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 14258
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
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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
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