Author:
Zanoni M., ,Chiumeo R.,Tenti L.,Volta M.
Abstract
This paper presents the development of an automated tool called QuEEN PyService, aimed to the extraction of events voltage signals from the QuEEN distribution network monitoring system database, for advanced Power Quality analysis. The application has allowed the integration of the DELFI classifier (DEep Learning for False voltage dips Identification), recently developed by RSE, making it possible for the first time the intensive validation of the latter on a large number of voltage dips. Thanks to this tool, a comparison between the performance of DELFI and those of an older criterion based on the 2nd voltage harmonic measurement has been performed using data recorded by 61 measurement units in the period 2015-2020 The analysis has been focused on traditional PQ voltage dips counting indices as N2a e N3b. Results show that the usage of the DELFI classifier increases the N2a and the N3b by respectively the 20.6 % and 38.8% with respect to the QuEEN criterion.
Publisher
AEDERMACP (European Association for the Development of Renewable Energies and Power Quality)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献