A thin film type magnetic/dielectric hybrid transmission-line with a large wavelength shortening

2001 ◽  
Vol 37 (4) ◽  
pp. 2903-2905 ◽  
Author(s):  
S. Ikeda ◽  
T. Sato ◽  
A. Ohshiro ◽  
K. Yamasawa ◽  
T. Sakuma
Keyword(s):  
Thin Film ◽  
Transmission Line ◽  
Film Type ◽  
Hybrid Transmission ◽  
Magnetic Dielectric ◽  
Large Wavelength

Analysis of a directional coupler using magnetic/dielectric hybrid transmission-line for cellular phone

2006 ◽  
Author(s):  
Kazutaka Takizawa ◽  
So Mizuta ◽  
Masahiro Nakazawa ◽  
Toshiro Sato ◽  
Kiyohito Yamasawa ◽  
...  
Keyword(s):  
Transmission Line ◽  
Directional Coupler ◽  
Cellular Phone ◽  
Hybrid Transmission ◽  
Magnetic Dielectric

Analysis of Thin Film Optical Filters Using Transmission Line Models

1994 ◽  
Vol 23 (1) ◽  
pp. 39-43
Author(s):  
B. Lakshmi ◽  
K. R. Suresh Nair ◽  
Y. G. K. Patro ◽  
B. M. Arora
Keyword(s):  
Thin Film ◽  
Transmission Line ◽  
Optical Filters ◽  
Transmission Line Models

Unsynchronized parameter free fault location scheme for hybrid transmission line

2021 ◽  
Vol 192 ◽  
pp. 106982
Author(s):  
Seyed Mehran Hashemian ◽  
Seyed Nasrollah Hashemian ◽  
Mehdi Gholipour
Keyword(s):  
Transmission Line ◽  
Fault Location ◽  
Hybrid Transmission

scholarly journals Comparison of Artificial Intelligence Methods for Fault Classification of the 115-kV Hybrid Transmission System

2020 ◽  
Vol 10 (11) ◽  
pp. 3967 ◽  
Author(s):  
Jittiphong Klomjit ◽  
Atthapol Ngaopitakkul
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Transmission Line ◽  
Fault Location ◽  
Back Propagation ◽  
Fault Classification ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mother Wavelet ◽  
Hybrid Transmission

This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.

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