Application of Artificial Intelligence in Power Electronic Devices and Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence Circuits and Systems (AICAS)".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 9303

Special Issue Editors


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Guest Editor

Special Issue Information

Dear Colleagues,

Maintaining the quality of life of modern society is associated with increasing energy needs, and declining stocks of energy resources inevitably lead to the need for their efficient and sustainable consumption. The main units in the process of energy conversion are power electronic devices and power electronic systems. The significance of these studies is indisputable—the improvement of the efficiency of electricity conversion by only a few percentage points, with the current volumes of global consumption, will lead to savings significantly exceeding the entire production of a number of countries. This will have a direct effect by reducing harmful emissions and improving the environment.

It is therefore crucial that researchers working in the field of power electronic converters and power electronic systems have sufficient modern knowledge and tools, as well as access to the experience and achievements gained. This is the purpose of this Special Issue: for engineers and scientists who have gained new knowledge and experience while working in the field of power electronic devices and power electronic systems to disseminate and multiply them.

In this Special Issue, we expect contributions from the fields of research related to the adaptation, development, and implementation of artificial intelligence techniques in the modeling, design, control, and operation of power electronic devices and power electronic systems.

The area of interest of the Special Issue is vast. We expect contributions on the following topics:

  • Modeling of power electronic devices and systems;
  • Innovative control of power electronic devices and systems;
  • Optimal design of power electronic devices and systems;
  • Maintenance of power electronic devices and systems;
  • Energy harvesting and storage;
  • Power electronic converters in industrial technology;
  • Charging infrastructure for electric vehicles;
  • Power electronic converters for electric vehicles;
  • Smart grids;
  • Smart city;
  • IoT for sustainable energy conversion;
  • Cybersecurity in power electronic systems.

Prof. Dr. Nikolay Hinov
Prof. D.Sc. Valeri Mladenov
Guest Editors

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Keywords

  • power electronic converters
  • power electronic systems
  • artificial intelligence
  • mathematical modeling
  • control theory and applications
  • circuits and systems

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Published Papers (3 papers)

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Research

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13 pages, 4481 KiB  
Article
Synthesis of Induction Brazing System Control Based on Artificial Intelligence
by Dragomir Grozdanov, Bogdan Gilev and Nikolay Hinov
Electronics 2021, 10(10), 1190; https://doi.org/10.3390/electronics10101190 - 16 May 2021
Viewed by 2475
Abstract
This paper considers the synthesis of control of an electro-technological system for induction brazing and its relationship with the guarantee of the parameters and the quality of this industrial process. Based on a created and verified 3D model of the electromagnetic system, the [...] Read more.
This paper considers the synthesis of control of an electro-technological system for induction brazing and its relationship with the guarantee of the parameters and the quality of this industrial process. Based on a created and verified 3D model of the electromagnetic system, the requirements to the system of power electronic converters for obtaining brazing between different common combinations of materials are determined. After processing and summarizing the results, an approach for automatic recognition of the type of material to be brazed is proposed and researched, as well as switching between different controller settings in order to achieve optimal performance and ease the operator. The advantages of using such an approach based on the use of artificial intelligence techniques are considered, including guidelines for its application and development in industrial systems with induction heating. Full article
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19 pages, 6137 KiB  
Article
Intervention of Artificial Neural Network with an Improved Activation Function to Predict the Performance and Emission Characteristics of a Biogas Powered Dual Fuel Engine
by Vinay Arora, Sunil Kumar Mahla, Rohan Singh Leekha, Amit Dhir, Kyungroul Lee and Hoon Ko
Electronics 2021, 10(5), 584; https://doi.org/10.3390/electronics10050584 - 3 Mar 2021
Cited by 12 | Viewed by 2348
Abstract
Biogas is a significant renewable fuel derived by sources of biological origin. One of today’s research issues is the effect of biofuels on engine efficiency. The experiments on the engine are complicated, time consuming and expensive. Furthermore, the evaluation cannot be carried out [...] Read more.
Biogas is a significant renewable fuel derived by sources of biological origin. One of today’s research issues is the effect of biofuels on engine efficiency. The experiments on the engine are complicated, time consuming and expensive. Furthermore, the evaluation cannot be carried out beyond the permissible limit. The purpose of this research is to build an artificial neural network successfully for dual fuel diesel engine with a view to overcoming experimental difficulties. Authors used engine load, bio-gas flow rate and n-butanol concentration as input parameters to forecast target variables in this analysis, i.e., smoke, brake thermal efficiency (BTE), carbon monoxide (CO), hydrocarbon (HC), nitrous-oxide (NOx). Estimated values and results of experiments were compared. The error analysis showed that the built model has quite accurately predicted the experimental results. This has been described by the value of Coefficient of determination (R2), which varies between 0.8493 and 0.9863 with the value of normalized mean square error (NMSE) between 0.0071 and 0.1182. The potency of the Nash-Sutcliffe coefficient of efficiency (NSCE) ranges from 0.821 to 0.8898 for BTE, HC, NOx and Smoke. This research has effectively emulated the on-board efficiency, emission, and combustion features of a dual-fuel biogas diesel engine taking the Swish activation mechanism in artificial neural network (ANN) model. Full article
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Review

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14 pages, 6159 KiB  
Review
Low-Dimensional Layered Light-Sensitive Memristive Structures for Energy-Efficient Machine Vision
by Gennady N. Panin
Electronics 2022, 11(4), 619; https://doi.org/10.3390/electronics11040619 - 17 Feb 2022
Cited by 3 | Viewed by 1984
Abstract
Layered two-dimensional (2D) and quasi-zero-dimensional (0D) materials effectively absorb radiation in the wide ultraviolet, visible, infrared, and terahertz ranges. Photomemristive structures made of such low-dimensional materials are of great interest for creating optoelectronic platforms for energy-efficient storage and processing of data and optical [...] Read more.
Layered two-dimensional (2D) and quasi-zero-dimensional (0D) materials effectively absorb radiation in the wide ultraviolet, visible, infrared, and terahertz ranges. Photomemristive structures made of such low-dimensional materials are of great interest for creating optoelectronic platforms for energy-efficient storage and processing of data and optical signals in real time. Here, photosensor and memristor structures based on graphene, graphene oxide, bismuth oxyselenide, and transition metal dichalcogenides are reviewed from the point of view of application in broadband image recognition in artificial intelligence systems for autonomous unmanned vehicles, as well as the compatibility of the formation of layered neuromorphic structures with CMOS technology. Full article
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