Quantifying Transient Dynamics for Microgrid’s Inverter-Based Resources

Power converters are becoming increasingly important in power systems due to the high penetration of inverter-based resources. Converters are used to implement control strategies and to interface different subsystems (e.g., distributed energy resources, loads, batteries, etc.) that produce, store, or consume electrical energy. They are suitable for microgrids because they provide fast operation and the ability to implement control algorithms at the primary and secondary levels. This chapter studies the complexity associated with the emergence of new complex dynamics in power systems that arise with the high penetration of inverter-based resources. Complexity manifests as bifurcation, oscillation, instability, and chaos, which traditional Models or data-based prediction, such as machine learning, cannot predict. It is mainly due to power electronics nonlinearity, coupling between these components, different feedbacks such as control, and human and environmental feedbacks. Complexity quantification is crucial to modern microgrids with high penetration of inverter-based resources as it allows them to indicate the system's vulnerability, instability, and hidden problems. In this chapter, two complexity metrics are investigated and computed using the permutation entropy approach and ordinal pattern technique, and approximate entropy. Various case studies are conducted on simulated data from noise-coupled buck converters, two parallel-connected buck converters, and solid-state transformers integrated into a microgrid.