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

Michele Zanoni, Riccardo Chiumeo, Liliana Tenti and Massimo Volta
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Michele Zanoni: Ricerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, Italy
Riccardo Chiumeo: Ricerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, Italy
Liliana Tenti: Ricerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, Italy
Massimo Volta: Ricerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, Italy

Energies, 2021, vol. 14, issue 23, 1-25

Abstract: 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.

Keywords: power quality; voltage dips; distributed monitoring system; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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