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Special Issue "Powering Net Zero Emissions—Selected Papers from 56th International Universities Power Engineering Conference (UPEC 2021)"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 1780

Special Issue Editors

Dr. Gobind Pillai
E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: distributed generation; energy economics; energy informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Maher Al-Greer
E-Mail Website
Guest Editor
School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, Cleveland, UK
Interests: system identification and intelligent control; power converter design and control; battery characterization
Special Issues, Collections and Topics in MDPI journals
Dr. Imran Bashir
E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: condition monitoring; acoustics metamaterials and metasurfaces; aeroengine acoustics and vibration; signal processing and instrumentation
Dr. Gill Lacey
E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: electric vehicles; smart charging; wireless charging; network modelling; battery degradation
Dr. Musbahu Muhammad
E-Mail Website
Guest Editor
School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: power electronics; transport electrification; Li-ion battery characterization; non-destructive testing

Special Issue Information

Dear Colleagues,

The 56th International Universities Power Engineering Conference (UPEC 2021) will be hosted virtually from 31st August to 3rd September 2021 by Teesside University, UK. UPEC 2021 continues the long tradition of the UPEC conferences. These conferences are particularly aimed at hosting scientific contributions presented by young researchers and Ph.D. students who meet each other, as well as with experienced researchers and professors, to discuss their research lines. The conference UPEC 2021 has the motto “Powering Net Zero Emissions”, and will deal in particular with research topics in power engineering concerning the role of electricity for achieving sustaianble energy transition and net zero emissions.

A set of papers will be selected for submission of a substantially improved version to this Special Issue “Powering Net Zero Emissions - Selected Papers from the 56th  International Universities Power Engineering Conference (UPEC 2021)”. The topics of interest include, but are not limited to, the following:

  • Active distribution networks and virtual power plants;
  • Advanced metering infrastructures;
  • Condition monitoring and diagnostics;
  • Continuity of supply, reliability and resilience;
  • Data analytics and artificial intelligence applied to power systems;
  • Demand-side management, flexibility and demand response;
  • Distributed generation;
  • Electric vehicles and e-mobility;
  • Electrical machines and drives;
  • Electromagnetics and electrostatics;
  • Energy efficiency in buildings;
  • Energy storage;
  • Environmental impacts and targets;
  • High voltage engineering;
  • HVDC, FACTS and power electronics;
  • ICT for future electricity grids
  • Load and generation forecasting;
  • Magnetic materials for energy
  • Multi-energy systems and networks;
  • Power engineering education;
  • Power quality;
  • Power system economics and electricity markets;
  • Power system modeling and analysis;
  • Power system operations and control;
  • Power system optimization and planning;
  • Power system protection;
  • Renewable energy systems;
  • Smart grids;
  • Substation and distribution system automation;
  • Sustainable e-transition;
  • Transient analysis and EMTP modeling.

Dr. Gobind Pillai
Dr. Maher Al-Greer
Dr. Imran Bashir
Dr. Gill Lacey
Dr. Musbahu Muhammad
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.

Keywords

  • electrical power engineering
  • power systems
  • smart grids
  • renewable energy
  • distributed generation and storage

Published Papers (2 papers)

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Research

Article
New Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection
Energies 2022, 15(4), 1488; https://doi.org/10.3390/en15041488 - 17 Feb 2022
Cited by 1 | Viewed by 480
Abstract
Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of [...] Read more.
Fault diagnosis of induction motor anomalies is vital for achieving industry safety. This paper proposes a new hybrid Machine Learning methodology for induction-motor fault detection. Some of the motor parameters such as the stator currents and vibration signals provide a great deal of information about the motor’s conditions. Therefore, these signals of the motor were selected to test the proposed model. The induction motor was assessed in a laboratory under healthy, mechanical, and electrical faults with different loadings. In this study a new hybrid model was developed using the collected signals, an optimal features selection mechanism is proposed, and machine learning classifiers were trained for fault classification. The procedure is to extract some statistical features from the raw signal using Matching Pursuit (MP) and Discrete Wavelet Transform (DWT). Then, the Invasive Weed Optimization algorithm (IWO)-based optimal subset was selected to reduce the data dimension and increase the average accuracy of the model. The optimal subset of features was fed into three classification algorithms: k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), which were trained using k-fold cross-validation to distinguish between the induction motor faults. A similar strategy was performed by applying the Genetic Algorithm (GA) to compare with the performance of the proposed method. The suggested fault detection model’s performance was evaluated by calculating the Receiver Operation Characteristic (ROC) curve, Specificity, Accuracy, Precision, Recall, and F1 score. The experimental results have proved the superiority of IWO for selecting the discriminant features, which has achieved more than 99.7% accuracy. The proposed hybrid model has successfully proved its robustness for diagnosing the faults under different load conditions. Full article
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Article
Different Fuzzy Control Configurations Tuned by the Bees Algorithm for LFC of Two-Area Power System
Energies 2022, 15(2), 657; https://doi.org/10.3390/en15020657 - 17 Jan 2022
Cited by 1 | Viewed by 553
Abstract
This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the [...] Read more.
This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the best possible performance, the Bees Algorithm (BA) and other optimisation tools are used to accomplish this task. A Step Load Perturbation (SLP) of 0.2 pu is applied in area one to examine the dynamic performance of the system with the proposed controller employed as the LFC system. The supremacy of Fuzzy PIDF is proven by comparing the results with those of previous studies for the same power system. As the designed controller is required to provide reliable performance, this study is further extended to propose three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA for the LFC for the same dual-area power system. Moreover, an extensive examination of the robustness of these structures towards the parametric uncertainties of the investigated power system, considering thirteen cases, is carried out. The simulation results indicate that the contribution of the BA tuned the proposed fuzzy control structures in alleviating the overshoot, undershoot, and the settling time of the frequency in both areas and the tie-line power oscillations. Based on the obtained results, it is revealed that the lowest drop of the frequency in area one is −0.0414 Hz, which is achieved by the proposed Fuzzy PIDF tuned by the BA. It is also divulged that the proposed techniques, as was evidenced by their performance, offer a good transient response, a considerable capability for disturbance rejection, and an insensitivity towards the parametric uncertainty of the controlled system. Full article
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