Detection of High-Impedance Low-Current Arc Faults at Electrical Substations
Arcing faults in low voltage (LV) distribution systems associated with arc-flash risk and potentially significant equipment damage are notoriously difficult to detect under some conditions. Especially so when attempting to detect using sensing at the line, high voltage side of a substation transformer. This paper presents an analytics-based physics-aware approach to detect high-impedance, low-current arcing faults from the primary side of the substation transformer at current thresholds, below normal operating events, along with transformer inrush currents. The proposed methodology leverages the Hankel Alternative View Of Koopman Operator approach to differentiate arcing faults from standard operations, while the Series2Graph method is employed to identify the time of fault occurrence and duration. Unlike prior studies that detect such faults at the device or secondary transformer side, this work demonstrates successful fault detection at the primary side of the distribution substation transformer for faults occurring on the secondary side. The approach addresses the practical challenges of differentiating primary side expected and acceptable transients from similar magnitude LV arcing fault currents that may occur on the secondary side. The results demonstrate the efficacy of the proposed method in accurately identifying fault occurrence and duration, minimizing the risk of false positives during similar characteristic events, thus improving the reliability and operational efficiency of power distribution systems. This approach can benefit both traditional and smart power grids that employ similar transformer configurations.
Code (0)
등록된 구현이 없습니다.
Tasks
ARCFault DetectionSimilar Papers 제목 키워드 기반
Recursive-ARX for Grid-Edge Fault Detection
Future electrical grids will require new ways to identify faults as inverters are not capable of supplying large fault currents to support existing fault detection methods and because distributed resources may feed fault…
Fault DetectionEstimation of High Impedance Fault Location in Electrical Transmission Lines Using Artificial Neural Networks and R-X Impedance Graph
It is very important to ensure continuity in the process from generation of electricity to transmission to cities. The most important part of the system is energy transmission lines and distance protection relays that pr…
Enhanced Rapid Detection of High-impedance Arc Faults in Medium Voltage Electrical Distribution Networks
High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety…
ARCFault DetectionDetection of High Impedance Faults in Microgrids using Machine Learning
This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends …
feature selectionVocal Bursts Intensity PredictionFault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines
Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage undergrou…
BIG-bench Machine Learningregression