Abstract
The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.
Highlights
Accepted: 27 September 2021Transient stability is a crucial aspect of power system stability, which refers to the ability to keep all generators synchronized after a large disturbance
Due to the effort to reduce greenhouse gas emissions, conventional synchronous generators are being replaced by inverter-based renewables (IBRs), resulting in lower system inertia, unusual power flow paths, and larger angular differences spreading across the system [5]
This study is based on the reduced Western Electricity Coordinating Council (WECC) 240-bus system model developed by the National Renewable Energy Laboratory (NREL), which has the generation of different fuel types, e.g., coal, gas, bio, nuclear, hydro, wind, and solar [16]
Summary
Transient stability is a crucial aspect of power system stability, which refers to the ability to keep all generators synchronized after a large disturbance. If a power system is not transient stable, necessary corrective actions, e.g., load shedding and generation trip, will take place by Remedial Action Scheme (RAS) or Special Protection System (SPS) [1,2,3,4] These corrective actions are designed offline based on the time-domain simulation of a few selected severe contingencies under some representative operating conditions (e.g., spring light, summer peak, and winter peak). [7] can impact system dynamics significantly These changes make the traditional RAS or SPS design method inadequate to guarantee its effectiveness under all possible operating conditions. The deep learning–based adaptive remedial action scheme (RAS) is proposed in this paper This new method suggests optimal load shedding and generation trip amount paper.
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.
