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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| Published in: | IEEE transactions on industry applications pp. 1 - 16 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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2025
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| ISSN: | 0093-9994, 1939-9367 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Freitas, Luiz Carlos Gomes Resende, Enio Costa Bernardes, Wellington Maycon Santos Passatuto, Luiz Arthur Tarralo de Souza, Arthur Gomes |
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| Snippet | Short-Duration Voltage Variations (SDVVs) are phenomena that significantly impact power quality. Although they typically last no longer than three minutes,... |
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| SubjectTerms | Artificial neural networks Distribution networks MATLAB OpenDSS phenomenon responsibility Power quality Python QGIS short-duration voltage variation Software packages Transformers Voltage control Voltage fluctuations |
| Title | Attribution of Responsibility for Short-Duration Voltage Variations in Power Distribution Systems via QGIS, OpenDSS, and Python Language |
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