Instability Pattern-guided Model Updating Method for Data-driven Transient Stability Assessment

Deep learning methods are widely adopted in power system transient stability assessment (TSA). However, the interpretability of the assessment results and the controllability of the assessment process hinder the further application of deep learning methods in practice. In this paper, an instability pattern-guided model updating method is proposed to optimize the TSA model. Firstly, a TSA model based on Transformer encoder is proposed to explain and analyze the model's prediction through attention distribution. Secondly, an attention-guiding loss is employed to revise the assessment rules for specified instability patterns. The samples with specified instability patterns can be classified more accurately. Thirdly, an attention-keeping loss is employed to maintain the assessment rules for other samples and mitigate overfitting in the update. In addition, a representative dataset is introduced to reduce the update cost. The samples in the representative dataset are extracted from an original training set based on the attention distribution. The effectiveness of the proposed method is verified in the IEEE 39-bus system and the East China Power Grid system.