Special Issue "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 "Electrical Power and Energy System".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editor

Guest Editor
Prof. Dr. Srđan Skok Website E-Mail
University North, Varazdin, Croatia
Interests: power system monitoring; protection and control; synchronized measurements; power system stability; power transmission; generation and distribution; renewable sources; electric power industry

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 requirements for 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 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 the modern transmission system.

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 cyber-security;
  • 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 papers will be 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 1800 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 (2 papers)

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Research

Open AccessArticle
Optimization Strategy of SVC for Eliminating Electromagnetic Oscillation in Weak Networking Power Systems
Energies 2019, 12(18), 3489; https://doi.org/10.3390/en12183489 - 10 Sep 2019
Abstract
The central Tibet AC interconnection project (CTAIP), which connects the Tibet power grid and the Sichuan power grid through a long distance transmission line of more than 1400 km, has a significant problem of voltage regulation. In order to improve the voltage regulation [...] Read more.
The central Tibet AC interconnection project (CTAIP), which connects the Tibet power grid and the Sichuan power grid through a long distance transmission line of more than 1400 km, has a significant problem of voltage regulation. In order to improve the voltage regulation performance, six sets of ±60 Mvar static VAR compensators (SVC) were installed in the CTAIP. However, the SVCs may lead to electromagnetic oscillation below 50 Hz while improving voltage regulation capability. In this paper, the electromagnetic oscillation modes and the sensitivity of control parameters of SVC are analyzed. Then, the characteristics and influencing factors of the oscillation are discussed. It was found that there is an inherent electromagnetic oscillation mode below 50 Hz in the ultra-long distance transmission system. The employ of SVCs weaken the damping of this mode. Large proportional gain and integral gain (PI) parameters of SVCs can improve the voltage regulation performance, but weaken the electromagnetic oscillation mode damping. Therefore, a suppression method based on SVC PI parameters optimization is proposed to damp the oscillation. The essential of this method is to use the rising time of voltage response and setting time of SVCs as performance indicators of voltage regulation, and take the damping level of the electromagnetic oscillation mode as the performance index of SVC electromagnetic oscillation suppression ability. Combining the two indicators to form a comprehensive optimization index function, an intelligent optimization algorithm is applied. The process of SVC parameter optimization and the steps of multi-SVC parameter optimization in large power grids is proposed. Finally, PSCAD and real-time digital simulation (RTDS) simulation results verified the correctness of the proposed method. The optimization strategy was applied to CTAIP. The artificial grounding short circuit experimental results proved the effectiveness of the proposed strategy. Full article
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Open AccessArticle
Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network
Energies 2019, 12(18), 3424; https://doi.org/10.3390/en12183424 - 05 Sep 2019
Abstract
Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the [...] Read more.
Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method. Full article
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