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Modern Power System Operations, Control, and Measurement

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 2165

Special Issue Editors


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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

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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

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Guest Editor
School of Electrical, Electronic and Control Engineering, Changwon University, 20 Changwondaehak-ro, Uichang-gu, Gyeongsangnam-do, Changwon 51140, Korea
Interests: power system economics; power system analysis; renewable energy; optimization; integrated energy system planning and operation

Special Issue Information

Dear Colleagues,

Much attention is paid to renewable energy in modern power systems as an approach to reducing net zero emissions by 2050. In order to efficiently integrate a high portion of renewable energy, improvements in power system operation and new smart grid technologies have been appeared in research. Instead of a centralized operation in conventional power systems, a decentralized operation is actively considered for sustainable power systems and the self-sufficient supply of energy. With an increase in renewable energy, electricity markets evolve to support power system operations by developing the flexible ramping market. At the same time, for efficient and reliable power systems, there is a growing emphasis on smart grid technologies and application methods such as big data analysis, condition monitoring and diagnosis of power system equipment, and industrial IoT (internet of things). 

The objective of this Special Issue is to address power system operations, control and market topics in modern power systems with new technologies. We look forward to considering your submissions.

Prof. Dr. Hyeongon Park
Prof. Dr. Chun-Kwon Lee
Prof. Dr. Woong Ko
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 2600 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

  • novel smart grid technologies
  • sector coupling of power and other energy
  • research and application of decentralized power system operation
  • electricity market operation and design
  • power system prognostics and health management (PHM)
  • deep learning techniques for fault detection and diagnosis
  • measurement and signal processing

Published Papers (1 paper)

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Research

18 pages, 7007 KiB  
Article
Development of Data Cleaning and Integration Algorithm for Asset Management of Power System
by Jae-Sang Hwang, Sung-Duk Mun, Tae-Joon Kim, Geun-Won Oh, Yeon-Sub Sim and Seung Jin Chang
Energies 2022, 15(5), 1616; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051616 - 22 Feb 2022
Cited by 4 | Viewed by 1815
Abstract
Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of [...] Read more.
Asset management technology is rapidly growing in the electric power industry because utilities are paying attention to which of their aged assets should be replaced first. The global trend of asset management follows risk management that comprehensively considers the probability and consequences of failures. In the asset management system, the risk assessment algorithm operates by interfacing digital datasets from various legacy systems. In this study, among the various electric power assets, we consider transmission cable systems as a representative linear asset consisting of different segments. First, the configurations and characteristics of linear asset datasets are analyzed. Second, six types of data cleaning functions are proposed for extracting dirty data from the entire dataset. Third, three types of data integration functions are developed to simulate the risk assessment algorithm. This technique supports the integration of distributed asset data in various legacy systems into one dataset. Finally, an automatic data cleaning and integration system is developed and the algorithm could repeat the cleaning and integration process until data quality is satisfied. To evaluate the performance of the proposed system, an automatic cleaning process is demonstrated using actual legacy datasets. Full article
(This article belongs to the Special Issue Modern Power System Operations, Control, and Measurement)
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