Parameter Error Identification for Validation and Calibration of Dynamic Models of Inverter-Based Resources

Accurate dynamic models of Inverter-Based Resources (IBRs) are crucial for power system operation and planning as renewable energy grows. In practice, model parameter errors may arise from a variety of conditions and are difficult to pinpoint due to the large number of parameters in IBR models. This paper proposes a framework for detecting, identifying, and estimating parameter errors within IBR models using terminal measurements. The largest normalized Lagrange multiplier method, which was previously designed for the calibration of steady-state models (algebraic equations), is extended to dynamic models for IBRs by its integration into the Kalman filtering framework. It can accurately pinpoint the erroneous parameter that requires calibration without the need of estimating all parameters simultaneously, and also differentiate between model parameter errors and sensor measurement errors. Simulation results from the IEEE 39-bus test system are presented to validate the methodology for both the parameters of physical IBR systems and those of their digital controllers.