LLaTSA：大语言模型对齐的通用暂态稳定分析
LLaTSA: Large Language Model-Aligned General-Purpose Transient Stability Analysis
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中文总结 AI 辅助
LLaTSA通过文本前缀、词汇对齐和稀疏MoE骨干，结合状态变量耦合模块，实现通用且高效的暂态稳定轨迹预测与判别。
中文摘要 AI 辅助
动态轨迹预测已成为数据驱动暂态稳定分析（TSA）的重要范式，然而现有的大多数预测器仍局限于特定系统，当网络配置、发电组合或状态变量集合发生变化时，需要大量重新训练。Uni-TSA引入了通用TSA框架，将通道独立建模与预训练的大语言模型（LLM）预测器相结合。然而，其在异构系统中的应用受到短观测数据模糊性、数值轨迹与LLM嵌入之间的不匹配、状态变量间耦合被忽略以及密集骨干网络高推理成本的限制。本文提出LLaTSA，一种面向通用基于轨迹TSA的LLM对齐框架。LLaTSA首先通过结构化文本前缀纳入运行条件、扰动属性和状态变量标识。然后，在利用预训练的稀疏解码器专用混合专家（MoE）骨干处理之前，将归一化的时间补丁与TSA相关词汇表对齐。状态变量耦合模块捕获故障后的协调演化，而教师强制和基于展开的训练支持迭代长时域预测。在多个测试系统上的案例研究表明，LLaTSA实现了准确的轨迹预测、可靠的稳定性判别以及跨未见场景的有效适应。
英文摘要
Dynamic trajectory prediction has become an important paradigm for data-driven transient stability analysis (TSA), yet most existing predictors remain system-specific and require substantial retraining when network configurations, generation mixes, or state-variable sets change. Uni-TSA introduced a general-purpose TSA framework that combines channel-independent modeling with a pretrained large language model (LLM) predictor. Nevertheless, its application to heterogeneous systems is limited by ambiguity in short observations, a mismatch between numerical trajectories and LLM embeddings, neglected coupling among state variables, and the high inference cost of dense backbones. This paper proposes LLaTSA, an LLM-aligned framework for general-purpose trajectory-based TSA. LLaTSA first incorporates operating conditions, disturbance attributes, and state-variable identity through a structured textual prefix. It then aligns normalized temporal patches with a TSA-related vocabulary before processing them with a pretrained sparse decoder-only mixture-of-experts (MoE) backbone. A state-variable coupling module captures coordinated post-fault evolution, while teacher forcing and rollout-based training support iterative long-horizon prediction. Case studies on multiple test systems demonstrate accurate trajectory prediction, reliable stability discrimination, and effective adaptation across unseen scenarios.
