Methodology for Early Prediction of Single-Phase Ground Faults in Distribution Networks

The article presents the results of the development of a methodology for forecasting single-phase ground faults in medium-voltage overhead lines, based on the analysis of emergency events that occurred in the Kaliningrad region from 2018 to 2023. The statistical studies carried out made it possible to estimate the number of different types of faults in the distribution network, as well as to group all occurred cases according to their development scenarios. A correlation between weather conditions in various combinations and the probability of single-phase ground faults occurring has been identified. To predict systematic faults, it is proposed to use the identified relationship between the occurrence of a neutral point displacement and the subsequent single-phase ground fault (SPGF). Previously, this parameter of the electrical network mode was not considered for prediction of single-phase faults. To determine the level of single-phase ground fault current, it is proposed to use the relationship identified in the study between the zero-sequence current in the previous neutral displacement mode and the single-phase ground fault current. Unlike existing prediction methods in distribution networks, the proposed method is based on a comprehensive analysis of possible causes of faults. It covers both systematic faults, which can be predicted by monitoring the electrical parameters of the electrical network, and non-systematic faults caused by external factors.

Authors

Institutions

Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/uralcon62137.2024.10718899
Citations
2
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
0.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Methodology for Early Prediction of Single-Phase Ground Faults in Distribution Networks

Ilya E. Kazhekin, Egor Starostin
2 citations
Power Systems and Technologies
0.42
article

Methodology for Early Prediction of Single-Phase Ground Faults in Distribution Networks

Ilya E. Kazhekin, Egor Starostin
article en
2 citations

Abstract

The article presents the results of the development of a methodology for forecasting single-phase ground faults in medium-voltage overhead lines, based on the analysis of emergency events that occurred in the Kaliningrad region from 2018 to 2023. The statistical studies carried out made it possible to estimate the number of different types of faults in the distribution network, as well as to group all occurred cases according to their development scenarios. A correlation between weather conditions in various combinations and the probability of single-phase ground faults occurring has been identified. To predict systematic faults, it is proposed to use the identified relationship between the occurrence of a neutral point displacement and the subsequent single-phase ground fault (SPGF). Previously, this parameter of the electrical network mode was not considered for prediction of single-phase faults. To determine the level of single-phase ground fault current, it is proposed to use the relationship identified in the study between the zero-sequence current in the previous neutral displacement mode and the single-phase ground fault current. Unlike existing prediction methods in distribution networks, the proposed method is based on a comprehensive analysis of possible causes of faults. It covers both systematic faults, which can be predicted by monitoring the electrical parameters of the electrical network, and non-systematic faults caused by external factors.

Kaliningrad Law Institute Ministry of Internal Affairs of the Russian Federation (RU), Kaliningrad State Technical University (RU)
Climate action
Openalex Percentile: Top 37%
Power Systems and Technologies
0.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.