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Volume II: Advanced Solutions for Monitoring, Protection and Control of Modern Power Transmission System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 9565

Special Issue Editor

Department of Electrical Engineering, University North, 42000 Varaždin, Croatia
Interests: power system monitoring; protection and control; synchronized measurements; power system stability; power transmission; generation and distribution; renewable sources; electric power industry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Undoubtedly, renewable energy sources (RES) have changed traditional transmission grids and represent a significant electricity resource in modern transmission grids. The positive effects of RES on environmental preservation are indisputable, but their long-term intermittent and unpredictable performance, along with their low inertia, necessitate new power transmission system control in order to maintain the stability of the power system, creating challenges for system operators in terms of monitoring, protection, and control of advanced networks.

The traditional principle of the regulation of a power system relies on redundant production from classical generation units (thermal power plants, hydro power plants, and gas power plants) that in the case of disturbances in the system—especially in the case of renewable energy source outage—maintain the stability of the power system.

Therefore, it is necessary to develop innovative solutions (both in terms of hardware and software) to provide to system operators in order to maintain system integrity and preserve system resilience. The aim of this Special Issue is to present advanced and innovative technical solutions, which will emphasize the monitoring, protection, and control of modern transmission systems.

More specifically, topics of interest for this Special Issue include (but are not limited to) the following:

  • Power system monitoring, protection, and control;
  • Industry experience in deploying smart grid technologies for power transmission;
  • Synchronized measurements and applications;
  • Regulation of mixed generation;
  • Ancillary services of distributed generation;
  • Information and communication technologies for smart grids, interoperability, and cybersecurity;
  • Transmission system dynamic modeling;
  • Interoperability between the transmission system operator and distribution system operator;
  • Hybrid SCADA/EMS applications;
  • System integration of distributed energy resources, islanding, and hosting capacity;
  • Transmission system technologies, HVDC, FACTS, SVC, and energy storage;
  • Planning and management of transmission grid assets;
  • Power electronics and control and protection systems for transmission grid applications;
  • Transmission grid monitoring and advanced metering infrastructures;
  • Diagnostics, maintenance, risks, reliability, vulnerability, and self-healing of transmission grids;
  • Demand-side management;
  • Transmission grid planning, forecasting, and operation;
  • Regulations, standards, and codes for modern transmission grids;
  • Machine learning;
  • Big data analysis;
  • Smart transmission grid impacts on electricity markets;
  • Business models for transmission grids.

Prof. Dr. Srđan Skok
Guest Editor

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

  • Smart power transmission system
  • Monitoring, protection, and control
  • Distributed generation
  • Synchronized measurements
  • Power system stability
  • Transmission system resilience
  • Transmission system integrity

Published Papers (5 papers)

