Generator Model Validation and Calibration using Synchrophasor Measurements

Seyedeh Armina Foroutan

doi:10.7273/000006436

Planning and operation of power grid highly depends on the analytical studies using models representing the electric assets in the field. Therefore, the accuracy of these models is very critical. Model validation and calibration of generating units are important to ensure the compliance of the models with the actual behavior of the generating assets. The differential Algebraic Equations (DAEs) of these models have been obtained by diligent and extensive physical modeling and insight resulting into usually standard models, but contain some parameters that have unknown or uncertain numerical values over time. Parameter calibration of these Grey-box models, is quite challenging and has not yet been solved in full generality. The traditional methods involve taking generators off-line for intrusive experimental validation and manual calibration. However, Phasor Measurement Units (PMUs) provide synchronized measurements with high sampling rates and create an opportunity to avoid offline testing and achieve a cost efficient solution. Online testing using synchrophasors usually involves one form of Bayesian based Filtering (BbF) approaches to estimate states and parameters simultaneously. Traditionally, parameter calibration associated with transmission level bulk generators, but with increasing penetration level of Distributed Energy Resources (DER), their dynamic behaviour is no longer negligible. DER aggregators can be considered virtual power plants and along with distributed generators are the candidates for model validation. In fact, model calibration at distribution level is not only worth the study and effort, but seems necessary. The task of generating units parameter estimation is quite challenging due to the dependency between parameters and states causing stability and convergence issues, large number of parameters, the quality of synchrophasors and their integration with commercial modeling tools. This dissertation provides a detailed comparative analysis for conventional Kalman-based and Bayesian-based Filtering approaches with the associated numerical stability and convergence problem and proposed novel algorithms and architectures for generating units online parameter calibration. This study targeted the calibration issue in different levels of the power system, i.e. transmission and distribution and provides framework to integrate proposed methods with commercial tools.

Generator Model Validation and Calibration using Synchrophasor Measurements

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