Open AccessOptimal placement of static compensators for multi‐objective voltage stability enhancement of power systems
Author(s) -
Xu Yan,
Dong Zhao Yang,
Xiao Chixin,
Zhang Rui,
Wong Kit Po
Publication year - 2015
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.2015.0070
Subject(s) - electric power system , pareto principle , control theory (sociology) , stability (learning theory) , voltage , transient (computer programming) , transient voltage suppressor , mathematical optimization , ac power , decomposition , multi objective optimization , computer science , power (physics) , engineering , mathematics , physics , control (management) , quantum mechanics , artificial intelligence , machine learning , electrical engineering , ecology , biology , operating system
Static compensators (STATCOMs) are able to provide rapid and dynamic reactive power support within a power system for voltage stability enhancement. While most of previous research focuses on only an either static or dynamic (short‐term) voltage stability criterion, this study proposes a multi‐objective programming (MOP) model to simultaneously minimise (i) investment cost, (ii) unacceptable transient voltage performance, and (iii) proximity to steady‐state voltage collapse. The model aims to find Pareto optimal solutions for flexible and multi‐objective decision‐making. To account for multiple contingencies and their probabilities, corresponding risk‐based metrics are proposed based on respective voltage stability measures. Given the two different voltage stability criteria, a strategy based on Pareto frontier is designed to identify critical contingencies and candidate buses for STATCOM connection. Finally, to solve the MOP model, an improved decomposition‐based multi‐objective evolutionary algorithm is developed. The proposed model and algorithm are demonstrated on the New England 39‐bus test system, and compared with state‐of‐the‐art solution algorithms.