Open AccessReliability interdependencies and causality assessment for a converter‐penetrated power system
Author(s) -
Zhang Bowen,
Wang Mengqi,
Su Wencong
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/gtd2.12470
Subject(s) - interdependence , reliability engineering , reliability (semiconductor) , computer science , converters , bayesian network , electric power system , power (physics) , engineering , machine learning , physics , quantum mechanics , political science , law
This paper utilizes Bayesian network (BN) structure searching and scoring algorithms to identify critical nodes and investigate their reliability interdependencies for a power system under great converter penetration. As more converters are integrated into the system, reliability interactions among various converters will frequently emerge and consequently introduce system reliability concerns. However, reliability causal relations have rarely been explored and demonstrated in a clear manner. Therefore, the authors apply BN structure searching and scoring algorithms to visualize the proposed converter‐based BN structure. Moreover, reliability interactions among different nodes are quantified through information entropy theory. Numerical case studies illustrate the causal reliability relationship among various nodes while considering the reliability of all integrated converters. Critical nodes are identified such that system operators can improve the converter maintenance scheduling.