"Series and Parallel Arc Fault Detection with Dual Sensors Under Simultaneous Power Quality Disturbances Based on S-Transform"

"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"

电弧故障（Arc faults）是引发火灾的电气故障，其可能威胁人身安全并造成重大财产损失。在电力网络中，电弧故障主要分为串联型与并联型两种形式，二者各自具备独特的电气特性。为捕捉这两类故障的电气行为，研究采用混合传感方案，结合电流传感器与电压传感器进行数据采集。电弧故障可能与电网中的电能质量（Power Quality, PQ）扰动同时发生，由此产生的非平稳信号会增加故障检测的难度。为解决这一问题，本文提出采用斯托克尔变换（Stockwell transform, ST）实现在电能质量扰动场景下对串联型与并联型电弧故障的精准检测。利用斯托克尔变换从串联、并联电弧故障信号中提取时频特征。本文同时搭建了仿真实验与低成本实验平台，采集电流与电压信号数据集。提出以斯托克尔变换提取的时频特征作为输入，采用卷积神经网络（Convolutional Neural Network, CNN）对电能质量扰动下的电弧故障进行分类。选取11类扰动场景以评估所提方法的性能。实验结果表明，所提方法在同时存在电能质量扰动的场景下，能够实现串联型与并联型电弧故障的可靠、精准检测。