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Advanced Performance Metrics and Sensitivity Analysis for Model Validation and Calibration

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posted on 11.08.2020 by Urmila Agrawal, Pavel Etingov, Renke Huang
High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.

Funding

U.S. Department of Energy's Office of Electricity through CERTS program

History

Email Address of Submitting Author

urmila.agrawal@pnnl.gov

ORCID of Submitting Author

0000-0002-6402-0399

Submitting Author's Institution

Pacific Northwest National Laboratory

Submitting Author's Country

United States of America

Licence

Exports