Open AccessIntentional controlled islanding based on dynamic community detection for power grid
Author(s) -
Han Xu,
Huangpeng Qizi,
Duan Xiaojun,
Gao Qiannan,
Yin Yimin
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12591
Subject(s) - islanding , betweenness centrality , computer science , grid , electric power system , power (physics) , computation , power flow , dynamic demand , enhanced data rates for gsm evolution , algorithm , mathematical optimization , centrality , mathematics , artificial intelligence , physics , geometry , quantum mechanics , combinatorics
Power system controlled islanding is an emergency control method used to stop the propagation of disturbances and avoid blackouts. Intentional controlled islanding (ICI) has been proposed as a corrective measure of last resort to split the power system into several sustainable islands. Complex network community detection is widely used in network partitioning and can be used to solve controlled islanding problems. The objective of this study is to apply the dynamic network community detection method to solve ICI problems. Using dynamic network modelling, the division‐agglomeration (Di‐Ag) algorithm is proposed. Dynamic modelling presets the computation task of the algorithm in the healthy grid phase. The algorithm balances the efficiency of the algorithm, power imbalance, and power flow disruption, without sacrificing other objectives to optimise one objective. Specifically, the grid topology is taken into account when the grid is divided using betweenness as edge weight. The first step of the Di‐Ag algorithm uses the dynamic Girvan–Newman algorithm to achieve the goal of power imbalance. Moreover, only the islands' involved lines (nodes) are updated, which improves the efficiency of the algorithm. In the second step of the Di‐Ag algorithm, the algorithm merges the islands to reduce power flow disruption. The IEEE 39‐bus and 118‐bus system are used to compare the performance of the proposed dynamic modelling method and the static network community discovery method in three different cases.