Open AccessCritical node identification in active distribution network using resilience and risk theory
Author(s) -
Zhang Wen,
Liu Keyan,
Sheng Wanxing,
Du Songhuai,
Jia Dongli
Publication year - 2020
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.2019.1781
Subject(s) - node (physics) , identification (biology) , warning system , computer science , resilience (materials science) , stability (learning theory) , reliability engineering , electric power system , data mining , engineering , power (physics) , machine learning , telecommunications , botany , physics , structural engineering , quantum mechanics , biology , thermodynamics
Accurately identifying the key links in active distribution network (ADN) that affect the system's safety and stability is of great significance to improve the efficiency of operation and maintenance and risk early warning for distribution network (DN). In this study, the node resilience (NR) is first proposed as an indicator for structural stability identification of DN. Then, a critical node identification method considering the security, economy and structure stability in ADN is proposed, which uses the NR, the utility risks of branch load change, node voltage deviation and line loss variation as the identification indicators. Also, combining with subjective experience and objective data, the comprehensive evaluation method of critical nodes is improved, and the multilevel and multidimensional evaluation model of critical nodes is constructed. Furthermore, the time sequence of load change and photovoltaic output is considered. Finally, the rationality and effectiveness of the proposed method are proved by taking IEEE 33‐bus system and an actual ADN as examples.