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Electronics
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Machine Learning in Power System Monitoring and Control
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Machine Learning in Power System Monitoring and Control

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 3434

Special Issue Editors

Dr. Seung Jin Chang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Hanbat National University, Daejeon 34158, Republic of Korea
Interests: artificial neural networks; cable insulation; Fault Diagnosis; hierarchy clustering; reflectometry; signal crosstalk; Time-Frequency Analysis
Prof. Dr. Chun-Kwon Lee

E-Mail Website
Guest Editor
Department of Control and Instrumentation Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: fault diagnosis; time-frequency analysis; power system faults; signal processing; condition monitoring; pattern recognition; big-data analysis; power system protection
Special Issues, Collections and Topics in MDPI journals
Dr. Hyeongon Park

E-Mail Website
Guest Editor
Department of Safety Engineering, Pukyong National University, Busan 48513, Republic of Korea
Interests: power system operation; electricity market; renewable energy; power system optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scope and aim of the "Machine Learning in Power System Monitoring and Control" field include the application of machine learning theories and algorithms in power system monitoring and control to improve system reliability, stability, and efficiency. The goal is to promote the development of new machine learning techniques and methodologies to enhance power system operation and maintenance, and disseminate knowledge among experts in the field. As an editor for a special issue related to this field, the objective is to support researchers in publishing innovative and groundbreaking research, advancing the use of machine learning in power systems, and fostering knowledge-sharing among experts.

Dr. Seung Jin Chang
Prof. Dr. Chun-Kwon Lee
Dr. Hyeongon Park
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

  • power system protection
  • fault detection
  • condition monitoring
  • power system operation

Published Papers (2 papers)

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Research

26 pages, 1146 KiB  
Article
Power Transformer Fault Diagnosis Based on Improved BP Neural Network
by Yongshuang Jin, Hang Wu, Jianfeng Zheng, Ji Zhang and Zhi Liu
Electronics 2023, 12(16), 3526; https://doi.org/10.3390/electronics12163526 - 21 Aug 2023
Cited by 3 | Viewed by 1932
Abstract
Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it [...] Read more.
Power transformers are complex and extremely important piece of electrical equipment in a power system, playing an important role in changing voltage and transmitting electricity. Its operational status directly affects the stability and safety of power grids, and once a fault occurs, it may lead to significant economic losses and social impacts. The traditional detection methods rely on the technical level of power system operation and maintenance personnel, and are based on Dissolved Gas Analysis (DGA) technology, which analyzes the components of dissolved gases in transformer oil for preliminary fault diagnosis. However, with the increasing accuracy and intelligence requirements for transformer fault diagnosis in power grids, the DGA analysis method is no longer able to meet the requirements. Therefore, this article proposes an improved transformer fault diagnosis method based on a residual BP neural network. This method deepens the BP neural network by stacking multiple residual network modules, and fuses and expands gas feature information through an improved BP neural network. In the improved residual BP neural network, SVM is introduced to judge the extracted feature vectors at each layer, screen out feature vectors with high accuracy, and increase their weights. The feature vector with the highest cumulative weight is selected as an input for transformer fault diagnosis. This method utilizes multi-layer neural network mapping to extract gas feature information with more significant feature differences after fusion expansion, thereby effectively improving diagnostic accuracy. The experimental results show that, compared with traditional BP neural network methods, the proposed algorithm has higher accuracy in transformer fault diagnosis, with an accuracy rate of 92%, which can ensure the sustainable, normal, and safe operation of power grids. Full article
(This article belongs to the Special Issue Machine Learning in Power System Monitoring and Control)
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16 pages, 2902 KiB  
Article
Selection of TCC Curve and Protection Cooperation Method of Distribution Line Using Linear Optimization
by Geonho Kim, Woo-Hyun Kim and Chun-Kwon Lee
Electronics 2023, 12(12), 2705; https://doi.org/10.3390/electronics12122705 - 16 Jun 2023
Viewed by 1108
Abstract
Distribution systems are mostly composed of radial structures, which are susceptible to an increased variability and complexity of system operation due to frequent line changes during operation. When multiple changes in distribution lines occur simultaneously, the relative positions of protective devices also change. [...] Read more.
Distribution systems are mostly composed of radial structures, which are susceptible to an increased variability and complexity of system operation due to frequent line changes during operation. When multiple changes in distribution lines occur simultaneously, the relative positions of protective devices also change. The existing protection coordination method of distribution lines is configured by considering the operation characteristics and coordination time interval (CTI) of all protective devices in series from the substation to the terminal load. Therefore, the protection coordination algorithm needs to be redesigned whenever a line is changed or a protective device is added to the distribution line for which the existing protection coordination algorithm has been set. In addition, existing protection coordination methods require complex calculations and procedures, which are subject to human errors and are less feasible for responding in real-time to changes in the distribution system. In this paper, we propose the adaptive time–current curve (TCC) method by selecting the time dial setting (TDS) and minimum response time (MRT) of individual protective devices in accordance with the relative distance based on the linear optimization technique. Using PSCAD/EMTDC, a power system analysis program, the minimum operating current and the fault current of each protective device are obtained, and the proposed protection coordination algorithm is verified according to the series configuration relationship of the protective devices. Finally, the proposed method is applied to an actual distribution line to verify the improvement over the existing protection coordination. Full article
(This article belongs to the Special Issue Machine Learning in Power System Monitoring and Control)
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