Universal Transient Stability Analysis: A Large Language Model-Enabled Dynamics Prediction Framework
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 TSA-LLM, a large language model (LLM)-based universal framework that models multi-variate 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 or unstable pipelines, and temporal patching for efficient long-sequence modeling; Second, a parameter-efficient freeze-and-finetune strategy that augments the LLM's architecture 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 TSA-LLM 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 larger Iceland 189-bus system with only 5% fine-tuning data. This multi-scenario versatility validates a universal framework that eliminates scenario-specific retraining and achieves scalability via large-scale parameters and cross-scenario training data.
Summary
This paper introduces TSA-LLM, a novel framework for universal transient stability analysis (TSA) using large language models (LLMs). The core problem addressed is the lack of "universality" in existing TSA frameworks, meaning their inability to generalize across diverse operating conditions, unseen faults, and heterogeneous power systems without retraining. TSA-LLM models multivariate transient dynamics prediction as a univariate generative task, leveraging a pre-trained LLM (GPT). The approach involves three key innovations: a data processing pipeline with channel independence decomposition, sample-wise normalization, and temporal patching; a parameter-efficient freeze-and-finetune strategy for adapting the LLM; and a two-stage fine-tuning scheme combining teacher forcing and scheduled sampling to mitigate error propagation. The framework's performance was rigorously tested, demonstrating zero-shot generalization from the New England 39-bus system to mixed stability conditions and unseen faults. Furthermore, it achieves expert-level performance on the Iceland 189-bus system with only 5% fine-tuning data. This universality eliminates the need for scenario-specific retraining and promotes scalability. The core contribution is demonstrating universality in TSA via LLMs, systematically addressing limitations in data processing, model architecture, and fine-tuning. TSA-LLM matters to the field by offering a paradigm shift in TSA modeling. It moves away from system-specific models that require extensive retraining for each new scenario to a universal model that can generalize across diverse power systems. This addresses the increasing complexity of modern power grids due to renewable energy integration and operational uncertainty. The framework provides a foundation for cross-system knowledge transfer, enabling efficient adaptation to large-scale target grids with minimal data, and promotes scalability through large-scale parameters and cross-scenario training.
Key Insights
- •Novel Data Processing: Channel independence decomposition, sample-wise normalization, and temporal patching enable robust handling of heterogeneous systems and diverse dynamics, eliminating the need for separate stable/unstable pipelines.
- •Parameter-Efficient Fine-Tuning: The freeze-and-finetune strategy preserves the generic feature extraction capabilities of the pre-trained LLM while adapting it to the TSA task, achieving a balance between knowledge retention and task adaptation.
- •Two-Stage Fine-Tuning: Teacher Forcing followed by Scheduled Sampling mitigates error propagation during iterative prediction, enhancing long-term reliability by forcing the model to correct for self-generated errors.
- •Zero-Shot Generalization: TSA-LLM demonstrates impressive zero-shot generalization capabilities, performing well on unseen faults and heterogeneous systems without retraining. For instance, it achieves an MSE H of approximately 0.752 on the Iceland 189-bus system in a zero-shot setting.
- •Few-Shot Learning: The framework achieves expert-level performance on the Iceland 189-bus system with only 5% fine-tuning data, demonstrating high data efficiency and cross-system knowledge transfer. It reduces MSE H by nearly 89.62% compared to a fully-trained expert model (Encoder-only Transformer) when fine-tuned on the complete 189-bus dataset.
- •Superior Performance: TSA-LLM significantly outperforms baseline models (LSTM, DNR, Encoder-only Transformer) across various scenarios, with error reductions of at least 84.49% in MAE U and 97.27% in MSE U compared to baselines in mixed stability conditions on the 39-bus system.
- •Computational Efficiency: Online application mitigates potential computational overhead by treating all channels as a unified mini-batch to exploit GPU parallelization. The training time is significantly less than traditional time-domain simulation (TDS), and the inference time is comparable to TDS.
Practical Implications
- •Real-Time Stability Assessment: TSA-LLM enables rapid and accurate online TSA, crucial for preventive control and emergency response in modern power grids with high renewable penetration.
- •Cost Reduction: By eliminating the need for scenario-specific retraining and enabling cross-system knowledge transfer, TSA-LLM reduces the computational simulation and labor costs associated with custom model development for diverse power networks.
- •Scalable TSA Framework: TSA-LLM provides a scalable framework that can adapt to evolving grid complexities by incorporating data from an ever-expanding corpus of power systems, paving the way for a single, continually evolving foundation TSA model.
- •Applications in Corrective Actions: The ability to predict detailed dynamic trajectories informs corrective actions such as load shedding or out-of-step generator tripping, enhancing grid resilience against cascading failures.
- •Future Research Directions: Future research could explore integrating physics-informed constraints into the LLM framework, developing more sophisticated fine-tuning strategies, and investigating the transferability of TSA-LLM to other power system applications, such as voltage stability analysis and oscillation prediction.