Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis
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 autom...
| Hoofdauteurs: | , , , |
|---|---|
| Formaat: | Artikel |
| Taal: | English |
| Gepubliceerd in: |
MDPI AG
2021-11-01
|
| Reeks: | Energies |
| Onderwerpen: | |
| Online toegang: | https://www.mdpi.com/1996-1073/14/23/7949 |
| _version_ | 1827675037344202752 |
|---|---|
| author | Michele Zanoni Riccardo Chiumeo Liliana Tenti Massimo Volta |
| author_facet | Michele Zanoni Riccardo Chiumeo Liliana Tenti Massimo Volta |
| author_sort | Michele Zanoni |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:54:38Z |
| format | Article |
| id | doaj.art-bbcfe89d27d54c15b9ee727a7f1728d8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:54:38Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-bbcfe89d27d54c15b9ee727a7f1728d82023-11-23T02:20:20ZengMDPI AGEnergies1996-10732021-11-011423794910.3390/en14237949Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity AnalysisMichele Zanoni0Riccardo Chiumeo1Liliana Tenti2Massimo Volta3Ricerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, ItalyRicerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, ItalyRicerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, ItalyRicerca sul Sistema Energetico-RSE S.p.A., Via Rubattino 54, 20134 Milan, ItalyThis 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.https://www.mdpi.com/1996-1073/14/23/7949power qualityvoltage dipsdistributed monitoring systemmachine learningdeep learning |
| spellingShingle | Michele Zanoni Riccardo Chiumeo Liliana Tenti Massimo Volta Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis Energies power quality voltage dips distributed monitoring system machine learning deep learning |
| title | Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis |
| title_full | Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis |
| title_fullStr | Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis |
| title_full_unstemmed | Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis |
| title_short | Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis |
| title_sort | advanced machine learning functionalities in the medium voltage distributed monitoring system queen a macro regional voltage dips severity analysis |
| topic | power quality voltage dips distributed monitoring system machine learning deep learning |
| url | https://www.mdpi.com/1996-1073/14/23/7949 |
| work_keys_str_mv | AT michelezanoni advancedmachinelearningfunctionalitiesinthemediumvoltagedistributedmonitoringsystemqueenamacroregionalvoltagedipsseverityanalysis AT riccardochiumeo advancedmachinelearningfunctionalitiesinthemediumvoltagedistributedmonitoringsystemqueenamacroregionalvoltagedipsseverityanalysis AT lilianatenti advancedmachinelearningfunctionalitiesinthemediumvoltagedistributedmonitoringsystemqueenamacroregionalvoltagedipsseverityanalysis AT massimovolta advancedmachinelearningfunctionalitiesinthemediumvoltagedistributedmonitoringsystemqueenamacroregionalvoltagedipsseverityanalysis |