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On the Local Identifiability of Load Model Parameters in Measurement-based Approach
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 Title & Authors
On the Local Identifiability of Load Model Parameters in Measurement-based Approach
Choi, Byoung-Kon; Chiang, Hsiao-Dong;
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 Abstract
It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.
 Keywords
Local identifiability;Load modeling;Correlation matrix;Measurement-based approach;Parameter estimation;
 Language
English
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