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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (5 January 2023) | Viewed by 8152

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Dr. Ali Farzamnia

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Guest Editor

Electrical & Electronic Engineering Program (HK02); Faculty of Engineering; Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
Interests: signal processing; biomedical signal processing; network coding; deep learning; digital communication; wireless communication
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Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to the Special Issue of Energies “Challenges and Research Trends in Telecommunication and Electrical Engineering”. Submissions may include original, unpublished papers from researchers in academia and industry. The papers should include novel methodologies and implementations and creative and innovative developments in electrical engineering associated with the scope of the Special Issue.

Today, electrical systems and telecommunications play an important role in modern society. The use of Electrical Systems has been increasing not only in traditional fields but also in more recent areas such as renewable energy, electrical vehicles, unmanned aircraft, and robotics. Signal processing lies at the heart of our modern electrical systems. It enhances our ability to communicate and share information. On the other hand, with the rapid growth of electrical system measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge. In recent years, deep learning has emerged as a novel class of machine learning algorithms that represent electrical systems data via a large hypothesis space that leads to state-of-the-art performance. Deep learning techniques are especially useful for analyzing complex, rich, and multidimensional signals.

Based on these considerations, this Special Issue will focus on the modeling and analysis of electrical power systems, telecommunication systems, renewable energy, electrical vehicles, smart grids, energy and environment, and signal estimation.

Dr. Ali Farzamnia
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

signal processing
telecommunication engineering
renewable energy
power electronics
robotics
smart grids
deep learning
unmanned vehicles
power systems
signal detection and estimation
energy and environment
energy sources

Published Papers (5 papers)

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Research

15 pages, 2315 KiB

Open AccessArticle

Impulsive Noise Suppression Methods Based on Time Adaptive Self-Organizing Map

by Seyed Hamidreza Hazaveh, Ali Bayandour, Azam Khalili, Ali Barkhordary, Ali Farzamnia and Ervin Gubin Moung

Energies 2023, 16(4), 2034; https://doi.org/10.3390/en16042034 - 18 Feb 2023

Cited by 2 | Viewed by 1285

Abstract

Removal of noise and restoration of images has been one of the most interesting topics in the field of image processing in the past few years. Existing filter-based methods can remove image noise; however, they cannot preserve image quality and information such as lines and edges. In this article, various classifiers and spatial filters are combined to achieve desirable image restoration. Meanwhile, the time adaptive self-organizing map (TASOM) classifier is more emphasized in our feature extraction and dimensionality reduction approaches to preserve the details during the process, and restore the images from noise. The TASOM was compared with the self-organizing map (SOM) network, and a suitable noise reduction method for images was attempted. As a result, we achieved an optimum method to reduce impulsive noise. In addition, by using this neural network, better noise suppression was achieved. Experimental results show that the proposed method effectively removes impulse noise and maintains color information as well as image details. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Telecommunication and Electrical Engineering)

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19 pages, 6233 KiB

Open AccessArticle

A Comparison of Double-End Partial Discharge Localization Algorithms in Power Cables

by Asfarina Abu Bakar, Chai Chang Yii, Chin Kui Fern, Yoong Hou Pin, Herwansyah Lago and Mohamad Nur Khairul Hafizi Rohani

Energies 2023, 16(4), 1817; https://doi.org/10.3390/en16041817 - 11 Feb 2023

Cited by 3 | Viewed by 1414

Abstract

The double-end partial discharge (PD) measurement method is the most common method for measuring and localizing PD sources in power cables. The sensitivity of the PD sensor, the processing speed of the data acquisition unit, and the method of the PD localization algorithm are the three main keys to ensuring the accuracy of the PD source localization on power cables. A new multi-end PD localization algorithm known as segmented correlation trimmed mean (SCTM) has recently demonstrated excellent accuracy in the localization of PD sources on power cables. The algorithm, however, is only applicable to multi-end PD measurement methods. In this paper, the mathematical equation of the SCTM algorithm is customized to match the double-end PD measurement method. A MATLAB simulation was conducted to assess the performance of the SCTM algorithm in the double-end PD measurement method. The maximum peak detection (MPD) algorithm, segmented correlation (SC), and SCTM algorithm were compared as PD localization algorithms. The SC algorithms have shown that identifying the correlation bond between two cues instead of the peak of the PD signal in the MPD algorithm significantly increases the PD localization accuracy. The results show that the SCTM algorithm outperforms the MPD and SC algorithms in terms of accuracy. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Telecommunication and Electrical Engineering)

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18 pages, 4493 KiB

Open AccessArticle

Hybrid Gray Wolf Optimization–Proportional Integral Based Speed Controllers for Brush-Less DC Motor

by Shukri Mahmood Younus Younus, Uğurhan Kutbay, Javad Rahebi and Fırat Hardalaç

Energies 2023, 16(4), 1640; https://doi.org/10.3390/en16041640 - 7 Feb 2023

Cited by 7 | Viewed by 1567

Abstract

For Brush-less DC motors to function better under various operating settings, such as constant load situations, variable loading situations, and variable set speed situations, speed controller design is essential. Conventional controllers including proportional integral controllers, frequently fall short of efficiency expectations and this is mostly because the characteristics of a Brush-less DC motor drive exhibit non linearity. This work proposes a hybrid gray wolf optimization and proportional integral controller for management of the speed in Brush-less DC motors to address this issue. For constant load conditions, varying load situations and varying set speed situations, the proposed controller’s efficiency is evaluated and contrasted with that of PID controller, PSO-PI controller, and ANFIS. In this study, two PI controller are used to get the more stability of the system based on tuning of their coefficients with meta heuristic method. The simulation findings show that Hybrid GWO-PI-based controllers are in every way superior to other controllers under consideration. In this study, four case studies are presented, and the best-case study was obtained 0.18619, 0.01928, 0.00030, and 0.01233 for RMSE, IAE, ITAE, and ISE respectively. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Telecommunication and Electrical Engineering)

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15 pages, 3307 KiB

Open AccessArticle

Multi-Controller Model for Improving the Performance of IoT Networks

by Ganesh Davanam, Suresh Kallam, Ninni Singh, Vinit Kumar Gunjan, Sudipta Roy, Javad Rahebi, Ali Farzamnia and Ismail Saad

Energies 2022, 15(22), 8738; https://doi.org/10.3390/en15228738 - 21 Nov 2022

Cited by 4 | Viewed by 1505

Abstract

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Telecommunication and Electrical Engineering)

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17 pages, 9945 KiB

Open AccessArticle

Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

by Raad Salih Jawad and Hafedh Abid

Energies 2022, 15(20), 7775; https://doi.org/10.3390/en15207775 - 20 Oct 2022

Cited by 7 | Viewed by 1745

Abstract

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Telecommunication and Electrical Engineering)

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