In the past few years, overhead copper transmission lines have been replaced by lightweight aluminum transmission lines to minimize the cost and prevent the sagging of heavier copper transmission lines. High strength aluminum alloys are used as the core of the overhead transmission lines because of the low strength of the conductor line. However, alloying copper with aluminum causes a reduction in electrical conductivity due to the solid solution of each component. Therefore, in this study, the authors attempt to study the effect of various Al/Cu ratios (9:1, 7:3, 5:5) to obtain a high strength Al-Cu alloy without a significant loss in its conductivity through powder metallurgy. Low-temperature extrusion of Al/Cu powder was done at 350ºC to minimize the alloying reactions. The as-extruded microstructure was analyzed and various phases (Cu9Al4, CuAl2) were determined. The tensile strength and electrical conductivity of different mixing ratios of Al and Cu powders were studied. The results suggest that the tensile strength of samples is improved considerably while the conductivity falls slightly but lies within the limits of applications.
A design of microwave installation for energy concentration on a surface of a heated object is proposed. In the installation a dipole lattice on the basis of a single-wire transmission line is used which is located inside of reflector in a form of specular parabolic conducting cylinder. The heated object is placed in the area of microwave energy concentration.
In the article a waveguide field of a surface wave in a reradiation mode is explored. The surface wave is reradiated by a group of vibrators coaxial with the waveguide wire. Results of experimental studies of field distribution along the waveguide operating in various modes are presented. The possibility of efficiency increase in reradiated field and its adjustment by contactless movement of reflector is shown.
To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.
Controlling and reducing the radiation emitted by various systems helps the board designer improve systems’ performance. One proposed way to achieve these goals is to use an algorithm to control the radiation applied to systems. According to the executive structure of the algorithm and considering the nature of the existing signals in several components, the separation of the signal components is on the agenda of the algorithm. In fact, the goal is to create an intuitive view of the multi-component signals around the systems that enter the systems from different angles and have a detrimental effect on their performance. Using signal processing methods, we will be able to break down the signal into different components and simulate each component separately. To prevent high computational repetitions and increase simulation time in signal component analysis, by reducing the components, we reduce the number of mesh cells in the software and, using linear approximation, determine the exact position of the radiation signal applied to systems and thus the best linear relationship. The signal entry path is used to apply the rules required for prediction design.