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Energies
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Optimization and Fault Detection in Smart Power Grids
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Optimization and Fault Detection in Smart Power Grids

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 (30 September 2021) | Viewed by 4032

Special Issue Editors

Prof. Dr. Alfredo Alcayde

E-Mail Website
Guest Editor
Department of Engineering, Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain
Interests: power engineering; optimization techniques; ICT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Consolación Gil

E-Mail Website
Guest Editor
Departamento de Informática, CITE III. Universidad de Almería, La Cañada de San Urbano s/n, 04120 Almería, Spain
Interests: engineering optimization; embedded systems; renewable energy; power meters; network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The smart grid is experiencing exponential growth mainly due to the great penetration of renewable energy into electric power systems and the integration of electric vehicles with the network. However, due to the intermittent nature of renewable energy sources in electric vehicles of small producers / self-consumers, optimization is one of the main tools for its design and maintenance, minimizing costs, losses of energy, etc.

Most networks have installations that are interconnected and require intelligence while maintaining proper working order, so fault detection to perform the repair and optimization processes to minimize its impact on the network are important.

This Special Issue will collect original research or review articles on the main technological advances in the design and maintenance of intelligent networks, in order to improve their planning, organization, costs, and intelligence covering any optimization technique, such as bioinspired, metaheuristics, support vector machines, etc.

Dr. Alfredo Alcayde
Prof. Dr. Consolación Gil
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

  • optimization
  • smart grids
  • grid monitoring and measurement
  • automatic generation control
  • demand response
  • fault detection
  • solar photovoltaics
  • wind power
  • electric vehicles
  • metaheuristics
  • support vector machines

Published Papers (2 papers)

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Research

15 pages, 1537 KiB  
Article
A Data Driven Approach to Robust Event Detection in Smart Grids Based on Random Matrix Theory and Kalman Filtering
by Fujia Han, Phillip M. Ashton, Maozhen Li, Ioana Pisica, Gareth Taylor, Barry Rawn and Yi Ding
Energies 2021, 14(8), 2166; https://doi.org/10.3390/en14082166 - 13 Apr 2021
Cited by 5 | Viewed by 2016
Abstract
Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further [...] Read more.
Increasing levels of complexity, due to growing volumes of renewable generation with an associated influx of power electronics, are placing increased demands on the reliable operation of modern power systems. Consequently, phasor measurement units (PMUs) are being rapidly deployed in order to further enhance situational awareness for power system operators. This paper presents a novel data-driven event detection approach based on random matrix theory (RMT) and Kalman filtering. A dynamic Kalman filtering technique is proposed to condition PMU data. Both simulated and real PMU data from the transmission system of Great Britain (GB) are utilized in order to validate the proposed event detection approach and the results show that the proposed approach is much more robust with regard to event detection when applied in practical situations. Full article
(This article belongs to the Special Issue Optimization and Fault Detection in Smart Power Grids)
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19 pages, 1640 KiB  
Article
Solution to Fault of Multi-Terminal DC Transmission Systems Based on High Temperature Superconducting DC Cables
by Chun-Kwon Lee, Gyu-Sub Lee and Seung-Jin Chang
Energies 2021, 14(5), 1292; https://doi.org/10.3390/en14051292 - 26 Feb 2021
Cited by 3 | Viewed by 1386
Abstract
In this paper, we developed the small-signal state-space (SS) model of hybrid multi-terminal high-voltage direct-current (HVDC) systems and fault localization method in a failure situation. The multi-terminal HVDC (MTDC) system is composed of two wind farm side voltage-source converters (VSCs) and two grid [...] Read more.
In this paper, we developed the small-signal state-space (SS) model of hybrid multi-terminal high-voltage direct-current (HVDC) systems and fault localization method in a failure situation. The multi-terminal HVDC (MTDC) system is composed of two wind farm side voltage-source converters (VSCs) and two grid side line-commutated converters (LCCs). To utilize relative advantages of the conventional line-commutated converter (LCC) and the voltage source converter (VSC) technologies, hybrid multi-terminal high-voltage direct-current (MTDC) technologies have been highlighted in recent years. For the models, grid side LCCs adopt distinct two control methods: master–slave control mode and voltage droop control mode. By utilizing root-locus analysis of the SS models for the hybrid MTDC system, we compare stability and responses of the target system according to control method. Furthermore, the proposed SS models are utilized in time-domain simulation to illustrate difference between master–slave control method and voltage droop control method. However, basic modeling method for hybrid MTDC system considering superconducting DC cables has not been proposed. In addition, when a failure occurs in MTDC system, conventional fault localization method cannot detect the fault location because the MTDC system is a complex form including a branch point. For coping with a failure situation, we propose a fault localization method for MTDC system including branch points. We model the MTDC system based on the actual experimental results and simulate a variety of failure scenarios. We propose the fault localization topology on a branch cable system using reflectometry method. Through the simulation results, we verify the performance of fault localization. In conclusion, guidelines to select control method in implementing hybrid MTDC systems for integrating offshore wind farms and to cope with failure method are provided in this paper. Full article
(This article belongs to the Special Issue Optimization and Fault Detection in Smart Power Grids)
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