A Machine Learning Based Tool for Voltage Dip Classification

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Paper number
985
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Shadmehr, Houriyeh, Ricerca sul Sistema Energetico RSE, Italy
Chiumeo, 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.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/235
http://dx.doi.org/10.34890/456
ISSN
2032-9644
ISBN
978-2-9602415-0-1