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Electronics
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Celebrating the 200th Anniversary of the University of Manchester—Electrical and...
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Celebrating the 200th Anniversary of the University of Manchester—Electrical and Electronic Engineering

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1998

Special Issue Editors

Prof. Dr. Zhengtao Ding

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: nonlinear and adaptive control theory and their applications; more recently network-based control; distributed optimization and distributed learning with applications to power systems and robotics
Dr. Zhenhong Li

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: physical human-robot interaction for rehabilitation and industrial robots; multimodal brain-computer interfaces for robot control and brain monitoring
Dr. Alessandra Parisio

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: energy management systems under uncertainty; model predictive control; stochastic constrained control and distributed optimization for power systems

Special Issue Information

Dear Colleagues,

Since its establishment in 1824, the University of Manchester (UoM) has been at the forefront of fostering knowledge. With milestones such as the development of the world's first stored-program computer and the groundbreaking discovery of graphene, the university has consistently showcased its commitment to pioneering research and academic excellence.

Marking the 200th anniversary of the UoM, this Special Issue aims to highlight state-of-the-art research in Electrical and Electronic Engineering. We invite submissions that delve into the recent advancements in, but not limited to, smart buildings, power systems, robotics, and control. Contributions may encompass theoretical innovations, experimental investigations, or comprehensive reviews of a specific area.

Prof. Dr. Zhengtao Ding
Dr. Zhenhong Li
Dr. Alessandra Parisio
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

  • smart buildings and microgrids
  • renewable energy
  • sustainable and decarbonised power systems
  • multi-energy networks
  • data and ICT in power and energy systems
  • control theory and application
  • network control and optimization
  • robotics and assistive technology
  • human–robot interaction
  • communication theory
  • 5G network
  • electrical drives

Published Papers (3 papers)

