Search

Due to the increasing number of power electronics-based devices in modern power systems, there are concerns about the impact of higher-frequency interactions on system stability. The typically-used algebraic representation of networks fails to capture these newly emergent issues, but dynamically modelling the entire network with differential equations will lead to a prohibitively long simulation time. This paper proposes a computationally-efficient analysis framework to determine the system loadability in power electronics-rich, large networks with the consideration of multiple types of stabilities. The framework developed in this paper identifies the critical network elements based on the eigenvalue sensitivity in order to exploit a hybrid modelling approach. Within the hybrid model, only the critical network portion is modelled dynamically with the rest of the network represented by algebraic equations. Bifurcation theory is used to simultaneously analyze both small-disturbance rotor angle and small-disturbance voltage stability. The results obtained show that the high-frequency interactions are accurately captured with the proposed framework. Additionally, applying this methodology results in a small dimension for the system matrix and a reduction in the computational burden. Two test networks, a three-bus system and the IEEE 39-bus system, are used to illustrate and verify the analysis framework.

It is anticipated that a large number of voltage source converters (VSCs) will be integrated into future power systems, which can potentially be detrimental to system stability. Previous work has utilized an impedance-based approach to analyze the feasibility region boundaries of power systems with multiple VSCs. However, impedance-based modeling limits the analysis of the system feasibility region to just two or three dimensions. Hence, this paper develops a methodology based on bifurcation theory that enables the system multi-dimensional feasibility region to be identified efficiently with consideration of multiple different varying parameters. The developed methodology is generalized so that it can be applied to identify system feasibility region boundaries in other power systems with multiple varying parameters. The partial Spearman correlation coefficient is adopted in this paper to identify the key parameters that affect the feasibility region boundary. Additionally, the calculated correlation indices quantify the interactions between the control loops VSCs in the system. The proposed methodology is verified against other analytical methods and the impact of key parameter variation on the system feasibility region boundaries is discussed.