LLM-DMD: Large Language Model-based Power System Dynamic Model Discovery

TL;DR

This paper introduces LLM-DMD, a novel framework using large language models to discover detailed power system dynamic models, including algebraic constraints, improving simulation accuracy.

Contribution

The paper presents a new LLM-based approach for power system dynamic model discovery that integrates reasoning, code synthesis, and algebraic constraint enforcement.

Findings

Outperforms existing methods in discovering complete dynamic models.

Successfully applied to IEEE 39-bus system benchmarks.

Enhances model fidelity by incorporating algebraic constraints.

Abstract

Current model structural discovery methods for power system dynamics impose rigid priors on the basis functions and variable sets of dynamic models while often neglecting algebraic constraints, thereby limiting the formulation of high-fidelity models required for precise simulation and analysis. This letter presents a novel large language model (LLM)-based framework for dynamic model discovery (LLM-DMD) which integrates the reasoning and code synthesis capabilities of LLMs to discover dynamic equations and enforce algebraic constraints through two sequential loops: the differential-equation loop that identifies state dynamics and associated variables, and the algebraic-equation loop that formulates algebraic constraints on the identified algebraic variables. In each loop, executable skeletons of power system dynamic equations are generated by the LLM-based agent and evaluated via gradient-based optimizer. Candidate models are stored in an island-based archive to guide future iterations, and evaluation stagnation activates a variable extension mechanism that augments the model with missing algebraic or input variables, such as stator currents to refine the model. Validation on synchronous generator benchmarks of the IEEE 39-bus system demonstrates the superiority of LLM-DMD in complete dynamic model discovery.