DAE-Aware Bayesian Inference for Joint Generator-Network Parameter Estimation
It addresses the underexplored problem of joint parameter estimation in DAE-based power system models, offering a practical Bayesian approach for accurate and scalable inference.
This paper develops a Bayesian inference framework for joint estimation of generator and network parameters in power systems using DAE models, achieving accurate parameter recovery on the IEEE 9-bus system and showing scalability on a 39-bus system without requiring overly conservative priors.
This paper addresses the classic problem of parameter estimation (PE) in multimachine power system models. Such models are typically described by a set of nonlinear differential-algebraic equations (DAE), where generator physics and network power flow equations are coupled. DAE models are well established in classic power system textbooks, but parameter identification and estimation of generator inertia and damping together with network branch resistances and reactances for these models remain relatively underexplored. In contrast to prior approaches that rely on ODE approximations, this paper develops a joint Bayesian inference framework to perform PE of generator and network parameters while exploiting grid DAE models. It further combines physics-aware statistical modeling with computationally efficient posterior sampling to make joint Bayesian calibration practical. Results on the IEEE 9-bus system show accurate parameter recovery with well-behaved posterior uncertainty, while a short 39-bus study provides evidence that the framework remains effective on a materially larger joint-estimation problem. These results are obtained without requiring overly conservative priors.