In the past few years, overhead copper transmission lines have been replaced by lightweight aluminum transmission lines to minimize the cost and prevent the sagging of heavier copper transmission lines.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.
One of important methods used for diagnostics of a transformer’s active part is Frequency Response Analysis (FRA). It allows to determine the mechanical condition of windings, their displacements, deformations and electric faults, as well as some problems with internal leads and connections, core and bushings. Still pending problem is interpretation of measurements results. One of approaches is application of computer modeling to simulate various failure modes and connected with them changes in FRA response. The paper presents two types of models, one based on lumped parameters with RLC elements, and one based on distributed parameters with TLM method. Both methods give similar results, comparable to real measurements of simulated coil.
Power system state estimation is a process of real-time online modeling of an electric power system. The estimation is performed with the application of a static model of the system and current measurements of electrical quantities that are encumbered with an error. Usually, a model of the estimated system is also encumbered with an uncertainty, especially power line resistances that depend on the temperature of conductors. At present, a considerable development of technologies for dynamic power line rating can be observed. Typically, devices for dynamic line rating are installed directly on the conductors and measure basic electric parameters such as the current and voltage as well as non-electric ones as the surface temperature of conductors, their expansion, stress or the conductor sag angle relative to the plumb line. The objective of this paper is to present a method for power system state estimation that uses temperature measurements of overhead line conductors as supplementary measurements that enhance the model quality and thereby the estimation accuracy. Power system state estimation is presented together with a method of using the temperature measurements of power line conductors for updating the static power system model in the state estimation process. The results obtained with that method have been analyzed based on the estimation calculations performed for an example system - with and without taking into account the conductor temperature measurements. The final part of the article includes conclusions and suggestions for the further research.
The paper concerns the problem of choosing a criterion for transmission line when lumped parameters analysis is required. First, the formal introduction of transmission line scheme is presented. Secondly, a new criterion for lumped-parameter analysis of transmission line is proposed. The criterion has clear physical meaning and simple mathematical form. The proposed criterion takes into account not only wave length, but also the dissipation of transmission line. The criterion can be easily adjusted to some requirements, such as needed level of no-load output voltage change.
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.
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.