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Fault Locations for Smart 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 (5 September 2023) | Viewed by 13814

Special Issue Editors


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Guest Editor
Department of Informatics, Universidad Carlos III de Madrid, 28903 Madrid, Spain
Interests: smart grids; control theory; fuzzy systems; AI
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Guest Editor
Discipline of Engineering and Energy, Murdoch University, Murdoch 6150, Australia
Interests: electric distribution systems power; microgrids; smart-grid-distributed energy resources
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran
Interests: lightning protection; high-voltage engineering; power system transients; pulsed power technology; power system optimization

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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,

This is my pleasure to announce a Special Issue of Energies on “Fault Locations for Smart Grids” and invite you to contribute your recent results.

Smart grids have facilitated the interconnection of the different units in the power system, and the intelligence of the grid system has been improved by the multidirectional flow of information from the consumer to the substation and from the generation unit toward the distribution and consumption. Therefore, the security and efficiency of smart grids are expected to be superior to those of traditional power systems and the robustness and resiliency of services are expected to increase.

However, on the dark side of the distributed platform of the smart connection of consumer sensors to substations, there is a risk of propagating faults to neighbors via communication paths, connection ports, circuit breakers, etc. Therefore, fault diagnosis, system monitoring and management are crucial for the secure performance of smart grids as well as safe service provision.

This Special Issue is intended to focus on the Fault Locations for Smart Grids and explore the fault universe of the various components in smart grids. Moreover, the state-of-the-art diagnosis methods will be studied in terms of both model-based and data-based approaches.

Researchers and contributors are invited to submit novel solution methods to address the different aspects of fault diagnosis. Potential topics include but are not limited to:

  • Fault monitoring technologies;
  • Communication infrastructure;
  • Physical models for the fault diagnosis;
  • Data measurement for fault diagnosis in smart grids;
  • Control methodologies for fault mitigation;
  • System-wide impacts of component faults in smart grids;
  • Cascading failure of components in smart grids;
  • Identification of sensor gaps for faults minimization;
  • Decentralized control for fault mitigation;
  • Observer design for data measurement;
  • State-estimation-based methods;
  • Classification-based methods;
  • Resilient power generation;
  • Data feature extraction for fault localization;
  • Domain transformation for fault localization;
  • Chemical diagnosis of components;
  • Thermal diagnosis of components;
  • Mechanical diagnosis of components;
  • Optimization methods for fault impairment in smart grids;
  • Robust control methods for fault tolerance;
  • Intelligent methods for fault detection and minimization;
  • Reliability of power distribution networks;
  • Attack-resilient control in micro grids;
  • Fault locations in low-voltage and DC smart grids;
  • Local and nonlocal measurement-based techniques.

Kind Regards,

Dr. Ebrahim Navid Sadjadi
Dr. Farhad Shahnia
Dr. Behrooz Vahidi
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

  • fault locations
  • smart grids
  • fault classification
  • low-voltage and DC smart grids
  • resiliency of smart grids
  • local and nonlocal measurement-based techniques
  • state estimation for fault localization
  • attack-resilient control
  • microgrids
  • artificial intelligence
  • distributed control
  • multi-agent systems

Published Papers (5 papers)

