Open AccessA statistical jacobian application for power system optimization of voltage stability
Author(s) -
Raja Masood Larik,
Mohd Wazir Mustafa,
Manoj Kumar Panjwani
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v13.i1.pp331-338
Subject(s) - jacobian matrix and determinant , electric power system , voltage , margin (machine learning) , stability (learning theory) , control theory (sociology) , power flow , eigenvalues and eigenvectors , power (physics) , computer science , mathematics , engineering , electrical engineering , control (management) , physics , artificial intelligence , quantum mechanics , machine learning
Despite a tremendous development in optimal power flow (OPF), owing to the obvious complexity, non-linearity and unwieldy size of the large interconnected power systems, several problems remain unanswered in the existing methods of OPF. Seizing specific topics for maximizing voltage stability margin and its implementation, a detailed literature survey discussing the existing methods of solution and their drawbacks is presented in this research. The phenomenon of voltage collapse in power systems, methods to investigate voltage collapse, and methods related to voltage stability are briefly surveyed. Finally, the study presents a statistical method for analyzing a power system through eigenvalue analysis in relation to the singular values of the load flow Jacobian. Future study may focus on changes in theories in conjunction with large power systems.