Authors: Feiqin Zhu, Dmitrii Torbunov, Yihui Ren, Zhongjing Jiang, Tianqiao Zhao, Amirthagunaraj Yogarathnam, Meng Yue
Abstract: Data-driven modeling for dynamic systems has gained widespread attention in
recent years. Its inverse formulation, parameter estimation, aims to infer the
inherent model parameters from observations. However, parameter degeneracy,
where different combinations of parameters yield the same observable output,
poses a critical barrier to accurately and uniquely identifying model
parameters. In the context of WECC composite load model (CLM) in power systems,
utility practitioners have observed that CLM parameters carefully selected for
one fault event may not perform satisfactorily in another fault. Here, we
innovate a joint conditional diffusion model-based inverse problem solver
(JCDI), that incorporates a joint conditioning architecture with simultaneous
inputs of multi-event observations to improve parameter generalizability.
Simulation studies on the WECC CLM show that the proposed JCDI effectively
reduces uncertainties of degenerate parameters, thus the parameter estimation
error is decreased by 42.1% compared to a single-event learning scheme. This
enables the model to achieve high accuracy in predicting power trajectories
under different fault events, including electronic load tripping and motor
stalling, outperforming standard deep reinforcement learning and supervised
learning approaches. We anticipate this work will contribute to mitigating
parameter degeneracy in system dynamics, providing a general parameter
estimation framework across various scientific domains.
Source: http://arxiv.org/abs/2411.10431v1
