A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study
- Resource Type
- Conference
- Authors
- Zhao, Yinfeng; You, Shutang; Mandich, Mirka; Zhu, Lin; Zhang, Chengwen; Li, Hongyu; Su, Yu; Liu, Yilu; Jiang, Huaiguang; Yuan, Haoyu; Zhang, Yingchen; Tan, Jin
- Source
- 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2022 IEEE. :1-5 Apr, 2022
- Subject
- Power, Energy and Industry Applications
Training
Deep learning
Power system stability
Predictive models
Stability analysis
Synchronous generators
Smart grids
Transient stability assessment
machine learning
deep learning
critical clearing time
- Language
- ISSN
- 2472-8152
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.