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Research

14 pages, 7920 KiB  
Article
Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying
by Vattanak Sok, Sun-Woo Lee, Sang-Hee Kang and Soon-Ryul Nam
Energies 2022, 15(7), 2644; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072644 - 04 Apr 2022
Cited by 4 | Viewed by 1190
Abstract
To make a correct decision during normal and transient states, the signal processing for relay protection must be completed and designated the correct task within the shortest given duration. This paper proposes to solve a dc offset fault current phasor with harmonics and [...] Read more.
To make a correct decision during normal and transient states, the signal processing for relay protection must be completed and designated the correct task within the shortest given duration. This paper proposes to solve a dc offset fault current phasor with harmonics and noise based on a Deep Neural Network (DNN) autoencoder stack. The size of the data window was reduced to less than one cycle to ensure that the correct offset is rapidly computed. The effects of different numbers of the data samples per cycle are discussed. The simulations revealed that the DNN autoencoder stack reduced the size of the data window to approximately 90% of a cycle waveform, and that DNN performance accuracy depended on the number of samples per cycle (32, 64, or 128) and the training dataset used. The fewer the samples per cycle of the training dataset, the more training was required. After training using an adequate dataset, the delay in the correct magnitude prediction was better than that of the partial sums (PSs) method without an additional filter. Similarly, the proposed DNN outperformed the DNN-based full decay cycle dc offset in the case of converging time. Taking advantage of the smaller DNN size and rapid converging time, the proposed DNN could be launched for real-time relay protection and centralized backup protection. Full article
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15 pages, 5000 KiB  
Article
Monitoring the Geometry of Tall Objects in Energy Industry
by Tadeusz Głowacki
Energies 2022, 15(7), 2324; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072324 - 23 Mar 2022
Cited by 3 | Viewed by 1426
Abstract
The landscape shaped by the energy industry is rich in various slender structures, such as smokestacks, cooling towers, and others. It is thus becoming increasingly important to effectively monitor the geometrical condition of all types of such structures. Slender structures are deformed elastically [...] Read more.
The landscape shaped by the energy industry is rich in various slender structures, such as smokestacks, cooling towers, and others. It is thus becoming increasingly important to effectively monitor the geometrical condition of all types of such structures. Slender structures are deformed elastically under loads due to wind. A proper analysis of the changes and deformations of such structures requires a continuous ground-based measurement system which allows the movement of the structure to be measured in two horizontal directions, from a significant distance and with a possibly reduced number of stations. For this purpose, two methods were implemented: a linear terrestrial laser scanning method (TLS) and an optical, direct distance measurement method—tachymetry (TCH). The least squares method was used to fit rings on various levels of the structure and then the centers of the rings were identified. The comparison of the identified ring centers enabled the axis of the structure to be measured for deviation in two perpendicular directions. The methods were verified on actual structures: a smokestack 110 m in height, a cooling tower 60 m in height, and a wind turbine with the rotor axis at 149 m. The measurement results were compared with respect to the measurement time and the obtained accuracies at which the point locations were identified on the structure. The proposed methods are an effective tool for monitoring the condition of slender objects both during their operating life and after it. Regular monitoring of the geometric condition of slender structures in the energy industry limits the risk of major or catastrophic events, and as a result allows the safe and uninterrupted delivery of electric energy to clients. Full article
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13 pages, 8933 KiB  
Article
Design and Development of an Online Smart Monitoring and Diagnosis System for Photovoltaic Distributed Generation
by Thiago A. Felipe, Fernando C. Melo and Luiz C. G. Freitas
Energies 2021, 14(24), 8552; https://0-doi-org.brum.beds.ac.uk/10.3390/en14248552 - 18 Dec 2021
Cited by 3 | Viewed by 2049
Abstract
In photovoltaic power plants, fault diagnosis tools are essential for ensuring a high energy yield. These tools should be capable of accurately identifying and quantifying the factors behind the various fault mechanisms commonly found in photovoltaic plants. Considering the aforementioned factors, this article [...] Read more.
In photovoltaic power plants, fault diagnosis tools are essential for ensuring a high energy yield. These tools should be capable of accurately identifying and quantifying the factors behind the various fault mechanisms commonly found in photovoltaic plants. Considering the aforementioned factors, this article proposes an online smart PV monitoring solution, which is capable of detecting malfunctions that arise from accidental and/or technical causes through the analysis of I-V curves, however, without the necessity to interrupt the operation of the system, thus reducing the maintenance cost. Accidental causes can lead to the reduction of energy productivity due to the excessive accumulation of dirt on the photovoltaic modules, partial shading and eventual errors that occur during its installation. On the other hand, technical causes can be attributed to faults found on the photovoltaic modules, which lead to gradual losses in their electric and material characteristics. Therefore, by using the electric characteristics supplied by the manufacturer of the installed modules, the I-V and P-V curves of the operational photovoltaic strings were obtained in real time, compared to the respective theoretical curves obtained through mathematical modeling. In order to validate the proposed online monitoring system and its potential for predictive maintenance application, a field experimentation was mounted in a 93.8 kWp photovoltaic system. Full article
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27 pages, 41499 KiB  
Article
Designing Virtual Reality Environments through an Authoring System Based on CAD Floor Plans: A Methodology and Case Study Applied to Electric Power Substations for Supervision
by Camilo de Lellis Barreto Junior, Alexandre Cardoso, Edgard Afonso Lamounier Júnior, Paulo Camargos Silva and Alexandre Carvalho Silva
Energies 2021, 14(21), 7435; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217435 - 08 Nov 2021
Cited by 11 | Viewed by 2648
Abstract
The adoption of Virtual Reality (RV) technologies in prototype design and process revision has contributed to multiple industry areas. Nonetheless, the development of VR systems for engineering is a complex task, as it involves specialized teams handling low-level code development. Given these problems, [...] Read more.
The adoption of Virtual Reality (RV) technologies in prototype design and process revision has contributed to multiple industry areas. Nonetheless, the development of VR systems for engineering is a complex task, as it involves specialized teams handling low-level code development. Given these problems, the goal of this study is presenting a methodology for designing VR, through an Authoring System based on Computer-Aided Design (CAD). The presented methodology provides an easy integration of electric power substation floor plans and Virtual Reality software (VRS), as well as three-dimensional and symbol modeling conventions. Centralized software architecture was developed, composed of the CAD Editor, input manager and VRS. The methodology was evaluated through a case study applied to the conception (elaboration) of electric power substations (EPS) as part of a Research and Development (R&D) project for training and field assets supervision. The results demonstrated visual precision and high integrity in elaboration of a VR environment from the CAD floor plan. This work also presents a comparative analysis between manual conception and the Authoring System. Full article
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39 pages, 14828 KiB  
Article
Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks
by Igor Kotenko, Igor Saenko, Oleg Lauta and Mikhail Karpov
Energies 2021, 14(18), 5963; https://0-doi-org.brum.beds.ac.uk/10.3390/en14185963 - 20 Sep 2021
Cited by 9 | Viewed by 1539
Abstract
This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of [...] Read more.
This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95. Full article
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