Document Type
Dissertation - Open Access
Award Date
2024
Degree Name
Doctor of Philosophy (PhD)
Department / School
Electrical Engineering and Computer Science
First Advisor
Junjian Qi
Abstract
Accurate dynamic models are crucial for stable and reliable power system operation, making their validation essential. The accuracy of parameter estimation is influenced by several factors, including available observables, model structure, and estimation methods. However, in dynamic models, estimating parameters can be difficult when some of them are unidentifiable, meaning their values cannot be uniquely determined from the available data. This challenge may stem from limited impact of parameters on the measured outputs, parameter interactions, or poor data quality. Therefore, an identifiability analysis should be conducted prior to parameter estimation or model calibration. This preliminary step helps to determine which parameters can be reliably estimated from the available measurements, allowing for the selection of a subset of identifiable parameters to focus on during calibration. Despite its importance, the analysis of parameter identifiability, a theoretical property that evaluates the ability to estimate a unique set of model parameters from the available data, is often overlooked in power system parameter estimation. This analysis is particularly challenging due to the size and complexity of power system dynamic models, and most existing methods are limited to smaller models. In our research, we address these challenges by developing efficient methods for parameter identifiability analysis tailored for large power system dynamic models. We propose a model combining the strength of statistical methods and the advancement of machine learning methods to enhance the performance of parameter estimation. Our research objectives are: 1) to develop efficient methods for the identifiability analysis of large power system dynamic models, 2) to identify the most sensitive and identifiable subset of parameters for model calibration, and 3) to perform parameter calibration using a model combining approximate Bayesian computation and sequential neural posterior estimation for the selected parameter subset.
Publisher
South Dakota State University
Recommended Citation
Wang, Lei, "Parameter Identifiability Analysis and Calibration for Power System Dynamic Models" (2024). Electronic Theses and Dissertations. 1331.
https://openprairie.sdstate.edu/etd2/1331
Rights Statement
