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Analysis and Control of Power Systems and Microgrids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1160

Special Issue Editors


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Guest Editor
Center for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: renewable energy; photovoltaic; energy storage; power electronics; microgrids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark
Interests: microgrids; space power systems; psychobiology; brain networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Because of the limited reserve of fossil fuels as well as due to their impact on the globe, renewable energy sources have been and are to become more of a concern to the research community and industry. Improving the integration of renewable energy sources in electrical power systems, as well as promoting microgrids, are key to producing affordable energy and ensuring a greener future. Nevertheless, the transition to those renewable energies comes with some technical and, therefore, economical challenges.

This Special Issue aims to present and disseminate the most recent advances related to the theory, modelling, design, application, and control of electric power systems and microgrids.

Topics of interest for publication include, but are not limited to:

  • Sizing, sitting, and planning of renewable energy-powered microgrids and electric power systems.
  • Voltage and frequency analysis in weak grid conditions.
  • Related controls of V2’X’, where ‘X’ could be ‘H’ for vehicle-to-house, or vehicle-to-grid (V2G) control.
  • Power to X concepts.
  • Energy management systems and optimizations for multi-source microgrids.
  • The impact of renewable energy resources on power system dynamics and stability.
  • Electronics based-power systems.
  • Electrical power system protection.

Dr. Abderezak Lashab
Prof. Dr. Juan C. Vasquez
Prof. Dr. Josep M. Guerrero
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

  • microgrids
  • electric power system
  • power to x
  • stability analysis
  • energy management systems
  • protection
  • V2G

Published Papers (1 paper)

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Research

16 pages, 6617 KiB  
Article
A Deep GMDH Neural-Network-Based Robust Fault Detection Method for Active Distribution Networks
by Özgür Çelik, Jalal Sahebkar Farkhani, Abderezak Lashab, Josep M. Guerrero, Juan C. Vasquez, Zhe Chen and Claus Leth Bak
Energies 2023, 16(19), 6867; https://doi.org/10.3390/en16196867 - 28 Sep 2023
Viewed by 783
Abstract
The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To [...] Read more.
The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs. Full article
(This article belongs to the Special Issue Analysis and Control of Power Systems and Microgrids)
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