A Parameter Error Identification Method for Power Plant Dynamic Models

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2022-6-30

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Açılan, Etki

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Incorrect parameters in a power plant dynamic model affect the analysis results and control decisions in a power system, which may have serious consequences. The online calibration techniques in the literature utilize sensitivity analysis to determine the identifiable parameters before the calibration, and treat majority of sensitive parameters as potentially erroneous. Hence, the candidate parameter subset for calibration mostly consists of distractors, i.e. highly sensitive but already correct parameters. The inclusion of distractors in the parameter calibration may decrease the accuracy of calibration, and increase the computation time. This thesis proposes a method to identify erroneous parameters through the mismatch between collected PMU measurements and power plant model response. The proposed method relies on the classification of Recurrence Plots of the mismatch using 2-D Convolutional Neural Networks, and identifies the erroneous parameters via the orthogonal decomposition. Hence, a smaller subset of candidate parameters is obtained by eliminating the distractors.

Subject Keywords

Power Plant Parameter Calibration, Convolutional Neural Networks, Dynamic Parameter Estimation, Collinearity, Identifiability, Time Series Classification

URI

https://hdl.handle.net/11511/98192

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Açılan, “A Parameter Error Identification Method for Power Plant Dynamic Models,” M.S. - Master of Science, Middle East Technical University, 2022.