摘要
由电弧故障引起的航空安全问题深受人们重视,由于飞机内部空间狭小,电弧故障特征不明显且极易受到航空飞行条件的干扰,导致检测困难。该文将采集到的电弧电流信号看作一维时间序列,从系统的混沌特征出发,利用相空间重构技术将电弧电流信号引入高维空间,分析相平面吸引子的几何特征和属性特征,分别分析了中心距、矢径偏移、相关维数、K熵4种特征量在电弧故障发生前后的变化规律。利用主成分分析(principal component analysis,PCA)故障检测技术将特征矩阵降维处理,同时给出各试验负载下的PCA的监测统计量平方预测误差(squared prediction error,SPE)及其控制限,通过电弧故障发生前后的SPE值与其控制限的比较,实现航空交流电弧故障的检测。分析结果表明,4种特征量从不同的角度充分地反映了电弧故障发生前后系统变化的随机性和混沌性的差异,不需要人工设置阈值,可实现无监督在线判别电弧故障的发生。最后,利用主成分分析给出不同类型负载的分类方法,为在今后实现电弧故障检测的同时,实现负载的有效分类,能够更加有针对性地对电弧故障做出判断和应对。
The problems of aviation safety caused by arc fault are paid much attention by people. Due to the narrow internal space of the aircraft, the characteristics of arc fault are not obvious and they are easily interfered by aviation flight conditions, which makes detection difficult. In this paper, the collected arc current signal was regarded as a one-dimensional time series. Starting from the chaotic characteristics of the system, the phase space reconstruction technology was used to introduce the arc current signal into the high-dimensional space, and the geometric and attribute characteristics of the phase plane attractor were analyzed. The variation rules of the four characteristic quantities of center distance, radius vector offset, correlation dimension and Kolmogorov entropy were analyzed before and after arc fault. Arc fault detection technology based on principal component analysis(PCA) was used to reduce the dimensionality of the feature matrix. At the same time, the monitoring index of the squared prediction error(SPE) of the PCA and its control limit were given under each experimental load. By comparing the SPE value before and after the arc fault with its control limit, the aviation AC arc fault detection was realized. The analysis results show that the four characteristic quantities fully reflect the difference of randomness and chaos of the system before and after the occurrence of arc faults from different angles. There is no need to manually set the threshold, which can realize the unsupervised online identification of arc fault. Finally, the classification methods of different types of loads were given by principal component analysis. In order to realize the effective classification of loads while realizing the arc fault detection in the future, the arc faults can be judged and dealt with more specifically.
作者
崔芮华
李泽
佟德栓
CUI Ruihua;LI Ze;TONG Deshuan(State Key Laboratory of Reliability and Intelligence of Electrical Equipment(Hebei University of Technology),Hongqiao District,Tianjin 300130,China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province(Hebei University of Technology),Hongqiao District,Tianjin 300130,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2021年第14期5054-5065,共12页
Proceedings of the CSEE
关键词
航空电弧故障
混沌特性
相空间重构
吸引子
PCA检测技术
负载分类
aviation arc fault
chaos characteristics
phase space reconstruction
attractor
PCA detection technology
load classification
