Machine Learning in Power System Dynamic Security Assessment
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 15850
Special Issue Editor
Special Issue Information
Dear Colleagues,
The Guest Editor is inviting submissions to a Special Issue of Energies on the subject area of "Machine Learning in Power System Dynamic Security Assessment". The integration of extensive measuring, monitoring, and communication infrastructures into modern power systems (networks) offers unprecedented opportunities for acquiring massive amounts of data regarding its (real-time) performance. This data can be mined and utilized for studying various threats to the power system operation, which manifest primarily in the form of dynamic instabilities and security concerns, such as the transient stability assessment, voltage and frequency instability, power quality issues, and others. The need for efficient methodologies for faster identification and robust detection (and classification) of these network problems has always been a priority with energy stakeholders. Moreover, it is gaining importance over the last years, fueled partially by the liberalization of the energy markets and increasing penetration of renewable energy sources. Machine learning, as well as (most-recently) reinforcement learning, techniques have proven to be effective in numerous applications, including different power system studies. Various machine learning techniques, such as artificial neural networks, decision trees, support vector machines, to name only a few of the most prominent ones, have already been proposed in the literature, resulting in effective decision making and control actions that support secure and stable operations of the power system.
This Special Issue will deal with novel approaches to the power system dynamic security assessment, and related power disturbance issues, which are based on the applications of machine learning, deep learning, and reinforcement learning techniques. It will also deal with problems related to advanced data acquisition (wide-area measurement systems) and data-sets preparation (statistical processing, features engineering, encoding, embedding). Topics of interest for publication include, but are not limited to, applications of machine learning, deep learning, and reinforcement learning in the following:
- Power system dynamic security assessment;
- Transient stability assessment;
- Small signal stability analysis;
- Voltage stability assessment;
- Frequency stability assessment;
- Power quality disturbance analysis;
- Advanced metering, data acquisition, and monitoring;
- Analysis of electrical network vulnerabilities and threats;
- Intelligent monitoring and outage management (self-healing grids);
- Dynamic security assessment of mixed AC-DC power systems;
- Impact of new technologies (FACTS/HVDC) on power system stability;
- Stability and security analysis of future networks.
Dr. Petar Sarajcev
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
- Machine learning
- Deep learning
- Reinforcement learning
- Artificial intelligence
- Power system
- Dynamic security
- Transient stability
- Small signal stability
- Rotor angle stability
- Wide-area measurement systems
- Network vulnerability
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
