A New Method for Live Line Measuring the Impedance Parameters of Transmission Lines Based on WAMS

Author(s):  
Yishu Zhao ◽  
Jie Zhan ◽  
Yan Zhang ◽  
Yongjun Yang ◽  
Zhijian Hu
2022 ◽  
Vol 12 (2) ◽  
pp. 875
Author(s):  
Nan Zhang ◽  
Xiaolong Wang ◽  
Chunxi Bao ◽  
Bin Wu ◽  
Chun-Ping Chen ◽  
...  

In this paper, a novel synthetization approach is proposed for filter-integrated wideband impedance transformers (ITs). The original topology consists of N cascaded coupled line sections (CLSs) with 2N characteristic impedance parameters. By analyzing these characteristic impedances, a Chebyshev response can be derived to consume N + 2 design conditions. To optimize the left N − 2 variable parameters, CLSs were newly substituted by transmission lines (TLs) to consume the remaining variable parameters and simplify the circuit topology. Therefore, there are totally 2N − N − 2 substituting possibilities. To verify the proposed approach, 25 cases are listed under the condition of N = 5, and 7 selected cases are compared and discussed in detail. Finally, a 75–50 Ω IT with 100% fractional bandwidth and 20 dB bandpass return loss (RL) is designed and fabricated. The measured results meet the circuit simulation and the EM simulation accurately.


1992 ◽  
Vol 47 (11-12) ◽  
pp. 548-550
Author(s):  
Marco Bressan ◽  
Giuseppe Conciauro ◽  
Paolo Gamba

2013 ◽  
Vol 732-733 ◽  
pp. 1056-1064
Author(s):  
Yang Chen ◽  
Yan Hu ◽  
Neng Ling Tai

Since the existing fault phase identification methods can not identify all fault types quickly and accurately for high voltage transmission lines, this article proposed a new method of fault phase identification based on the fault component of phase voltage difference and the kalman filter algorithm. The method defined the fault components ratio of one phase voltage to the difference of the other two phase voltages as a fault phase identification factor. By analyzing the characteristics of fault phase identification factors in each fault type, the fault phase can be identified. Simulation results show that using the kalman filter algorithm to extract fundamental component is faster and more accurate. Meanwhile, the method can identify fault phases within half a cycle and is scarcely influenced by fault resistances, fault locations and fault initial phase angles. It also has a high sensitivity when the fault is on the side of strong source.


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