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

Zhao, Yinfeng·You, Shutang·Mandich, Mirka·Zhu, Lin·Zhang, Chengwen·Li, Hongyu·Liu, Yilu·Jiang, Huaiguang·Yuan, Haoyu·Zhang, Yingchen·Tan, Jin (ORCID:0000000205997730)
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Abstract

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.

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critical clearing timedeep learningmachine learningPOWER TRANSMISSION AND DISTRIBUTIONtransient stability assessment

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Zhao, Yinfeng, You, Shutang, Mandich, Mirka, Zhu, Lin, Zhang, Chengwen, Li, Hongyu, Liu, Yilu, Jiang, Huaiguang, Yuan, Haoyu, Zhang, Yingchen, Tan, Jin (ORCID:0000000205997730). 2022-07-12. A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study. https://doi.org/10.1109/isgt50606.2022.9817531

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DOE OSTI2023

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

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