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Research

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24 pages, 3644 KiB  
Article
Fault Detection and Localisation in LV Distribution Networks Using a Smart Meter Data-Driven Digital Twin
by Mohamed Numair, Ahmed A. Aboushady, Felipe Arraño-Vargas, Mohamed E. Farrag and Eyad Elyan
Energies 2023, 16(23), 7850; https://doi.org/10.3390/en16237850 - 30 Nov 2023
Cited by 3 | Viewed by 1524
Abstract
Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a [...] Read more.
Modern solutions for precise fault localisation in Low Voltage (LV) Distribution Networks (DNs) often rely on costly tools such as the micro-Phasor Measurement Unit (μPMU), which is potentially impractical for the large number of nodes in LVDNs. This paper introduces a novel fault detection technique using a distribution network digital twin without the use of μPMUs. The Digital Twin (DT) integrates data from Smart Meters (SMs) and network topology to create an accurate replica. In using SM voltage-magnitude readings, the pre-built twin compiles a database of fault scenarios and matches them with their unique voltage fingerprints. However, this SM-based voltage-only approach shows only a 70.7% accuracy in classifying fault type and location. Therefore, this research suggests using the cables’ Currents Symmetrical Component (CSC). Since SMs do not provide direct current data, a Machine Learning (ML)-based regression method is proposed to estimate the cables’ currents in the DT. Validation is performed on a 41-node LV distribution feeder in the Scottish network provided by the industry partner Scottish Power Energy Networks (SPEN). The results show that the current estimation regressor significantly improves fault localisation and identification accuracy to 95.77%. This validates the crucial role of a DT in distribution networks, thus enabling highly accurate fault detection when using SM voltage-only data, with further refinements being conducted through estimations of CSC. The proposed DT offers automated fault detection, thus enhancing customer connectivity and maintenance team dispatch efficiency without the need for additional expensive μPMU on a densely-noded distribution network. Full article
(This article belongs to the Special Issue Fault Locations for Smart Grids)
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29 pages, 3224 KiB  
Article
Operation of the System of Coupled Low-Voltage Feeders during Short-Circuit Faults
by Farhad Shahnia
Energies 2023, 16(16), 6009; https://doi.org/10.3390/en16166009 - 16 Aug 2023
Viewed by 801
Abstract
As a technique to control the voltage drop at network peak periods and voltage rise at middays when a high number of rooftop photovoltaic systems exist in a low-voltage feeder (LVF), two or more neighboring LVFs can be coupled. To add voltage controllability [...] Read more.
As a technique to control the voltage drop at network peak periods and voltage rise at middays when a high number of rooftop photovoltaic systems exist in a low-voltage feeder (LVF), two or more neighboring LVFs can be coupled. To add voltage controllability to the coupling point, a distribution static compensator (DSTATCOM) can be installed. An important issue for such a system is its operation under short-circuit conditions in one of the LVFs and relevant protection aspects. This paper investigates the performance of such a system under fault conditions and presents a protection scheme that can achieve the desired operation of the system, under short-circuit faults in either of the LVFs. The performance of the system of coupled LVFs is investigated by numerical analysis in MATLAB while the dynamic feasibility of the proposed technique is validated by simulation studies in PSCAD/EMTDC. Full article
(This article belongs to the Special Issue Fault Locations for Smart Grids)
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15 pages, 3395 KiB  
Article
SR-GNN Based Fault Classification and Location in Power Distribution Network
by Haojie Mo, Yonggang Peng, Wei Wei, Wei Xi and Tiantian Cai
Energies 2023, 16(1), 433; https://doi.org/10.3390/en16010433 - 30 Dec 2022
Cited by 5 | Viewed by 1619
Abstract
Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are [...] Read more.
Accurately evaluating the fault type and location is important for ensuring the reliability of the power distribution network. A mushrooming number of distributed generations (DGs) connected to the distribution system brings challenges to traditional fault classification and location methods. Novel AI-based methods are mostly based on wide area measurement with the assistance of intelligent devices, whose economic cost is somewhat high. This paper develops a super-resolution (SR) and graph neural network (GNN) based method for fault classification and location in the power distribution network. It can accurately evaluate the fault type and location only by obtaining the measurements of some key buses in the distribution network, which reduces the construction cost of the distribution system. The IEEE 37 Bus system is used for testing the proposed method and verifying its effectiveness. In addition, further experiments show that the proposed method has a certain anti-noise capability and is robust to fault resistance change, distribution network reconfiguration, and distributed power access. Full article
(This article belongs to the Special Issue Fault Locations for Smart Grids)
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24 pages, 626 KiB  
Article
State Estimation Fusion for Linear Microgrids over an Unreliable Network
by Mohammad Soleymannejad, Danial Sadrian Zadeh, Behzad Moshiri, Ebrahim Navid Sadjadi, Jesús García Herrero and Jose Manuel Molina López
Energies 2022, 15(6), 2288; https://doi.org/10.3390/en15062288 - 21 Mar 2022
Cited by 3 | Viewed by 2329
Abstract
Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion [...] Read more.
Microgrids should be continuously monitored in order to maintain suitable voltages over time. Microgrids are mainly monitored remotely, and their measurement data transmitted through lossy communication networks are vulnerable to cyberattacks and packet loss. The current study leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of estimation fusion using various machine-learning (ML) regression methods as data fusion methods by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid in order to achieve more accurate and reliable state estimates. This unreliability in measurements is because they are received through a lossy communication network that incorporates packet loss and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent ordered weighted averaging (DOWA) operators are also employed for further comparisons. The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML regression methods through the RMSE, MAE and R-squared indices under the condition of missing and manipulated measurements. In general, the results obtained by the Random Forest regression method were more accurate than those of other methods. Full article
(This article belongs to the Special Issue Fault Locations for Smart Grids)
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Review

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37 pages, 5625 KiB  
Review
Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends
by Jorge De La Cruz, Eduardo Gómez-Luna, Majid Ali, Juan C. Vasquez and Josep M. Guerrero
Energies 2023, 16(5), 2280; https://doi.org/10.3390/en16052280 - 27 Feb 2023
Cited by 18 | Viewed by 6099
Abstract
Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a [...] Read more.
Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribution sector as well as fault location methods recommended to improve resilience, which will aid readers in choosing methods for their own research. Finally, conclusions are given after discussing the trends in fault location and detection techniques. Full article
(This article belongs to the Special Issue Fault Locations for Smart Grids)
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