Abstract
This paper presents a novel fast machine learning-based scheme for predicting the transient and short-term voltage (STV) stability status and determining the driving force of instability, without any need to measure post-fault-clearance data. In the proposed scheme, by employing decision tree (DT) classifier and using the measurements of the installed phasor measurement unit (PMU) at the generator and the induction motor (IM) buses, first, the stability status of either transient or STV (including the sustained low voltage without recovery and the voltage collapse) is predicted just after disturbance in Wide Area Measurement System (WAMS) framework. Then, for unstable cases, based on transient and STV stability mechanisms, the driving force of the instability is determined between transient and STV instability. In order to evaluate the performance of the proposed scheme, the method has been tested on IEEE 39-bus test system and IEEE Nordic test system. The simulation results show the proposed scheme is highly accurate and timely.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Electrical Power & Energy Systems
Disclaimer: 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.