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

Xinlan Jia, Jiaojiao Dong, Yinfeng Zhao, Jin Tan, Yilu Liu

Research output: Contribution to conferencePaper


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.
Original languageAmerican English
Number of pages5
StatePublished - 2023
Event2023 IEEE PES/IAS PowerAfrica - Marrakech, Morocco
Duration: 6 Nov 202310 Nov 2023


Conference2023 IEEE PES/IAS PowerAfrica
CityMarrakech, Morocco

NREL Publication Number

  • NREL/CP-5D00-88768


  • critical clearing time
  • frequency nadir
  • machine learning
  • network topology change
  • power system stability assessment


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