Qualitative Stability and Synchronicity Analysis of Power Network Models in Port-Hamiltonian Form

TL;DR

This paper introduces a novel energy-based port-Hamiltonian formulation of the Kuramoto model for power network analysis, enhancing robustness, physical fidelity, and extensibility in stability and synchronization studies.

Contribution

It presents a new port-Hamiltonian differential-algebraic system formulation of the Kuramoto model, improving robustness and physical consistency for power network analysis.

Findings

Robust representation of power networks with disturbances

Explicit encoding of physical principles like dissipation and synchronization

Enhanced extensibility for complex and coupled systems

Abstract

In view of highly decentralized and diversified power generation concepts, in particular with renewable energies such as wind and solar power, the analysis and control of the stability and the synchronization of power networks is an important topic that requires different levels of modeling detail for different tasks. A frequently used qualitative approach relies on simplified nonlinear network models like the Kuramoto model. Although based on basic physical principles, the usual formulation in form of a system of coupled ordinary differential equations is not always adequate. We present a new energy-based formulation of the Kuramoto model as port-Hamiltonian system of differential-algebraic equations. This leads to a very robust representation of the system with respect to disturbances, it encodes the underlying physics, such as the dissipation inequality or the deviation from synchronicity, directly in the structure of the equations, it explicitly displays all possible constraints and allows for robust simulation methods. Due to its systematic energy based formulation the model class allows easy extension, when further effects have to be considered, higher fidelity is needed for qualitative analysis, or the system needs to be coupled in a robust way to other networks. We demonstrate the advantages of the modified modeling approach with analytic results and numerical experiments.