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New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems
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New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 10875

Special Issue Editors

Prof. Dr. Jahangir Hossain

E-Mail Website
Guest Editor
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, NSW 2007 (PO Box 123), Australia
Interests: renewable energy integration and stabilization; voltage stability; micro grids and smart grids; robust control; electric vehicles; building energy management systems; battery energy storage systems and distributed generations
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: fault detection and diagnosis; failure prognosis; cyberattack detection; fault-resilient control; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio J. Marques Cardoso

E-Mail Website
Guest Editor
CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P - 6201-001 Covilhã, Portugal
Interests: diagnosis and fault tolerance of electrical machines; power electronics and drives
Special Issues, Collections and Topics in MDPI journals
Dr. Seyedfoad F. Taghizadeh

E-Mail Website
Guest Editor
Department of Electrical Engineering, Macquarie University, 44 Waterloo Rd, Macquarie Park, NSW 2113, Australia
Interests: power electronics-based electric vehicle charger (plug-in/wireless), application of intelligent power sharing in power electronics and power systems, microgrid and distributed energy system integration
Dr. Sara Deilami

E-Mail Website
Guest Editor
School of Engineering/Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart grid and electric vehicle charging technology (V2G/G2V) integration; renewable energy; power system protection; power systems and power quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue titled "New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems" provides a comprehensive collection of research papers that explore cutting-edge innovations and advancements in the field of industrial electronics. This Special Issue focuses on two key areas: advanced devices and intelligent systems. It showcases the latest innovations in device technologies, including power electronics, electrical machines, sensors, actuators, and protection systems, highlighting their impact on power systems. Additionally, the Special Issue delves into the realm of intelligent power sharing and control systems, such as artificial intelligence, data analytics, machine learning, emphasising their role in optimising industrial processes, improving efficiency, power quality and enhancing productivity. The collection will provide a valuable resource database for researchers, and engineers seeking to stay updated on the forefront of industrial electronics

Prof. Dr. Jahangir Hossain
Prof. Dr. Mohamed Benbouzid
Prof. Dr. Antonio J. Marques Cardoso
Dr. Seyedfoad F. Taghizadeh
Dr. Sara Deilami
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.

Keywords

  • industrial electronics
  • advanced devices
  • power electronics
  • intelligent systems
  • artificial intelligence
  • data analytics
  • machine learning

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Published Papers (4 papers)

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27 pages, 4750 KiB  
Article
Dynamic Gust Detection and Conditional Sequence Modeling for Ultra-Short-Term Wind Speed Prediction
by Liwan Zhou, Di Zhang, Le Zhang and Jizhong Zhu
Electronics 2024, 13(22), 4513; https://doi.org/10.3390/electronics13224513 - 18 Nov 2024
Viewed by 703
Abstract
As the foundation for optimizing wind turbine operations and ensuring energy stability, wind speed forecasting directly impacts the safe operation of the power grid, the rationality of grid planning, and the balance of supply and demand. Furthermore, gust events, characterized by sudden and [...] Read more.
As the foundation for optimizing wind turbine operations and ensuring energy stability, wind speed forecasting directly impacts the safe operation of the power grid, the rationality of grid planning, and the balance of supply and demand. Furthermore, gust events, characterized by sudden and rapid wind speed fluctuations, pose significant challenges for ultra-short-term wind speed forecasting, making the data more complex and thus harder to predict accurately. To address this issue, this paper proposes a novel hybrid model that combines dynamic gust detection with Conditional Long Short-Term Memory (Conditional LSTM) and incorporates dynamic window adjustment and wind speed difference threshold screening methods. The model dynamically adjusts the window size to accurately detect gust events and uses a conditional LSTM model to adjust predictions based on gust and non-gust conditions. Experimental results show that the proposed model exhibits higher prediction accuracy across various wind speed scenarios, particularly during gust events. Through detailed experiments using data from a single actual wind farm, the effectiveness and practicality of the proposed hybrid model are demonstrated. The experimental results indicate that the proposed model outperforms contrast models, especially in handling gust events, significantly enhancing the robustness of ultra-short-term wind speed predictions. Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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20 pages, 3252 KiB  
Article
UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings
by Tarek Berghout and Mohamed Benbouzid
Electronics 2024, 13(12), 2419; https://doi.org/10.3390/electronics13122419 - 20 Jun 2024
Cited by 4 | Viewed by 1355
Abstract
Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions [...] Read more.
Maintenance planning is crucial for efficient operation of wind turbines, particularly in harsh conditions where degradation of critical components, such as bearings, can lead to costly downtimes and safety threats. In this context, prognostics of degradation play a vital role, enabling timely interventions to prevent failures and optimize maintenance schedules. Learning systems-based vibration analysis of bearings stands out as one of the primary methods for assessing wind turbine health. However, data complexity and challenging conditions pose significant challenges to accurate degradation assessment. This paper proposes a novel approach, Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion (UBO-EREX), which combines Extreme Learning Machines (ELM), a lightweight neural network, with Recurrent Expansion algorithms, a recently advanced representation learning technique. The UBO-EREX algorithm leverages Bayesian optimization to optimize its parameters, targeting uncertainty as an objective function to be minimized. We conducted a comprehensive study comparing UBO-EREX with basic ELM and a set of time-series adaptive deep learners, all optimized using Bayesian optimization with prediction errors as the main objective. Our results demonstrate the superior performance of UBO-EREX in terms of approximation and generalization. Specifically, UBO-EREX shows improvements of approximately 5.1460 ± 2.1338% in the coefficient of determination of generalization over deep learners and 5.7056% over ELM, respectively. Moreover, the objective search time is significantly reduced with UBO-EREX with 99.7884 ± 0.2404% over deep learners, highlighting its effectiveness in real-time degradation assessment of wind turbine bearings. Overall, our findings underscore the significance of incorporating uncertainty-aware UBO-EREX in predictive maintenance strategies for wind turbines, offering enhanced accuracy, efficiency, and robustness in degradation assessment. Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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17 pages, 9418 KiB  
Article
Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification
by Jinhua Zhang, Xin Liu and Jie Yan
Electronics 2024, 13(12), 2293; https://doi.org/10.3390/electronics13122293 - 12 Jun 2024
Viewed by 944
Abstract
The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind [...] Read more.
The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms. Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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25 pages, 2474 KiB  
Systematic Review
Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency
by Paul Arévalo, Danny Ochoa-Correa and Edisson Villa-Ávila
Electronics 2024, 13(18), 3754; https://doi.org/10.3390/electronics13183754 - 21 Sep 2024
Cited by 3 | Viewed by 6290
Abstract
Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies [...] Read more.
Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions. Full article
(This article belongs to the Special Issue New Insights in Industrial Electronics: Advanced Devices and Intelligent Systems)
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