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Symmetry
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Symmetry in Power and Electronic Engineering
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Symmetry in Power and Electronic Engineering

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 3563

Special Issue Editor

Dr. Ioannis P. Panapakidis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece
Interests: forecasting; machine learning; optimization; demand side management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transition of traditional power systems to smart grids is an inevitability that is accompanied by many operational, technical, economic, and environmental issues. Contemporary research in the power system sector attempts to address many of these issues. Symmetry is a term that is connected with power system modeling and analysis. For instance, fault analysis is related to the modeling framework of symmetrical components. Another challenging task is the study of symmetric architectures of machine learning models and how novel and sophisticated architectures can be applied in power system problems.

In the context of these scientific and engineering challenges, the main objective of this Special Issue is to publish new models and methodologies for addressing open issues in present power systems and future smart grids by employing the concept of symmetry in the formulation and implementation of the aforementioned models and methodologies.

We welcome submissions of multidisciplinary research and cutting-edge approaches as well as state-of-the-art papers.

Dr. Ioannis P. Panapakidis
Guest Editor

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. Symmetry is an international peer-reviewed open access monthly 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

  • power system modeling
  • power system analysis
  • power system protection
  • fault analysis
  • symmetrical components
  • computational intelligence for power systems
  • machine learning
  • deep learning applications in power systems

Published Papers (3 papers)

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Research

14 pages, 1858 KiB  
Article
GIS Partial Discharge Pattern Recognition Based on Multi-Feature Information Fusion of PRPD Image
by Kaiyang Yin, Yanhui Wang, Shihai Liu, Pengfei Li, Yaxu Xue, Baozeng Li and Kejie Dai
Symmetry 2022, 14(11), 2464; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14112464 - 21 Nov 2022
Cited by 8 | Viewed by 2176
Abstract
Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD [...] Read more.
Partial discharge (PD) pattern recognition is a critical indicator for evaluating the insulation state of gas-insulated switchgear (GIS). Aiming at the disadvantage of traditional PD pattern recognition methods, such as single feature extraction and low recognition accuracy, a pattern recognition method of PD based on multi-feature information fusion is proposed in this paper. Firstly, a recognition model based on quasi-Hausdorff distance is established according to the statistical characteristics of the phase-resolved partial discharge (PRPD) image, and then a modified convolutional neural network recognition model is established according to the image features of the PRPD image. Finally, Dempster–Shafer (D–S) evidence theory is used to fuse the two pattern recognition results and complement the advantages of the two approaches to improve the accuracy of partial discharge pattern recognition. The experimental results show that the total recognition accuracy rate of this method for four typical PD is more than 94.00%, and the recognition rate is significantly improved compared to support vector machine and normal convolution neural network. Maintaining stability in typical bipedal robots is challenging due to two main reasons. Full article
(This article belongs to the Special Issue Symmetry in Power and Electronic Engineering)
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8 pages, 1039 KiB  
Article
Polytopic Robust Stability for a Dual-Capacitor Boost Converter in Symmetric and Non-Symmetric Configurations
by Martín Antonio Rodríguez Licea
Symmetry 2022, 14(11), 2331; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14112331 - 07 Nov 2022
Cited by 4 | Viewed by 1161
Abstract
The step-up power electronic converter, which is easily implemented with two symmetric parallel-boost stages, has recently been proposed in the literature, showing considerable voltage gains with no excessive duty cycle, thus minimizing heat and other adverse effects. Its other advantages are floating-output voltage [...] Read more.
The step-up power electronic converter, which is easily implemented with two symmetric parallel-boost stages, has recently been proposed in the literature, showing considerable voltage gains with no excessive duty cycle, thus minimizing heat and other adverse effects. Its other advantages are floating-output voltage and increased power density because of the diminution of the capacitors’ voltage rating. In this paper, the Lyapunov-based robust stability of a converter operating in both closed- and open-loop is proved, showing its versatility even during the variation of parameters, which nullifies the symmetry of the converter. Simulation and experimental data allow the corroboration of the analysis. Full article
(This article belongs to the Special Issue Symmetry in Power and Electronic Engineering)
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19 pages, 3693 KiB  
Article
A Metaheuristics-Based Inputs Selection and Training Set Formation Method for Load Forecasting
by Ioannis Panapakidis, Michail Katsivelakis and Dimitrios Bargiotas
Symmetry 2022, 14(8), 1733; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14081733 - 19 Aug 2022
Cited by 4 | Viewed by 1050
Abstract
Load forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the [...] Read more.
Load forecasting is a procedure of fundamental importance in power systems operation and planning. Many entities can benefit from accurate load forecasting such as generation companies, systems operators, retailers, prosumers, and others. A variety of models have been proposed so far in the literature. Among them, artificial neural networks are a favorable approach mainly due to their potential for capturing the relationship between load and other parameters. The forecasting performance highly depends on the number and types of inputs. The present paper presents a particle swarm optimization (PSO) two-step method for increasing the performance of short-term load forecasting (STLF). During the first step, PSO is applied to derive the optimal types of inputs for a neural network. Next, PSO is applied again so that the available training data is split into homogeneous clusters. For each cluster, a different neural network is utilized. Experimental results verify the robustness of the proposed approach in a bus load forecasting problem. Also, the proposed algorithm is checked on a load profiling problem where it outperforms the most common algorithms of the load profiling-related literature. During input selection, the weights update is held in asymmetrical duration. The weights of the training phase require more time compared with the test phase. Full article
(This article belongs to the Special Issue Symmetry in Power and Electronic Engineering)
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