vehicles;recording devices;distribution network operators;low-voltage cables;electrical distribution network;variational autoencoder;cable fault probabilty;VAE;}, language = {English}, abstract = {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.}, title = {Automatic analysis of faulty low voltage network asset using deep neural networks}, journal = {The Journal of Engineering}, issue = {15}, volume = {2018}, year = {2018}, month = {October}, pages = {851-855(4)}, publisher ={Institution of Engineering and Technology}, copyright = {This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)}, url = {https://digital-library.theiet.org/;jsessionid=1ml0h6r0dfb4k.x-iet-live-01content/journals/10.1049/joe.2018.0249} }