ﻻ يوجد ملخص باللغة العربية
In this paper, a data-driven approach to characterize influence in a power network is presented. The characterization is based on the notion of information transfer in a dynamical system. In particular, we use the information transfer based definition of influence in a dynamical system and provide a data-driven approach to identify the influential state(s) and generators in a power network. Moreover, we show how the data-based information transfer measure can be used to characterize the type of instability of a power network and also identify the states causing the instability.
In this paper, we propose a data-driven energy storage system (ESS)-based method to enhance the online small-signal stability monitoring of power networks with high penetration of intermittent wind power. To accurately estimate inter-area modes that
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 o
In this paper, we present a novel approach to identify the generators and states responsible for the small-signal stability of power networks. To this end, the newly developed notion of information transfer between the states of a dynamical system is
Power grid parameter estimation involves the estimation of unknown parameters, such as inertia and damping coefficients, using observed dynamics. In this work, we present a comparison of data-driven algorithms for the power grid parameter estimation
We consider the problem of stability analysis for distribution grids with droop-controlled inverters and dynamic distribution power lines. The inverters are modeled as voltage sources with controllable frequency and amplitude. This problem is very ch