A Machine Learning Based Tool for Voltage Dip Classification
Paper number
985Conference name
CIRED 2019Conference date
3-6 June 2019Conference location
Madrid, SpainPeer-reviewed
YesMetadata
Show full item recordAuthors
Shadmehr, Houriyeh, Ricerca sul Sistema Energetico RSE, ItalyChiumeo, Riccardo, RSE spa, Italy
Tenti, Liliana, Ricerca sul Sistema Energetico RSE, Italy
Abstract
A Machine Learning based tool is presented in order to make voltage dips (VD) ex-post analysis more automatic and effortless. The tool takes as input the full waveforms associated to voltage dips occurring in the Italian MV networks and recorded by QuEEN monitoring system implemented by RSE. The first tool has been developed to classify events on the base of their HV/MV origin since the utilities will be responsible only for the events due to faults occurred in their networks; it uses the self-tuning Kalman Filter and Support Vector Machine (SVM) for extracting the VD’s features and classifying the events, respectively.Instead, the second tool, based on end-to-end Deep Learning techniques, has been developed to distinguish between “true” and “false” VD; it utilizes a Convolutional Neural Network (CNN) whose first layers undertake the task of the features extraction while the last layers carry out the events classification.Publisher
AIMDate
2019-06-03Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/235http://dx.doi.org/10.34890/456