Special Issue "Feature Papers in Industrial Electronics"

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

Deadline for manuscript submissions: 31 December 2022 | Viewed by 2594

Special Issue Editors

Dr. Jahangir Hossain
E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Sydney 2000, Australia
Interests: renewable energy integration and stabilization; voltage stability; micro grids and smart grids; robust control; electric vehicles; building energy management systems; energy storage systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mohamed Benbouzid
E-Mail Website
Guest Editor
Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Interests: tidal and wave power; wind power; microgrids; energy management; fault detection and diagnosis; fault-tolerant control
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Mussetta
E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, 20133 Milano, MI, Italy
Interests: computational intelligence; optimization; machine learning; fuzzy logic; wireless sensor networks; renewable energy; photovoltaics; wind power; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The growing commitment of governments, businesses, and industries towards net zero has provided new opportunities and challenges for energy sectors. The key drivers for energy transition are electrifications, technological innovations, sector couplings, and the introduction of new market structures, e.g., renewable generators, energy storage systems, electric vehicles, and hydrogen fuel cells. There are several challenges regarding the energy transition, for example, energy mix and grid flexibility requirements, the need an electric vehicle (EV) charging infrastructure, a longer-term backup capacity, and managing the impacts of new technologies such as a distributed generation, energy storage systems, and hydrogen fuel cells.

This Special Issue aims to address the emerging and broader issues for a reliable and sustainable operation of power and energy systems and provide a platform to improve interdisciplinary research, sharing the most recent ideas.

The topics of interest for the Special Issue include, but are not limited to, the following:

  1. Demand response and demand-side management in green energy for sustainable power systems;
  2. Modeling and management of EVs;
  3. Green hydrogen technology and integrations with the power network;
  4. Clean energy and recycling;
  5. Cyber-physical systems and wide area monitoring;
  6. Demand flexibility and minimum demand;
  7. Electrifications of fossil fuel-dominated industries;
  8. Power electronics applications in power and energy sectors;
  9. Nonlinear and intelligent control techniques for smart grid;
  10. Energy storage system;
  11. Artificial intelligence and energy;
  12. New energy initiatives.

Prof. Dr. S. M. Muyeen
Dr. Jahangir Hossain
Prof. Dr. Mohamed Benbouzid
Prof. Dr. Antonio J. Marques Cardoso
Dr. Marco Mussetta
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 2000 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

