Abstract
Modern electric power grids face a variety of new challenges and there is an urgent need to improve grid resilience more than ever before. The best approach would be to focus primarily on the grid intelligence rather than implementing redundant preventive measures. This paper presents the foundation for an intelligent operational strategy so as to enable the grid to assess its current dynamic state instantaneously. Traditional forms of real-time power system security assessment consist mainly of methods based on power flow analyses and hence, are static in nature. For dynamic security assessment, it is necessary to carry out time-domain simulations (TDS) that are computationally too involved to be performed in real-time. The paper employs machine learning (ML) techniques for real-time assessment of grid resiliency. ML techniques have the capability to organize large amounts of data gathered from such time-domain simulations and thereby extract useful information in order to better assess the system security instantaneously. Further, this paper develops an approach to show that a few operating points of the system called as landmark points contain enough information to capture the nonlinear dynamics present in the system. The proposed approach shows improvement in comparison to the case without landmark points.
Highlights
In the wake of new vulnerabilities such as those arising from severe weather events and cyber-attacks, current electric grids can no longer be allowed to operate as they did in the past
The ability to assess the current state of the power system instantaneously is the key attribute needed for enhanced grid resilience
Electric power entities carry out large number of offline studies on power system models of different sizes, generating tons of data
Summary
In the wake of new vulnerabilities such as those arising from severe weather events and cyber-attacks, current electric grids can no longer be allowed to operate as they did in the past. DSA implementations of the future will be required to handle large amounts of data and complete the computation cycles much faster in order to assess the system security in true “real-time” Such an instantaneous assessment would be possible only if grid resilience against any contingency can be expressed as a function of the state estimator output. DSA tools employing such ML techniques will have the ability to determine stability limits in real-time Such sophisticated tools will be able to analyze the current and future dynamics of power systems without carrying out extensive time-domain simulations. CPF facilitates plotting of voltage curves as a function of loading parameter λ, for each bus As stated earlier, such a framework can be used to generate a dataset consisting of multiple steady-state operating points. The section describes the application of machine learning techniques in order to arrive at this unknown function f
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
