Identification of Critical Nodes and Accident Chains of Power Systems under the Effect of Geomagnetic Storms

Qing Liu,Nan Ding,Xiao-Hu Yi,Jin-Long Li,Zhen Li

doi:10.1155/2022/2804781

Qing Liu, Nan Ding + Show 3 more

Open Access

https://doi.org/10.1155/2022/2804781

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Abstract

Geomagnetically induced currents (GICs) generated by geomagnetic disturbances (GMD) during strong magnetic storms may trigger chain failures in power systems. In this paper, a critical node identification method based on the improved LeaderRank algorithm considering the effects of geomagnetic storms is proposed. A chain fault identification process based on the fragility theory of complex systems is proposed to determine the initial fault line by critical nodes and simulate the fault development process to obtain the accident chain set. The effectiveness of the method is verified by using the IEEE RTS79 node system as an example. The research results show that the method can reflect the critical nodes and accident chain set of power systems under different geomagnetic storm effects, and the research results can provide a reference for geomagnetic storm disaster quantification, assessment, and prevention.

Highlights

Solar activity causes rapid changes in the ionosphere in extraterrestrial space resulting in strong geomagnetic disturbances (GMD)
E first type is based on complex system theory, which considers the dynamic process of chain failures to assess the criticality of nodes. ere are mainly cascade models [16, 17], such as branching process models [18, 19], optimal tidal chain outage models [20, 21], and manchester models [22]. ese types of models consider the effect of operating state on cascading fault propagation and ignore the effect of topology. e second type is based on complex network theory, which considers the grid topology and evaluates the node criticality by degree and mesonumber. e main models are the small-world network model [23], scale-free
We propose an improved LeaderRank algorithm that combines the grid topology with the system flow to accurately identify the critical nodes of the grid under the action of geomagnetic storms and to identify the set of possible accident chains caused by node failures

Summary

Introduction

Solar activity causes rapid changes in the ionosphere in extraterrestrial space resulting in strong geomagnetic disturbances (GMD). Zhou et al proposed a power LeaderRank method for identifying important nodes in complex grids by combining the uncertainty of renewable energy, system topology, transmission flow, and load loss due to cascading faults [27]. We propose an improved LeaderRank algorithm that combines the grid topology with the system flow to accurately identify the critical nodes of the grid under the action of geomagnetic storms and to identify the set of possible accident chains caused by node failures. At a certain geoelectric field angle, the reactive power loss of the substation increases with the enhancement of the geoelectric field strength, which is consistent with (4)

Improved LeaderRank Algorithm considering the Influence of Geomagnetic Storm

Conclusion