Transfer Extreme Learning Machine for Power System Cross-Fault and Cross-Scale Stability Assessment with Limited Guide Instances

This letter proposes two novel effective transfer learning (TL) methods for power system stability assessment (SA) under distinct scenarios: cross-fault, where different types of faults are considered, and cross-scale, which accounts for varying system knowledge levels. Addressing the challenges faced in scenarios with few limited labeled SA data, our proposed datadriven SA models aim to transfer to the different but related scenarios by leveraging numerous instances from fully knowledge database and few labeled instances from the limited knowledge database. Moreover, a significant feature of our approach is the incorporation of the Extreme Learning Machine, a rapid neural network-based learning algorithm. Preliminary testing showcases an improvement of more than 24% in SA accuracy, especially for large-scale cross-scale transfer, demonstrating the efficacy of our TL techniques while maintaining computational efficiency. © 2024 IEEE