FExWaveS application for voltage dips origin assessment: optimization of the tool in views of its integration into the QuEEN monitoring system

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.

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Implementation of a New Method for an Improved Voltage Dips Evaluation by the Italian Power Quality Monitoring System in Presence of VT Saturation Effects

Renewable Energy and Power Quality Journal ◽

10.24084/repqj08.383 ◽

2010 ◽

Vol 1 (08) ◽

pp. 519-522 ◽

Cited By ~ 2

Author(s):

R. Chiumeo ◽

M. de Nigris ◽

L. Garbero ◽

C. Gandolfi ◽

L. Tenti ◽

...

Keyword(s):

Power Quality ◽

Monitoring System ◽

New Method ◽

Quality Monitoring ◽

Power Quality Monitoring ◽

Voltage Dips ◽

Saturation Effects

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Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis

Energies ◽

10.3390/en14237949 ◽

2021 ◽

Vol 14 (23) ◽

pp. 7949

Author(s):

Michele Zanoni ◽

Riccardo Chiumeo ◽

Liliana Tenti ◽

Massimo Volta

Keyword(s):

Machine Learning ◽

Monitoring System ◽

Software Tool ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Distributed Monitoring ◽

Residual Voltage ◽

Medium Voltage ◽

Voltage Dips

This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its “validity” (invalidating any false events caused by voltage transformers saturation) and its “origin”(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas.

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The Italian power quality monitoring system of the MV network results of the measurements of voltage dips after 3 years campaign

10.1049/cp.2009.0952 ◽

2009 ◽

Cited By ~ 10

Author(s):

R. Chiumeo ◽

A. Porrino ◽

L. Garbero ◽

L. Tenti ◽

M. de Nigris

Keyword(s):

Power Quality ◽

Monitoring System ◽

Quality Monitoring ◽

Power Quality Monitoring ◽

Voltage Dips

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Monitoring des Arzneimittelmissbrauchs 2006 bei Klienten von Suchtberatungsstellen (PHAR-MON)

SUCHT - Zeitschrift für Wissenschaft und Praxis / Journal of Addiction Research and Practice ◽

10.1024/2007.07.05 ◽

2007 ◽

Vol 53 (7) ◽

pp. 65-77 ◽

Cited By ~ 1

Author(s):

Susanne Roesner ◽

Heinrich Küfner

Keyword(s):

Monitoring System

Hintergrund und Zielsetzung: PHAR-MON ist ein Monitoring-System, das die auf dem deutschen Markt befindlichen Arzneimittel in ihrer Bedeutung für die Entwicklung von Missbrauch und Abhängigkeit in Suchtberatungsstellen überwacht. Methodik: Klienten ambulanter Beratungsstellen werden im Rahmen der Standarddokumentation zu ihrem Arzneimittelkonsum befragt und Fälle eines abhängigen Konsums, eines schädlichen Gebrauchs oder eines Missbrauchs in PHAR-MON dokumentiert. Ergebnisse: Im Jahr 2006 wurden insgesamt 448 Meldungen von 276 überwiegend alkohol- und drogenabhängigen Klienten in das Monitoring einbezogen. Tranquilizer vom Benzodiazepin-Typ wurden in allen Klientengruppen mit Anteilen zwischen 29,1 % und 35,3 % am häufigsten dokumentiert. An benzodiazepinabhängige Klienten werden zunehmend auch Nicht-Benzodiazepin-Hypnotika verordnet. Bei opioidabhängigen Klienten war im Zeitraum der letzten fünf Jahre ein Anstieg im missbräuchlichen Substitutionsmittelkonsum von 14,9 % auf 33,8 % zu verzeichnen. Schlussfolgerungen: Das Risiko gefährlicher Wechselwirkungen zwischen Arzneimitteln mit Alkohol und Drogen sollte stärker als bisher in die ärztliche Verordnungsentscheidung einbezogen werden.

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