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Abstract

In the past few years, overhead copper transmission lines have been replaced by lightweight aluminum transmission lines to minimize the cost and prevent the sagging of heavier copper transmission lines.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.

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Authors and Affiliations

Deokhyun Han
ORCID: ORCID
Geon-Hong Kim
Jaesung Kim
Byungmin Ahn
ORCID: ORCID
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Abstract

The effect of 0.2% addition of Mg, Co and Ce to 99.9% cast aluminium was studied by evaluation of changes in microstructure and mechanical properties. The microstructure was analyzed by scanning electron microscopy and transmission electron microscopy. The Al99.9 alloy contained only Al-Fe-Si phase particles. Similar Al-Fe-Si particles were observed in alloy with 0.2% Mg addition, because this amount of magnesium was fully dissolved in the solid solution. The addition of cobalt resulted in the formation of Al9.02Co1.51Fe0.47 phase particles assuming the shape of eutectic plates. The electron backscattered diffraction map made for the alloy with 0.2% Co addition showed numerous twin boundaries with distances between them in the range from 10 to 100 µm. The addition of cerium was located in the grain boundary area. Cerium also gave rise to the formation of two types of particles, i.e. Al4Ce and Al-Ce-Fe-Si. The Al-Ce-Fe-Si phase is a nucleation site for the Al4Ce phase, which forms eutectic plates. The results showed that the introduction of additives increases the mechanical properties of the cast materials. The 99.9% cast aluminium has a hardness of 16.9 HB. The addition of 0.2% by weight of Mg, Co, Ce increases this hardness to 21.8 HB, 22.6 HB and 19.1 HB, respectively.
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Authors and Affiliations

P. Koprowski
1
ORCID: ORCID
M. Lech-Grega
1
Ł. Wodzinski
2
ORCID: ORCID
B. Augustyn
1
ORCID: ORCID
S. Boczkal
1
ORCID: ORCID
P. Uliasz
2
ORCID: ORCID
M. Ożóg
2
ORCID: ORCID

  1. Łukasiewicz Research Network – Institute of Non-Ferrous Metals, Division in Skawina, 19 Piłsudskiego Str., 32-050 Skawina, Poland
  2. Boryszew S.A., Modern Aluminium Products, 23 Piłsudskiego Str., 32-050 Skawina, Poland
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Abstract

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.
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Bibliography

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Authors and Affiliations

Melih Coban
1 2
ORCID: ORCID
Suleyman S. Tezcan
2
ORCID: ORCID

  1. Bolu Abant Izzet Baysal University, Bolu, Turkey
  2. Gazi University, Ankara, Turkey
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Abstract

Transmission lines’ live working is one of an effective means to ensure the reliable operation of transmission lines. In order to solve the unsafe problems existing in the implementation of traditional live working, the paper uses ground-based lidar to collect point cloud data. A tile based on the pyramid data structure is proposed to complete the storage and calling of point cloud data. The improved bidirectional filtering algorithm is used to distinguish surface features quickly and obtain a 3D model of the site. Considering the characteristics of live working, the speed of data reading and querying, the nearest point search algorithm based on octree is used to acquire a real- time calculation of the safe distance of each point in the planned path, and the safety of the operation mode is obtained by comparing with the value specified in the regulation, and assist in making decisions of the operation plan. In the paper, the simulation of the actual working condition is carried out by taking the “the electric lifting device ascending” as an example. The experimental results show that the established three-dimensional model can meet the whole process control of the operation, and has achieved practical effect.
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Authors and Affiliations

Ying Wang
1
ORCID: ORCID
Haitao Zhang
1 2 3
Qiang Lv
3
Qiang Gao
3
Mingxing Yi
3

  1. School of Automation & Electrical Engineering, Lanzhou Jiaotong University, Gansu, China
  2. Key Laboratory of Opto-Electronic Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University Gansu, China
  3. The UHV Company of State Grid Gansu Electric Power Company, Gansu, China
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Abstract

Lightning is one of the causes of transmission disorders and natural phenomena that cannot be avoided. The South Sulawesi region is located close to the equator and has a high lightning density. This condition results in lightning susceptibility of disturbances to electrical system lines, especially in high-voltage airlines and substations. An Adaptive Neuro-Fuzzy Inference System (ANFIS) will show the Root Mean Square Error (RMSE) based on the membership function type. This journal is to predict the value of the transmission tower lightning density using the ANFIS method. The value of the lightning strike density index can later be determined based on ANFIS predictions. Analysis of the value calculation system of structural lightning strikes in the South Sulawesi region of the Sungguminasa-Tallasa route can be categorized as three characteristics lightning density (Nd). The calculation system results for the value of structural lightning struck in the South Sulawesi region and validated between manual calculations and ANFIS with an average percentage of 0.0554%.
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Bibliography

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[2] Rawi I.M., Kadir M.Z.A.A., Azis N., Lightning study and experience on the first 500kV transmission line arrester in Malaysia, in 2014 International Conference on Lightning Protection (ICLP), pp. 1106–1109 (2014), DOI: 10.1109/ICLP.2014.6973289.
[3] Gassing, Analisis Sistem Proteksi Petir (Lighting Performance) Pada Sutt 150 kV Sistem Sulawesi Selatan, vol. 6, pp. 978–979 (2012).
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Authors and Affiliations

Sri Mawar Said
1
Muhammad Bachtiar Nappu
1
Andarini Asri
2
Bayu Tri Utomo
1

  1. Hasanuddin University, Indonesia
  2. Ujung Pandang State Polytechnic, Indonesia
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Abstract

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.

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Authors and Affiliations

Milad Daneshvar
Nasser Parhizgar
Homayoon Oraizi
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Abstract

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.

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Authors and Affiliations

Jacek Cieslik
Vladimir Kismereshkin
Ekaterina Ritter
Alexey Savostin
Dmitry Ritter
Nabi Nabiyev
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Abstract

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.

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Authors and Affiliations

S. Fuada
H.A. Shiddieqy
T. Adiono

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