Bidirectional Active Transfer Learning for Adaptive Power System Stability Assessment and Dominant Instability Mode Identification

Abstract

Deep learning (DL) is a useful tool for power system stability assessment (PSSA) and dominant instability mode (DIM) identification. However, when faced with operational variability, the performance of DL models degrades. This paper proposes a bidirectional active transfer learning (Bi-ATL) framework for more adaptive PSSA and DIM identification, where the DL model is easier to adapt to unlearned operating conditions with fewer newly labeled instances. At the instance level, forward active learning and backward active learning are integrated to progressively build a mixed instance set by actively including the most label-worthy instances of new operating conditions and actively eliminating the most useless original operating condition instances. Then at the model parameter level, the mixed instance set is utilized to fine-tune the original DL model to new operating conditions. The Bi-ATL framework synthesizes three-way information of the instances and model of the original operating condition, and a few labeled instances of new operating conditions for more efficient adaptation. Intensive case studies conducted on a benchmark power system (CEPRI 36-bus system) and a real-world large-scale power system (Northeast China Power System-2131 bus) validate the efficacy and efficiency of the Bi-ATL framework as well as the role of the three-way information.