Abstract
Synchronous machine analysis has been the subject of investigation for a long time. For the reliable prediction and evaluation of the dynamic performance of power systems, it is essential to use a comprehensive mathematical model. In practice, however, such detailed knowledge is rarely available. This paper describes an application of artificial neural networks in studies of unmodeled synchronous machine dynamics. The goal is to examine the extrapolation and interpolation capabilities of neural networks within the scope of being able to learn synchronous machine behavior and to predict the time response of a synchronous generator for new initial conditions.
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.
