ELECTROMAGNETIC INTERFERENCE (EMI) PRODUCED BY HIGH VOLTAGE TRANSMISSION LINES

Electromagnetic interference in high voltage transmission lines has been an interest topic due to its effect on human health, plants, electrical and telecommunication equipment. Extremely high voltages (EHV) in transmission lines are reasons of electrostatic effects, while short circuit currents and line loading currents are responsible for electromagnetic effects. The aim of this research is to analyze electromagnetic fields in high voltage transmission lines in theoretical study and calculating its level in overhead T. L and therefore estimated the EMI produced, by employing a mathematical model of 230 KV tower double circuit configurations of high voltage transmission lines. The calculation is based on computer aided analysis (CAA) by using fields and corona effects software (FACE). It's found that the overhead power lines of general frequency (50 Hz) generates a highly intense magnetic field, the electromagnetic fields depends on the distance from sources and the type of line configuration. They decrease as the distance increase from the tower and conductors and increase with a high current. The strength of an electric field is proportional to the voltage of the line and the magnetic field strength is proportional to the current in the high voltage transmission lines. Distribution line with a high current load may produce a magnetic field that is as high as those produced by some high voltage transmission lines.  Some techniques of reduction of the effects of electromagnetic interference have suggested such as rearrangement conductors of transmission line, and distance from phase conductor and grounding system. The study recommended to keep safety distance operation in high voltage transmission lines with the necessity for engineer to take into account the effect of electromagnetic interference in the design stage of high voltage transmission power system, and to avoid any addition cost may be occur due to neglected effects of electromagnetic interference that produces by high voltage transmission line

Review of cloud computing in science, technology, and real life

This paper presents an overview of the general idea and history of cloud computing in theory. The objective of this review is to draw attention to preceding studies about cloud computing that have common characteristics with the theme of this paper. There were some points discussed in general, including the advantages of this technology, its subjects, security, and the effects of adopting cloud computing in an organization

Emerging wireless communication technologies in Iraqi government: Exploring cloud, edge, and fog computing

This study aims to structure the implementation of a governmental cloud of things (CoT), edge computing (EC), and fog computing in Iraq in the context of sustainable wireless communication. A base of literature was built that included any challenges, opportunities, and best practices relevant to these innovative technologies to set up the background for this paper. A concept model was created that included core components (cognitive technologies and fog computing), key processes (resource analysis, infrastructure design), and stakeholders (governments, industry, community). A strategic methodology made up of stakeholder involvement, capacity building, and pilot projects was used in the project. Concerning IoT planned deployment and services provision, network infrastructure was put in place to support the devices and a higher level of security measures were recommended. Using scenario hypothesis, MATLAB simulator was employed to simulate data value distribution as well as received power distribution based on different institutions for 12 months. Monitoring and evaluation should be followed to measure performance indicators and effects on this process. Continuously improvement strategies were the highlight of the session which further stimulated innovations. Acquainted projects will be put in the function to extend the range of activities by including additional government agencies, regions, or sectors. Reporting of the collected data and funding will be done with stakeholders to share and pool knowledge

Convolutional Deep Neural Network and Full Connectivity for Speech Enhancement

The speech signal that is received in real-time has background noise and reverberations, which have an impact on the quality of speech. Therefore, it is crucial to reduce or eliminate the noise and increase the intelligibility and quality of speech signals. In this study, a proposed method that is the most effective and challenging in a low SNR environment for three types of noise are removed, including washing machine, traffic noise, and electric fan noise, and clean speech is recovered. with three samples of noise which are mixed and added to the clean speech signal with a lower level of SNR value fixed at (-5, 0, 5) dBs, that noise source takes equal weights. The enhancement of the corrupted speech signal is done by applying a fully connected and convolutional neural network-based denoising algorithm and comparing their performance. The proposed network shows that a fully connected network (FCN) has less elapsed time than a convolutional network (CNN) while still achieving better performance, demonstrating its applicability for an embedded system. Also, the results obtained show that, overall, the CNN is better than the FCN regarding maximum coloration, PSNR, MES, and STOI

Filtering Power Divider Design Using Resonant LC Branches for 5G Low-Band Applications

This paper proposes an ultra-compact filtering power divider with a wide harmonic suppression band. In this design, the proposed power divider (PD) in the ideal case has 100% size reduction and an infinite number of harmonics suppression. However, in the real case, the proposed divider has a 92% size reduction and suppresses the 2nd to 45th harmonics. The small-proposed divider is designed at 0.9 GHz. The typical Wilkinson divider has two long quarter-wavelength branches. In the proposed design, new resonant series LC branches are used instead of the divider’s typical branches, leading to performance improvements in the proposed PD. To the best of the authors’ knowledge, the proposed filtering PD has the best size reduction, and harmonics suppression reported thus far. The proposed divider has a filtering response with good insertion loss at the passband, which is desirable for modern communication systems

A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications

This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement