Universal Transient Stability Analysis: A Pre-trained Generative Transformer-Enabled Power System Dynamics Prediction Framework

TL;DR

This paper introduces Uni-TSA, a pre-trained Transformer-based framework for power system transient stability prediction that generalizes across diverse systems and conditions with minimal fine-tuning.

Contribution

The paper presents a novel universal TSA framework using a pre-trained generative Transformer, featuring innovative data processing, parameter-efficient fine-tuning, and a two-stage training scheme.

Findings

Achieves zero-shot generalization to unseen systems and faults.

Matches expert performance on large systems with minimal fine-tuning.

Demonstrates strong transferability across multiple power system benchmarks.

Abstract

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.