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Amidst the increasing penetration of intermittent renewable generation and the persistent growth of load demands, voltage stability assumes a pivotal concern in smart grids. The real-time voltage stability assessment (VSA) under time-varying operating conditions becomes paramount. Recent strides in real-time VSA, utilizing intelligent data-driven learning with measurements, mark significant progress. However, a critical and unresolved challenge with purely data-driven methods is their susceptibility to performance degradation, especially in out-of-sample scenarios. To this end, this article presents a physics-informed guided deep learning (PGDL) paradigm for the practical and accurate assessment of voltage stability margins (VSMs), leveraging both physics-based and data-driven techniques. The PGDL architecture includes an improved temporal convolutional network (iTCN) for the automatic extraction of representative temporal features necessary for VSA from measurement data. Additionally, PGDL integrates physics-based features informed by domain-specific knowledge. A feature fusion scheme is then devised to merge deep-learned features with pertinent physics-based attributes. Acknowledging the unique contributions of these feature modalities to VSA, a novel twin attention mechanism (TAM) is proposed to adaptively adjust attention weights, prioritizing learned features and thus optimizing VSA performance. Substantial experiments on power systems of different scales, coupled with comparative analyses against state-of-the-art benchmarks, illustrate the efficacy and merits of the proposed approach.
Su et al. (Wed,) studied this question.