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Article
Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Alternating Current Networks
Electronics 2022, 11(8), 1287; https://doi.org/10.3390/electronics11081287 - 18 Apr 2022
Viewed by 285
Abstract
In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks [...] Read more.
In this paper, we solve the optimal power flow problem in alternating current networks to reduce power losses. For that purpose, we propose a master–slave methodology that combines the multiverse optimization algorithm (master stage) and the power flow method for alternating current networks based on successive approximation (slave stage). The master stage determines the level of active power to be injected by each distributed generator in the network, and the slave stage evaluates the impact of the proposed solution on each distributed generator in terms of the objective function and the constraints. For the simulations, we used the 10-, 33-, and 69-node radial test systems and the 10-node mesh test system with three levels of distributed generation penetration: 20%, 40%, and 60% of the power provided by the slack generator in a scenario without DGs. In order to validate the robustness and convergence of the proposed optimization algorithm, we compared it with four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: Particle Swarm Optimization, the Continuous Genetic Algorithm, the Black Hole Optimization algorithm, and the Ant Lion Optimization algorithm. The results obtained demonstrate that the proposed master–slave methodology can find the best solution (in terms of power loss reduction, repeatability, and technical conditions) for networks of any size while offering excellent performance in terms of computation time. Full article
(This article belongs to the Special Issue Feature Papers in Industrial Electronics)
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Article
Comparative Study of Discrete PI and PR Controller Implemented in SRG for Wind Energy Application: Theory and Experimentation
Electronics 2022, 11(8), 1285; https://doi.org/10.3390/electronics11081285 - 18 Apr 2022
Viewed by 309
Abstract
The Switched Reluctance Generator (SRG) has been widely studied for Wind Energy Conversion Systems (WECS). However, a major drawback of the SRG system adopting the conventional control is the slow response of the DC link voltage controller. In this paper, a Proportional Resonant [...] Read more.
The Switched Reluctance Generator (SRG) has been widely studied for Wind Energy Conversion Systems (WECS). However, a major drawback of the SRG system adopting the conventional control is the slow response of the DC link voltage controller. In this paper, a Proportional Resonant (PR) control strategy is proposed to control the output voltage of the SRG system to improve the fast response. The SRG model has a high non-linearity, which makes the design of controllers a difficult task. For this reason, the important practical engineering aspect of this work is the role played by the SRG model linearization in testing the sensitivity of the PR controller performance to specific parameter changes. The characteristics of steady-state behaviors of the SRG-based WECS under different control approaches are simulated and compared. The controller is implemented on a digital signal processor (TMS320F28379D). The experimental results are carried out using a 250 W 8/6 SRG prototype to assess the performance of the proposed control compared with the traditional Proportional Integral (PI) control strategy. The experimental results show that the PR control enhances the steady-state performance of the SR power generation system in WECS. Compared to PI control, the rise and settling times are reduced by 45% and 43%, respectively, without an overshoot. Full article
(This article belongs to the Special Issue Feature Papers in Industrial Electronics)
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Article
A Risk Curtailment Strategy for Solar PV-Battery Integrated Competitive Power System
Electronics 2022, 11(8), 1251; https://doi.org/10.3390/electronics11081251 - 15 Apr 2022
Viewed by 311
Abstract
Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers [...] Read more.
Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer’s point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system’s risk, hence enhancing the system’s reliability and societal welfare. As a result, it will benefit both supply and demand-side participants. Full article
(This article belongs to the Special Issue Feature Papers in Industrial Electronics)
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Article
Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification
Electronics 2022, 11(5), 729; https://doi.org/10.3390/electronics11050729 - 26 Feb 2022
Viewed by 410
Abstract
Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when [...] Read more.
Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when an analytical model is not feasible. One of the many model architectures of system identification is to utilize a Local Model Network (LMN). Hierarchical Local Model Tree (HILOMOT) is an iterative LMN training algorithm that uses the axis-oblique split method to divide the input space hierarchically. The split positions of the local models directly influence the accuracy of the entire model. However, finding the best split positions of the local models presents a nonlinear optimization problem. This paper presents an optimized HILOMOT algorithm with enhanced Expectation–Maximization (EM) and Particle Swarm Optimization (PSO) algorithms which includes the normalization parameter and utilizes the reduced-parameter vector. Finally, the performance of the improved HILOMOT algorithm is compared with the existing algorithm by modeling the NOx emission model of a gas turbine and multiple nonlinear test functions of different orders and structures. Full article
(This article belongs to the Special Issue Feature Papers in Industrial Electronics)
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Review

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Review
A Systematic Guide for Predicting Remaining Useful Life with Machine Learning
Electronics 2022, 11(7), 1125; https://doi.org/10.3390/electronics11071125 - 01 Apr 2022
Viewed by 714
Abstract
Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. [...] Read more.
Prognosis and health management (PHM) are mandatory tasks for real-time monitoring of damage propagation and aging of operating systems during working conditions. More definitely, PHM simplifies conditional maintenance planning by assessing the actual state of health (SoH) through the level of aging indicators. In fact, an accurate estimate of SoH helps determine remaining useful life (RUL), which is the period between the present and the end of a system’s useful life. Traditional residue-based modeling approaches that rely on the interpretation of appropriate physical laws to simulate operating behaviors fail as the complexity of systems increases. Therefore, machine learning (ML) becomes an unquestionable alternative that employs the behavior of historical data to mimic a large number of SoHs under varying working conditions. In this context, the objective of this paper is twofold. First, to provide an overview of recent developments of RUL prediction while reviewing recent ML tools used for RUL prediction in different critical systems. Second, and more importantly, to ensure that the RUL prediction process from data acquisition to model building and evaluation is straightforward. This paper also provides step-by-step guidelines to help determine the appropriate solution for any specific type of driven data. This guide is followed by a classification of different types of ML tools to cover all the discussed cases. Ultimately, this review-based study uses these guidelines to determine learning model limitations, reconstruction challenges, and future prospects. Full article
(This article belongs to the Special Issue Feature Papers in Industrial Electronics)
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