Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

Yinfeng Zhao, Shutang You, Mirka Mandich, Lin Zhu, Chengwen Zhang, Hongyu Li, Yu Su, Chujie Zeng, Yi Zhao, Yilu Liu, Huaiguang Jiang, Haoyu Yuan, Yingchen Zhang and Jin Tan
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Yinfeng Zhao: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Shutang You: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Mirka Mandich: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Lin Zhu: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Chengwen Zhang: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Hongyu Li: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Yu Su: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Chujie Zeng: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Yi Zhao: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Yilu Liu: Department of Electrical Engineering & Computer Science, Tickle College of Engineering, The University of Tennessee at Knoxville, Knoxville, TN 37996, USA
Huaiguang Jiang: National Renewable Energy Laboratory, Golden, CO 80401, USA
Haoyu Yuan: National Renewable Energy Laboratory, Golden, CO 80401, USA
Yingchen Zhang: National Renewable Energy Laboratory, Golden, CO 80401, USA
Jin Tan: National Renewable Energy Laboratory, Golden, CO 80401, USA

Energies, 2021, vol. 14, issue 20, 1-17

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.

Keywords: adaptive capability; deep learning; customized loss function; Remedial Action Scheme (RAS); security margin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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