Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identi...Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.展开更多
The nonlinear resistance characteristics of microcavity dielectric barrier discharge are mainly studied in the paper. A simulation model of microcavity dielectric barrier discharge is herein built to study the relatio...The nonlinear resistance characteristics of microcavity dielectric barrier discharge are mainly studied in the paper. A simulation model of microcavity dielectric barrier discharge is herein built to study the relationship between voltage and current in the process of discharge, and thus its I–V characteristic curve can be obtained. The I–V characteristics of the memristor are analyzed and compared with the I–V characteristics of the dielectric barrier discharge; it can be found that the I–V characteristics of the microcavity dielectric barrier discharge are similar to the characteristics of the memristor by analyzing them. The memory characteristics of microcavity dielectric barrier discharge are further analyzed.展开更多
基金This work was supported in part by the Natural Science Foundation of Henan Province,and the specific grant number is 232300420301。
文摘Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.
基金Project supported by the National Natural Science Foundation of China(Nos.U1204506,11405044)
文摘The nonlinear resistance characteristics of microcavity dielectric barrier discharge are mainly studied in the paper. A simulation model of microcavity dielectric barrier discharge is herein built to study the relationship between voltage and current in the process of discharge, and thus its I–V characteristic curve can be obtained. The I–V characteristics of the memristor are analyzed and compared with the I–V characteristics of the dielectric barrier discharge; it can be found that the I–V characteristics of the microcavity dielectric barrier discharge are similar to the characteristics of the memristor by analyzing them. The memory characteristics of microcavity dielectric barrier discharge are further analyzed.
