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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2512.20970 |
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| _version_ | 1866911703883579392 |
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| author | Shen, Chao Zuo, Ke Sun, Mingyang |
| author_facet | Shen, Chao Zuo, Ke Sun, Mingyang |
| contents | Existing dynamics prediction frameworks for transient stability analysis (TSA) fail to achieve multi-scenario "universality": the inherent ability of a single, pre-trained architecture to generalize across diverse operating conditions, unseen faults, and heterogeneous systems. To address this, this paper proposes Uni-TSA, a pre-trained generative Transformer-enabled universal framework that models multivariate transient dynamics prediction as a univariate generative task with three key innovations: First, a novel data processing pipeline featuring channel independence decomposition to resolve dimensional heterogeneity, sample-wise normalization to eliminate separate stable/unstable pipelines, and temporal patching for efficient long-sequence modeling; Second, a parameter-efficient freeze-and-finetune strategy that augments the pre-trained generative Transformer backbone with dedicated input embedding and output projection layers while freezing core transformer blocks to preserve generic feature extraction capabilities; Third, a two-stage fine-tuning scheme that combines teacher forcing, which feeds the model ground-truth data during initial training, with scheduled sampling, which gradually shifts to leveraging model-generated predictions, to mitigate cumulative errors in long-horizon iterative prediction. Comprehensive testing demonstrates the framework's universality, as Uni-TSA trained solely on the New England 39-bus system achieves zero-shot generalization to mixed stability conditions and unseen faults, and matches expert performance on the Iceland 189-bus system with only 5% fine-tuning data. Additional cross-system experiments on the IEEE 68-bus and IEEE 118-bus systems, together with stability metrics and PEBS comparison, further confirm Uni-TSA's strong zero-shot transferability and data-efficient adaptation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20970 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal Transient Stability Analysis: A Pre-trained Generative Transformer-Enabled Power System Dynamics Prediction Framework Shen, Chao Zuo, Ke Sun, Mingyang Systems and Control Existing dynamics prediction frameworks for transient stability analysis (TSA) fail to achieve multi-scenario "universality": the inherent ability of a single, pre-trained architecture to generalize across diverse operating conditions, unseen faults, and heterogeneous systems. To address this, this paper proposes Uni-TSA, a pre-trained generative Transformer-enabled universal framework that models multivariate transient dynamics prediction as a univariate generative task with three key innovations: First, a novel data processing pipeline featuring channel independence decomposition to resolve dimensional heterogeneity, sample-wise normalization to eliminate separate stable/unstable pipelines, and temporal patching for efficient long-sequence modeling; Second, a parameter-efficient freeze-and-finetune strategy that augments the pre-trained generative Transformer backbone with dedicated input embedding and output projection layers while freezing core transformer blocks to preserve generic feature extraction capabilities; Third, a two-stage fine-tuning scheme that combines teacher forcing, which feeds the model ground-truth data during initial training, with scheduled sampling, which gradually shifts to leveraging model-generated predictions, to mitigate cumulative errors in long-horizon iterative prediction. Comprehensive testing demonstrates the framework's universality, as Uni-TSA trained solely on the New England 39-bus system achieves zero-shot generalization to mixed stability conditions and unseen faults, and matches expert performance on the Iceland 189-bus system with only 5% fine-tuning data. Additional cross-system experiments on the IEEE 68-bus and IEEE 118-bus systems, together with stability metrics and PEBS comparison, further confirm Uni-TSA's strong zero-shot transferability and data-efficient adaptation. |
| title | Universal Transient Stability Analysis: A Pre-trained Generative Transformer-Enabled Power System Dynamics Prediction Framework |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.20970 |