Series and Parallel Arc Fault Detection with Dual Sensors Under Simultaneous Power Quality Disturbances Based on S-Transform 核心 · 已核验

doi:10.21227/nef4-9f98

"Arc faults are electrical faults cause fires which have the potential to threaten human safety and major losses. In the electric power network, arc faults occur in two main forms of series and parallel which each exhibiting distinct characteristics. To capture the electrical behavior of both types, hybrid sensing is employed using a combination of current and voltage sensors. The Arc faults may arise simultaneously with power quality (PQ) disturbances in the power grid, making detection more challenging due to the resulting non-stationary signal. To address this issue, this paper proposes Stockwell transform (ST) for accurate detection of series and parallel arc fault under PQ disturbances. ST is utilized to extract time-frequency features from the series and parallel arc fault signals. Both simulation and a low-cost experimental setup are developed to collect current and voltage signals data. Convolutional neural network (CNN) is proposed for classify of arc fault under PQ disturbances using the time-frequency features obtained from ST as input. Eleven types of disturbances are selected to evaluate the performance of the proposed methods. The results demonstrate that the proposed methods achieve reliable and accurate results in detection of series and parallel arc fault under simultaneous PQ disturbances"

落地页

https://ieee-dataport.org/documents/series-and-parallel-arc-fault-detection-dual-sensors-under-simultaneous-power-quality

许可证

CC-BY-4.0 (判读置信:inferred)

国内可访问性

国内直连:可达 (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

PHM 任务

fault_detection

传感器