Open AccessVoltage stability assessment and identification of important nodes in power transmission network through network response structural characteristics
Author(s) -
Adebayo Isaiah,
Jimoh Adisa A.,
Yusuff Adedayo
Publication year - 2017
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/iet-gtd.2016.0745
Subject(s) - centrality , eigenvalues and eigenvectors , spectral graph theory , graph theory , electrical network , topology (electrical circuits) , electric power system , voltage , computer science , vertex (graph theory) , node (physics) , grid , identification (biology) , power network , stability (learning theory) , graph , mathematics , power (physics) , engineering , theoretical computer science , line graph , physics , electrical engineering , biology , botany , quantum mechanics , machine learning , geometry , structural engineering , combinatorics , graph power
Electrical power grids are often susceptible to voltage instability and have been a growing concern. In this study, the authors propose techniques based on network response structural characteristics for voltage stability assessment and for the identification of important nodes in electrical power grids. First, the network response structural characteristic indices (NRSCIs) which are inherent in the conventional power grids in terms of the Kirchhoff matrix is formulated. The eigenvalue decomposition (ED) technique is then applied to a submatrix of the Kirchhoff matrix to determine the topological strength of the electrical network graph. The vertex (node) that has a minimum eigenvalue is taken as the critical mode, from which its contributions to the entire graph is identified using the proposed network response structural characteristics theory participation factor (NRSCTPF). To demonstrate the magnitude of the concept formulated, the known degree centrality is modified in terms of the established NRSCI, which are then used to determine the important nodes of the network graph. The results obtained show that the proposed NRSCTPF and the degree centrality based on the NRSCI can be used for voltage stability assessment and identification of important nodes. The proposed approach is also less computationally intensive compared with the traditional approach.