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Application of Machine Learning in Addressing Power Quality Issues in...
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Application of Machine Learning in Addressing Power Quality Issues in Power Electronic Converters

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 6396

Special Issue Editors

Dr. Arshad Arshad

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Guest Editor
Electrical Power Engineering, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G1 1XQ, UK
Interests: high-voltage engineering; electricity markets; smart grids; power quality; power system design and operation
Special Issues, Collections and Topics in MDPI journals
Dr. Mohd Tariq

E-Mail Website
Guest Editor
Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India
Interests: power electronic converters; electrical drives; energy storage systems; renewable energy systems; electric vehicle; insulation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is increased interest in the design and development of modern power electronic converters due to the increased demand for renewable energy systems. Power electronic converters are playing an important role in the integration of renewable energy sources to the grid. Although these converters offer many advantages, however, there are certain challenges associated with the increased presence of converters in the grid. One of those challenges is the issue of power quality. The presence of power electronic converters leads to power quality problems such as harmonics, transients, as well as voltage swell/dips. Machine learning can play an important role in addressing these power quality issues by detecting and eliminating them. Apart from that, intelligent systems can also help in discriminating between different power quality phenomena and help utility engineers and managers in taking preemptive actions. This Special Issue will focus on publishing high-quality research work in the field of power electronics, power quality, and the applications of machine learning in addressing these problems. The specific topics of interest include but are not limited to:

  • Development in power electronic converters;
  • Power quality problems associated with converters;
  • Modern power electronic converters and their potential of addressing power quality concerns;
  • Condition monitoring of power electronic converters utilizing machine learning techniques;
  • Intelligent systems for addressing power quality concerns;
  • Challenges associated with renewable integration;
  • Propagation of harmonics through power electronic converters;
  • Interaction of harmonics with converter control systems;
  • Control architecture of modern power electronic converters.

Dr. Arshad Arshad
Dr. Mohd Tariq
Guest Editors

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. Electronics 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 2400 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.

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Published Papers (2 papers)

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Research

18 pages, 6761 KiB  
Article
Design and Comparative Analysis of an Ultra-Highly Efficient, Compact Half-Bridge LLC Resonant GaN Converter for Low-Power Applications
by Muhammad Faizan, Xiaolei Wang and Muhammad Zain Yousaf
Electronics 2023, 12(13), 2850; https://doi.org/10.3390/electronics12132850 - 28 Jun 2023
Cited by 7 | Viewed by 3088
Abstract
For low-power applications, this paper presents the development and design of a compact and ultra-highly efficient half-bridge LLC resonant converter. By using Galium Nitride (GaN) devices and high-efficient magnetics, the efficiency and power density of resonant converters can be improved. Compared to Silicon [...] Read more.
For low-power applications, this paper presents the development and design of a compact and ultra-highly efficient half-bridge LLC resonant converter. By using Galium Nitride (GaN) devices and high-efficient magnetics, the efficiency and power density of resonant converters can be improved. Compared to Silicon MOSFETs, GaN high-electron-mobility transistors (GaN HEMT) have a lower output capacitance and gate charge, resulting in lower driving loss and shorter dead times. Consequently, the proposed LLC converter based on GaN devices has excellent performance characteristics such as ultra-high efficiency, low switching losses, compact size, high voltage endurance, high operating temperature and high operating frequency. Furthermore, the proposed resonant converter features soft switching properties that ensure that the switches and diodes on the primary side are always switched at zero voltage and current. By doing so, LLC resonant converter switching losses are significantly reduced by up to 3.1%, and an overall efficiency of 98.5% is achieved. The LLC resonant converter design with GaN HEMT has great advantages over Si MOSFET solution regarding efficiency, overall losses, switching loose and power factor correction. A 240 W, 240 V to 60 V half-bridge GaN HEMT LLC resonant converter is simulated with a switching frequency of 75 KHz, along with the comparative analysis of the Si metal oxide semiconductor field effect transistor (MOSFET) solution. Moreover, the design and analysis of highly efficient magnetics with a power factor of 0.99 at full load is presented. A 240-Watt single stage LED driver with power factor correction is also designed to verify and compare the performance of proposed LLC resonant converter. Full article
(This article belongs to the Special Issue Application of Machine Learning in Addressing Power Quality Issues in Power Electronic Converters)
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21 pages, 9577 KiB  
Article
Multisegmented Intelligent Solution for MT-HVDC Grid Protection
by Muhammad Zain Yousaf, Sohrab Mirsaeidi, Saqib Khalid, Ali Raza, Chen Zhichu, Wasif Ur Rehman and Fazal Badshah
Electronics 2023, 12(8), 1766; https://doi.org/10.3390/electronics12081766 - 7 Apr 2023
Cited by 10 | Viewed by 2578
Abstract
Fault detection continues to be a relevant and ongoing topic in multiterminal High Voltage Direct Current (MT-HVDC) grid protection. In MT-HVDC grids, however, high DC-fault currents result from a failure of a complex protective threshold in traditional protection schemes, making Voltage Source Converter [...] Read more.
Fault detection continues to be a relevant and ongoing topic in multiterminal High Voltage Direct Current (MT-HVDC) grid protection. In MT-HVDC grids, however, high DC-fault currents result from a failure of a complex protective threshold in traditional protection schemes, making Voltage Source Converter (VSC) vulnerable to such potent transient currents. In this innovative single-ended DC protection scheme, multiple time window segments are used to consider the effects of the transient period across limiting inductors at each end of the link. Multiple segments of 0–0.8, 0.8–1.5, and 1.5–3.0 ms reduce relay failure and improve the sensitivity to high fault impedance while requiring minimal computational effort. It employs feature extraction tools such as Stationary Wavelet Transform and Random Search (RS)-based Artificial Neural Networks (ANNs) for detecting transmission line faults within DC networks. Its goal is to improve the accuracy and reliability of protective relays as a result of various fault events. Simulations showed that the proposed algorithms could effectively identify any input data segment and detect DC transmission faults up to 500 ohms. Accuracy for the first segment is 100% for fault impedance up to 200 ohms, whereas the second and third segments show 100% accuracy for high impedance faults up to 400 ohms. In addition, they maintain 100% stability even under external disturbances. Full article
(This article belongs to the Special Issue Application of Machine Learning in Addressing Power Quality Issues in Power Electronic Converters)
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