Attribution of Responsibility for Short-Duration Voltage Variations in Power Distribution Systems via QGIS, OpenDSS, and Python Language

Short-Duration Voltage Variations (SDVVs) are phenomena that significantly impact power quality. Although they typically last no longer than three minutes, such events can disrupt load operations and cause substantial production losses. This study presents an enhanced methodology for determining whe...

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Veröffentlicht in:IEEE transactions on industry applications S. 1 - 16
Hauptverfasser: de Souza, Arthur Gomes, Passatuto, Luiz Arthur Tarralo, Bernardes, Wellington Maycon Santos, Freitas, Luiz Carlos Gomes, Resende, Enio Costa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Artificial neural networks
Distribution networks
MATLAB
OpenDSS
phenomenon responsibility
Power quality
Python
QGIS
short-duration voltage variation
Software packages
Transformers
Voltage control
Voltage fluctuations
ISSN:0093-9994, 1939-9367
Online-Zugang:Volltext
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Zusammenfassung:Short-Duration Voltage Variations (SDVVs) are phenomena that significantly impact power quality. Although they typically last no longer than three minutes, such events can disrupt load operations and cause substantial production losses. This study presents an enhanced methodology for determining whether an SDVV event originates upstream or downstream of the point of common coupling between two agents interconnected through a transformer. Building upon the work of Ferreira et al., whose original approach was applied to a circuit using MATLAB/Simulink, this research advances the methodology by applying it to both real and benchmark distribution systems using open-source tools, namely QGIS, OpenDSS, and Python™. The well-known IEEE 34-Bus Test System has been used to verify the methodology's generalizability. The method was also further validated through tests conducted on two actual Brazilian distribution feeders in Uberlandia, Minas Gerais: one supplying large industrial consumers such as a rice mill and a carbonated beverages factory, and the other serving a municipal wastewater treatment plant and a large photovoltaic plant. By using real, detailed and georeferenced data, the approach ensures an accurate representation of both the network topology and the installed equipment. The results confirm that the proposed methodology reliably identifies the origin of SDVV events. A key contribution of this study is that the attribution of responsibility remains robust regardless of variations in transformer winding configurations, fault resistance, circuit topology, load characteristics, or the presence of distributed generation. These findings demonstrate the accessibility, robustness and practical applicability, offering a valuable tool for utilities and researchers aiming to enhance power quality and accountability in distribution networks.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2025.3618601