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Special Issue "Challenges and Opportunities in Modern Power Electronics"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Power Electronics".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 5646

Special Issue Editors

Prof. Dr. Gianpaolo Vitale
E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking, National Research Council, 90146 Palermo, Italy
Interests: power electronics; renewable energy sources; electromagnetic compatibility; electric vehicles; storage systems; artificial intelligence applications
Special Issues, Collections and Topics in MDPI journals
Prof. Giuseppe Lullo
E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Universita' di Palermo, Viale delle Scienze, Edif.9, 90128 Palermo PA, Italy
Interests: power electronics; microfabrication techniques; characterization of electronic and photonic devices

Special Issue Information

Dear Colleagues,

Power electronics applications are finding new market opportunities thanks to innovative devices, such as SiC and GaN-based power switches, and to new topologies. However, the exploitation of such opportunities imposes some challenges, mainly due to power density increase, cost reduction, electromagnetic compatibility, and converter optimization. Besides, the multidisciplinary aspect of modern power electronics applications requires a combination of several disciplines and technologies working together, which necessitates new perspectives on the use of artificial intelligence, optimization techniques, and big data management. 

The aim of the present Special Issue is to attract original, high-quality papers and review articles focused on the modern opportunities and applications in power electronics, but showing at the same time how the new challenges can be successfully faced and solved.

Prospective authors are invited to submit original contributions, survey papers, or tutorials to be considered for publication in this Special Issue. Topics of interest include but are not limited to the following:

Design:

wide bandgap devices applications; EMI and high-frequency design; modular converters; hybrid (linear-switching) converters; fractional charging converters; electro-thermal design; converters with non-linear inductors

Applications:

innovative lighting systems; more electric vehicles and more electric aircraft; energy saving; supercapacitor management

Optimization:

power density improvement; power converter efficiency improvement; reliability improvement; multiobjective optimization of converters (regarding their efficiency, power density, and costs)

AI applications:

big data for power electronics; AI techniques in power electronics

Prof. Gianpaolo Vitale
Prof. Giuseppe Lullo
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. 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 2200 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.

Published Papers (5 papers)

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Research

Article
Non-Linear Inductors Characterization in Real Operating Conditions for Power Density Optimization in SMPS
Energies 2021, 14(13), 3924; https://doi.org/10.3390/en14133924 - 30 Jun 2021
Cited by 3 | Viewed by 456
Abstract
The exploitation of power inductors outside their linear region in switching converters can be achieved by raising the current until a decrease in the inductance can be noticed. This allows using a smaller magnetic core, increasing the power density of the converter. On [...] Read more.
The exploitation of power inductors outside their linear region in switching converters can be achieved by raising the current until a decrease in the inductance can be noticed. This allows using a smaller magnetic core, increasing the power density of the converter. On the other hand, a detailed description of the magnetization curve including the temperature is required. Since this information is often not included in the inductor’s datasheets, this paper shows how to identify the behavior of an inductor when it is operated up to saturation and its temperature rises. In order to characterize the inductor in real operating conditions, a dedicated measurement rig was developed. It consists of a switching converter that encompasses the inductor under test and is controlled by a virtual instrument developed in LabVIEW. The characterization system was tested by retrieving the inductance and the magnetization curves vs. current for two commercial inductors at core temperatures up to 105 °C. The magnetic core was then characterized by the saturation current vs. inductance, obtaining an expression for the whole family of inductors sharing the same core. Finally, we experimentally analyzed the thermal transient of the inductors in operating conditions, confirming the fundamental role of the temperature in changing the current profiles and the core saturation condition. Full article
(This article belongs to the Special Issue Challenges and Opportunities in Modern Power Electronics)
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Article
Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks
Energies 2021, 14(8), 2118; https://doi.org/10.3390/en14082118 - 10 Apr 2021
Cited by 6 | Viewed by 664
Abstract
This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as [...] Read more.
This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstrated results show that the trained model is able to represent the transition between two steady states and furthermore reflect the frequency coupling characteristic of the grid-side current. Full article
(This article belongs to the Special Issue Challenges and Opportunities in Modern Power Electronics)
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Article
Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review
Energies 2020, 13(18), 4727; https://doi.org/10.3390/en13184727 - 11 Sep 2020
Cited by 13 | Viewed by 1467
Abstract
Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as [...] Read more.
Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research. Full article
(This article belongs to the Special Issue Challenges and Opportunities in Modern Power Electronics)
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Article
Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids
Energies 2020, 13(16), 4063; https://doi.org/10.3390/en13164063 - 06 Aug 2020
Cited by 6 | Viewed by 1121
Abstract
The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control [...] Read more.
The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures. Full article
(This article belongs to the Special Issue Challenges and Opportunities in Modern Power Electronics)
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Article
An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities
Energies 2020, 13(12), 3242; https://doi.org/10.3390/en13123242 - 23 Jun 2020
Cited by 12 | Viewed by 1306
Abstract
Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient [...] Read more.
Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms. Full article
(This article belongs to the Special Issue Challenges and Opportunities in Modern Power Electronics)
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