首页 | 本学科首页   官方微博 | 高级检索  
     

基于混沌分形理论的故障电弧诊断方法研究
引用本文:苏晶晶,许志红. 基于混沌分形理论的故障电弧诊断方法研究[J]. 电机与控制学报, 2021, 25(3): 125-133. DOI: 10.15938/j.emc.2021.03.014
作者姓名:苏晶晶  许志红
作者单位:福州大学 电气工程与自动化学院,福州350116;闽江学院 计算机与控制工程学院,福州350108;福州大学 电气工程与自动化学院,福州350116
基金项目:福建省科技创新领军人才资助项目;宁德师范学院科研发展基金项目
摘    要:引入混沌分形理论,从混沌空间域角度分析故障电弧的内在演化规律和电弧特性,提出一种基于混沌分形理论的故障电弧诊断方法.通过重构相空间和盒维数、关联维数、最大Lyapunov指数等对电弧电流的混沌分形特性进行定性、定量分析,形成电弧的空间域特征向量,构建故障电弧诊断模型.针对低压用电系统中空气压缩机、开关电源等负载线路,对...

关 键 词:故障电弧  混沌分形特性  重构相空间  空间域特征  概率神经网络  故障电弧诊断

Arc fault diagnosis method based on chaos and fractal theories
SU Jing-jing,XU Zhi-hong. Arc fault diagnosis method based on chaos and fractal theories[J]. Electric Machines and Control, 2021, 25(3): 125-133. DOI: 10.15938/j.emc.2021.03.014
Authors:SU Jing-jing  XU Zhi-hong
Affiliation:(School of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350116,China;College of Computer and Control Engineering,Minjiang University,Fuzhou 350108,China)
Abstract:Based on the chaos fractal theories,the characteristics and the internal evolution of arc fault were analyzed,and an arc fault diagnosis method was put forward.The chaos and fractal characteristics of arc were qualitatively and quantitatively analyzed by using the reconstruction phase space theory and chaotic and fractal feature parameters,such as box dimension,correlation dimension and Lyapunov index.Then the spatial domain eigenvectors and the diagnosis model of arc fault were constructed.The chaos and fractal characteristics of current pre-and post-arc fault were analyzed to verify validity for low-voltage power systems with air compressors and switching power supplies.Experimental results show that the fractal structures and the characteristic parameters of chaotic fractal of current are different in the change of running state and load.Different evolution trend between normal current and arc current,and the chaos and fractal characteristic parameters show different rules.The accuracy of arc diagnosis model based on this feature is more than 90%.Meanwhile,the load identification rate is more than 90% under normal operation.
Keywords:arc fault  chaos and fractal characteristics  reconstructing phase space  space domain feature  probabilistic neural network  fault arc diagnosis
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号