Open AccessMultivariate statistical analysis‐based power‐grid‐partitioning method
Author(s) -
Ge Huaichang,
Guo Qinglai,
Sun Hongbin,
Wang Bin,
Zhang Boming
Publication year - 2016
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.2015.0796
Subject(s) - computer science , electric power system , grid , ac power , multivariate statistics , selection (genetic algorithm) , voltage , sensitivity (control systems) , power grid , power network , node (physics) , power (physics) , state (computer science) , data mining , engineering , electronic engineering , algorithm , mathematics , artificial intelligence , machine learning , electrical engineering , physics , geometry , structural engineering , quantum mechanics
A multivariate statistical analysis (MSA)‐based power‐grid‐partitioning method is proposed. Considering the effectiveness of automatic voltage regulation of generators, network modelling is based on the quasi‐steady‐state sensitivity of voltage with respect to reactive power. The MSA‐based power‐grid‐partitioning method contains two stages: a quantitative method for determining the appropriate number of zones and allocation of nodes into a number of zones. The proposed method is applied to an automatic voltage control system and an N − 1 robust pilot node selection method is presented. Simulation studies on IEEE 14‐bus, 39‐bus systems and a real provincial power system in China are described to illustrate the effectiveness of the proposed method.