Attenuation constant and characteristic impedance calculation of top metal-covered CPW transmission line using neural networks

2019 ◽  
Vol 18 (4) ◽  
pp. 1342-1346
Author(s):  
Amit Kumar Sahu ◽  
Dhruba Charan Panda ◽  
Nihar Kanta Sahoo
Keyword(s):  
Neural Networks ◽  
Transmission Line ◽  
Characteristic Impedance ◽  
Attenuation Constant ◽  
Impedance Calculation

A Bandpass Filter Based on Novel SCRLH Transmission Line Structure

2013 ◽  
Vol 456 ◽  
pp. 624-626
Author(s):  
Li Bin Wan ◽  
Ya Lin Guan ◽  
Xin Kun Tang
Keyword(s):  
Transmission Line ◽  
Floquet Theory ◽  
Bandpass Filter ◽  
Characteristic Impedance ◽  
The Novel ◽  
Attenuation Constant ◽  
Left Handed ◽  
Impedance Distribution ◽  
Novel Structure ◽  
Impedance Characteristics

In this paper, a novel simplified composite right/left handed (SCRLH) transmission line (TL) structure is proposed.The dispersion and impedance characteristics of the novel structure are first analysed based on Bloch-Floquet theory, which shows that the attenuation constant keeps zero with a relatively smooth characteristic impedance distribution within the passband and that the characteristic impedance is purely imaginary with the inhibition of the electromagetic wave propagation outside the passband.

scholarly journals INVESTIGATION OF CALCULATION TECHNIQUES OF FINITE DIFFERENCE METHOD

2010 ◽  
Vol 2 (1) ◽  
pp. 103-107 ◽  
Author(s):  
Audrius Krukonis
Keyword(s):  
Finite Difference ◽  
Finite Difference Method ◽  
Transmission Line ◽  
Difference Method ◽  
Characteristic Impedance ◽  
Electrical Potential ◽  
Microstrip Transmission Line ◽  
Impedance Calculation ◽  
Line Analysis ◽  
Matrix Calculation

Finite difference method used for microstrip transmission line analysis is considered in this article. Paper mainly deals with iterative and bound matrix calculation techniques of finite difference method. Mathematical model for microstrip transmission line electrical potential calculations using both techniques is described. Results of characteristic impedance calculation using iterative and bound matrix techniques are presented and analyzed.

scholarly journals Classification and direction discrimination of faults in transmission lines using 1D convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 1928
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer ◽  
Adel Manaa Dakhil
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Transmission Line ◽  
Convolutional Neural Networks ◽  
Transmission Lines ◽  
Fault Classification ◽  
Line System ◽  
Direction Discrimination ◽  
Conventional Methods

<span lang="EN-US">This paper presents a fast and accurate fault detection, classification and direction discrimination algorithm of transmission lines using one-dimensional convolutional neural networks (1D-CNNs) that have ingrained adaptive model to avoid the feature extraction difficulties and fault classification into one learning algorithm. A proposed algorithm is directly usable with raw data and this deletes the need of a discrete feature extraction method resulting in more effective protective system. The proposed approach based on the three-phase voltages and currents signals of one end at the relay location in the transmission line system are taken as input to the proposed 1D-CNN algorithm. A 132kV power transmission line is simulated by Matlab simulink to prepare the training and testing data for the proposed 1D- CNN algorithm. The testing accuracy of the proposed algorithm is compared with other two conventional methods which are neural network and fuzzy neural network. The results of test explain that the new proposed detection system is efficient and fast for classifying and direction discrimination of fault in transmission line with high accuracy as compared with other conventional methods under various conditions of faults.</span>

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

Characteristic impedance of a curved microstrip transmission line

1988 ◽  
Vol 1 (7) ◽  
pp. 257-259 ◽  
Author(s):  
J. S. Roy ◽  
D. R. Poddar ◽  
A. Mukherjee ◽  
S. K. Chowdhury
Keyword(s):  
Transmission Line ◽  
Characteristic Impedance ◽  
Microstrip Transmission Line

Transmission Line Theories for the Analysis of Electromagnetic Transients in Coil Windings

2013 ◽  
pp. 1-44
Author(s):  
Akihiro Ametani ◽  
Teruo Ohno
Keyword(s):  
Transmission Line ◽  
Propagation Constant ◽  
Characteristic Impedance ◽  
Electromagnetic Transients ◽  
Distributed Parameter ◽  
System A ◽  
Three Phase ◽  
Distributed Parameter Circuit ◽  
Transformer Winding ◽  
Phase Transmission

The chapter contains the basic theory of a distributed-parameter circuit for a single overhead conductor and for a multi-conductor system, which corresponds to a three-phase transmission line and a transformer winding. Starting from a partial differential equation of a single conductor, solutions of a voltage and a current on the conductor are derived as a function of the distance from the sending end. The characteristics of the voltage and the current are explained, and the propagation constant (attenuation and propagation velocity) and the characteristic impedance are described. For a multi-conductor system, a modal theory is introduced, and it is shown that the multi-conductor system is handled as a combination of independent single conductors. Finally, a modeling method of a coil is explained by applying the theories described in the chapter.

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