Online Tracking of Small-Signal Stability Rightmost Eigenvalue Based on Reference Point

Abstract

Machine learning technologies have been applied to improve the real-time performance of small-signal stability (SSS) assessment, while achieving a high accuracy requires numerous samples, and model performance may degrade if not updated over time. Furthermore, single models tend to learn general features without analyzing specific characteristics within the samples, which may lead to a high frequency of large errors, particularly at operating points (OPs) where the eigenvalue trajectories have sudden changes. Facing such issues, this paper introduces the concept of reference points (RPs) for accurate online tracking of the rightmost eigenvalue (RE), as the RP information reflects the characteristics among its surrounding OPs. The performance of this model is sensitive to RPs, so affinity propagation (AP) clustering is employed to determine the number of RPs and generate corresponding groups, accommodating diverse OP characteristics. This paper generates data-driven networks for each group and combines them into a multi-network for precise RE prediction. Case studies show that the use of RPs improves accuracy by nearly 2% compared to methods without them, with even greater improvements in mixed load type scenarios. To mitigate computational stress, this paper proposes an adaptive partial update strategy based on the dynamic time warping algorithm, avoiding the need to update all networks within each sliding time window. Experimental results verify that the total running time is reduced by more than 10%. Online tracking demonstrates that RPs help decrease the frequency of large errors by 40%, especially at sudden change points of RE trajectories.