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Research

16 pages, 4142 KiB  
Article
A Novel Active Anti-Disturbance Control Strategy for Unmanned Aerial Manipulator Based on Variable Coupling Disturbance Compensation
by Hai Li, Zhan Li, Tong Wu, Chen Dong, Quman Xu, Yipeng Yang and Xinghu Yu
Electronics 2024, 13(8), 1477; https://doi.org/10.3390/electronics13081477 - 13 Apr 2024
Viewed by 306
Abstract
Inspired by the kangaroo’s active tail wagging to stabilize its body posture while jumping, this paper proposes an active anti-disturbance control strategy for unmanned aerial manipulators based on variable coupling disturbance compensation (AADCVCD), which can achieve the active and energy-saving [...] Read more.
Inspired by the kangaroo’s active tail wagging to stabilize its body posture while jumping, this paper proposes an active anti-disturbance control strategy for unmanned aerial manipulators based on variable coupling disturbance compensation (AADCVCD), which can achieve the active and energy-saving anti-disturbance performance of “using the enemy’s strength against the enemy” to keep the UAM stable under disturbances. First, the goal of using the coupling disturbance generated by the active swing of the manipulator as a control input signal for active anti-disturbance is clarified. Then, based on the proposed variable coupling disturbance model, this goal is formulated as a nonlinear programming optimization problem under specific physical constraints and solved. Finally, the coupling disturbance torque generated when the manipulator executes an active swing to the solved desired joint angles can be used to compensate and suppress other disturbances of the UAM, thereby achieving active anti-disturbance. The effectiveness and superiority of the proposed AADCVCD were validated through two simulations in Simscape. The simulation results demonstrated that our approach achieved a good active anti-disturbance and energy-saving performance, significantly reducing the position offset of the UAM caused by disturbances and improving the UAM’s ability to maintain stability. Full article
(This article belongs to the Special Issue Celebrating the 200th Anniversary of the University of Manchester—Electrical and Electronic Engineering)
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15 pages, 1152 KiB  
Article
TabNet: Locally Interpretable Estimation and Prediction for Advanced Proton Exchange Membrane Fuel Cell Health Management
by Benyuan Zhang, Xin Jin, Wenyu Liang, Xiaoyu Chen, Zhenhong Li, George Panoutsos, Zepeng Liu and Zezhi Tang
Electronics 2024, 13(7), 1358; https://doi.org/10.3390/electronics13071358 - 03 Apr 2024
Viewed by 477
Abstract
In the pursuit of advanced Predictive Health Management (PHM) for Proton Exchange Membrane Fuel Cells (PEMFCs), conventional data-driven models encounter considerable barriers due to data reconstruction resulting in poor data quality, and the complexity of models leading to insufficient interpretability. In addressing these [...] Read more.
In the pursuit of advanced Predictive Health Management (PHM) for Proton Exchange Membrane Fuel Cells (PEMFCs), conventional data-driven models encounter considerable barriers due to data reconstruction resulting in poor data quality, and the complexity of models leading to insufficient interpretability. In addressing these challenges, this research introduces TabNet, a model aimed at augmenting predictive interpretability, and integrates it with an innovative data preprocessing technique to enhance the predictive performance of PEMFC health management. In traditional data processing approaches, reconstruction methods are employed on the original dataset, significantly reducing its size and consequently diminishing the accuracy of model predictions. To overcome this challenge, the Segmented Random Sampling Correction (SRSC) methodology proposed herein effectively eliminates noise from the original dataset whilst maintaining its effectiveness. Notably, as the majority of deep learning models operate as black boxes, it becomes challenging to identify the exact factors affecting the Remaining Useful Life (RUL) of PEMFCs, which is clearly disadvantageous for the health management of PEMFCs. Nonetheless, TabNet offers insights into the decision-making process for predicting the RUL of PEMFCs, for instance, identifying which experimental parameters significantly influence the prediction outcomes. Specifically, TabNet’s distinctive design employs sequential attention to choose features for reasoning at each decision-making step, not only enhancing the accuracy of RUL predictions in PEMFC but also offering interpretability of the results. Furthermore, this study utilized Gaussian augmentation techniques to boost the model’s generalization capability across varying operational conditions. Through pertinent case studies, the efficacy of this integrated framework, merging data processing with the TabNet architecture, was validated. This work not only evidences that the effective data processing and strategic deployment of TabNet can markedly elevate model performance but also, via a visual analysis of the parameters’ impact, provides crucial insights for the future health management of PEMFCs. Full article
(This article belongs to the Special Issue Celebrating the 200th Anniversary of the University of Manchester—Electrical and Electronic Engineering)
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19 pages, 533 KiB  
Article
Evolutionary Game Dynamics between Distributed Energy Resources and Microgrid Operator: Balancing Act for Power Factor Improvement
by Mukesh Gautam
Electronics 2024, 13(2), 248; https://doi.org/10.3390/electronics13020248 - 05 Jan 2024
Viewed by 731
Abstract
This article investigates the intricate dynamics between Distributed Energy Resources (DERs) and the Microgrid Operator (MGO) within a microgrid interconnected with the main grid. Employing an evolutionary game framework, the study scrutinizes the strategic evolution of DERs’ decision-making processes in their interactions with [...] Read more.
This article investigates the intricate dynamics between Distributed Energy Resources (DERs) and the Microgrid Operator (MGO) within a microgrid interconnected with the main grid. Employing an evolutionary game framework, the study scrutinizes the strategic evolution of DERs’ decision-making processes in their interactions with the MGO. Modeled as an evolutionary game, these interactions encapsulate the strategies adopted by DERs, resulting in stable equilibrium strategies over time. Motivated by direct benefits linked to increased active power production, DERs strive to sell all available power, while the MGO focuses on optimizing the microgrid’s overall performance. The study assesses the microgrid’s performance in terms of its power factor, emphasizing the strategic balance DERs must achieve in their active power generation to avoid penalization. This penalization results in decreased individual utility for DERs due to the overall power factor decrease resulting from their prioritization of active power generation. Additionally, the diminished overall power factor implies a decrease in MGO utility. The individual utility of each DER is further influenced by the strategies adopted by other DERs, impacting the penalization factor. Leveraging a modified IEEE 13-node distribution microgrid consisting of three DERs, the study presents case studies encompassing both cooperative and non-cooperative evolutionary game scenarios. These case studies illuminate the intricacies of interactions and the resulting equilibrium outcomes. Full article
(This article belongs to the Special Issue Celebrating the 200th Anniversary of the University of Manchester—Electrical and Electronic Engineering)
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