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Title: A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study

Conference ·

Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
1883210
Report Number(s):
NREL/CP-6A40-83790; MainId:84563; UUID:62682947-23aa-4027-883f-5abf5848a32e; MainAdminID:65160
Resource Relation:
Conference: Presented at the 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 24-28 April 2022, New Orleans, Louisiana
Country of Publication:
United States
Language:
English

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