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 data-driven 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.