When a fault occurs in a DC system,the fault current rises rapidly with no zero-crossing point which makes fault-line selection and fault-type identification difficult.In this paper,an online detection and protection ...When a fault occurs in a DC system,the fault current rises rapidly with no zero-crossing point which makes fault-line selection and fault-type identification difficult.In this paper,an online detection and protection method based on graph theory,namely the“double D method”,is proposed for fault-line selection and fault-type identification in DC systems.In the proposed method,the entire distribution network is visualized as a“map”with vertices representing the line convergence points and edges representing the connection lines.A network topology matrix“D”is formed by detecting the current directions as the current directions are altered following a fault,whereas the current directions at the ends of non-fault lines remain the same.In order to prevent misjudgment problems arising from power flow reversal,the rates of change of the fault currents are used to further determine whether a fault has occurred and the“double D method”is introduced to identify the fault type.Simulations results with different fault types verify the effectiveness and reliability of the proposed method.展开更多
当前传统输变电线路定时巡检系统存在自动化程度低、故障识别准确率低等问题,为优化输变电线路的巡检系统,以提升其自动化水平和故障识别的精确度,文中采用了无人机低空遥感技术用于输变电线路巡检,通过无人机搭载高清摄像头获取高清图...当前传统输变电线路定时巡检系统存在自动化程度低、故障识别准确率低等问题,为优化输变电线路的巡检系统,以提升其自动化水平和故障识别的精确度,文中采用了无人机低空遥感技术用于输变电线路巡检,通过无人机搭载高清摄像头获取高清图像数据。同时,结合You Only Look Once version 5-Lite(YOLOv5-Lite)目标检测模型对图像中的故障进行实时识别,并对识别出的线路潜在故障点进行及时反馈。经实验测试,当巡检数据量为8 GB时,低空遥感技术的巡检时间仅为4.89 min。当巡检辐射频率为50 Hz时,低空遥感技术的巡检定时精度高达91.21%。此外,文中所提的目标检测模型的故障识别准确率高达92.64%。结果表明,结合无人机低空遥感技术与YOLOv5-Lite目标检测模型能够有效优化输变电线路的巡检系统,增强故障识别的准确性,为未来输变电线路巡检领域的智能化发展提供了技术支持。展开更多
对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模...对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。展开更多
基金Thanks for the financial support from the following fund projects:Project Supported by National Natural Science Foundation of China(51607070)。
文摘When a fault occurs in a DC system,the fault current rises rapidly with no zero-crossing point which makes fault-line selection and fault-type identification difficult.In this paper,an online detection and protection method based on graph theory,namely the“double D method”,is proposed for fault-line selection and fault-type identification in DC systems.In the proposed method,the entire distribution network is visualized as a“map”with vertices representing the line convergence points and edges representing the connection lines.A network topology matrix“D”is formed by detecting the current directions as the current directions are altered following a fault,whereas the current directions at the ends of non-fault lines remain the same.In order to prevent misjudgment problems arising from power flow reversal,the rates of change of the fault currents are used to further determine whether a fault has occurred and the“double D method”is introduced to identify the fault type.Simulations results with different fault types verify the effectiveness and reliability of the proposed method.
文摘当前传统输变电线路定时巡检系统存在自动化程度低、故障识别准确率低等问题,为优化输变电线路的巡检系统,以提升其自动化水平和故障识别的精确度,文中采用了无人机低空遥感技术用于输变电线路巡检,通过无人机搭载高清摄像头获取高清图像数据。同时,结合You Only Look Once version 5-Lite(YOLOv5-Lite)目标检测模型对图像中的故障进行实时识别,并对识别出的线路潜在故障点进行及时反馈。经实验测试,当巡检数据量为8 GB时,低空遥感技术的巡检时间仅为4.89 min。当巡检辐射频率为50 Hz时,低空遥感技术的巡检定时精度高达91.21%。此外,文中所提的目标检测模型的故障识别准确率高达92.64%。结果表明,结合无人机低空遥感技术与YOLOv5-Lite目标检测模型能够有效优化输变电线路的巡检系统,增强故障识别的准确性,为未来输变电线路巡检领域的智能化发展提供了技术支持。
文摘对于小电流接地系统的单相接地故障选线,传统方法普遍采用基于一维信号的选线模型,存在选线准确率低、抗噪性弱等问题。为此提出一种改进的变分模态分解及Conv Ne Xt的小电流接地系统单相接地故障选线方法。首先引入蚁狮算法优化变分模态分解算法,通过蚁狮算法自动寻优选取合适的分解次数和惩罚因子,计算分解得到的各分量的分布熵,将其中的噪声分量筛选去除,将其余有效分量进行线性重构得到降噪后的零序电流信号;其次,将经过降噪处理后的一维零序电流信号经格拉姆角场转换为二维图像,制备故障选线数据集;然后,引入预训练的ConvNeXt模型,根据该研究数据模型特征,在其已有权重基础上对模型参数进行对应微调,从而提高模型精度并形成最终的选线模型;最后引入绝对平均误差、均方根误差作为评价指标验证所提降噪算法有效性。分别在加入噪声与否的前提下,将所提模型与3种选线模型相比较。实验结果表明该模型的准确率最高、抗噪性方面更好,其中该研究算法准确率达到了99.82%并且在不同噪声条件下都能维持91%以上的准确率,高于其他选线模型,克服了传统故障选线方法准确率低、抗噪性差的问题。
