Inverter Model Validation and Calibration Using Phasor Measurement Unit Data

Abstract

As the penetration of inverter-based renewable energy resources increases in the power grid, especially at the distribution and microgrid levels, the need to accurately represent them in planning studies increases as well. However, due to the lack of well-established standard procedures, and vendor reluctance towards the detailed sharing of proprietary models, automated dynamic model validation and parameter calibration tools for inverter based resources (IBRs) remain scarce. This work presents a model validation and parameter calibration platform for representing IBRs with generic phasor-domain models. Phasor measurements of power system events are used for continuous validation using the data playback method, and model parameters are re-calibrated if a significant mismatch between measurements and model response is observed. Unique features of the proposed platform include- 1) an iterative Bayesian optimization approach towards parameter calibration to address potential differences between the structures of generic models implemented in simulation softwares and commercial inverters, 2) error metrics designed to account for a possible mismatch between the time resolution of simulation and measurements, and 3) analysis of the measurement-simulation discrepancies to provide guidance to engineering personnel regarding model shortcomings. The performance of the platform has been illustrated using both simulated data and field measurements to validate/calibrate inverter models in GridLAB-D. Numeric tests show that the proposed iterative calibration algorithm is more accurate and computationally efficient than the standard Bayesian Optimization approach. The computation time required by the iterative calibration algorithm is less than half of that needed by the standard approach.

Keywords

• Model validation
• parameter calibration
• inverter-based resources
• Bayesian optimization