Diffusion model-based parameter estimation in dynamic power systems

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents a critical barrier to accurate and unique identification. Here we introduce a parameter estimation framework to address such limits: the Joint Conditional Diffusion Model-based Inverse Problem Solver. By leveraging the stochasticity of diffusion models, it produces candidate solutions that capture underlying parameter distributions conditioned on the observations. Joint conditioning on multiple observations further narrows the posterior distributions of non-identifiable parameters. For composite load model parameterization, a challenging task in dynamic power systems, the proposed method achieves a 58.6% reduction in parameter estimation error compared to the single-condition model. It also accurately replicates system’s dynamic responses under various electrical faults with root mean square errors below 4 × 10−3, exhibiting comprehensive advantages in calibration and efficiency over existing methods. Given its data-driven nature, it provides a general framework for parameter estimation while effectively mitigating the non-uniqueness problem across scientific domains. Feiqin Zhu, Dmitrii Torbunov and colleagues propose a probabilistic parameter estimation framework to address limits of parameter non-uniqueness in dynamical systems. Study on a composite load model in dynamic power systems demonstrates its effectiveness in reducing parameter estimation uncertainties and improving cross-event generalization.