Data-Driven Power Electronic Converter Modeling for Low Inertia Power System Dynamic Studies

TL;DR

This paper introduces a data-driven black-box modeling approach for power electronic converters, enabling efficient system-level stability analysis in low inertia power grids with high renewable integration.

Contribution

It presents a novel system identification method to derive linear dynamic models of converters using real-time digital simulation, simplifying large-scale power system studies.

Findings

Effective linear models for converters are derived.

Models facilitate large system stability analysis.

Approach reduces computational complexity.

Abstract

A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.