Open AccessSVC damping controller design based on novel modified fruit fly optimisation algorithm
Author(s) -
Zhang Kaoshe,
Shi Zhaodi,
Huang Yuehui,
Qiu Chengjian,
Yang Shuo
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2017.0401
Subject(s) - control theory (sociology) , electric power system , controller (irrigation) , probabilistic logic , wind power , cluster (spacecraft) , static var compensator , engineering , computer science , ac power , power (physics) , control engineering , voltage , control (management) , electrical engineering , physics , artificial intelligence , agronomy , biology , programming language , quantum mechanics
Existing reactive power systems do not readily provide support or anti‐disturbance capabilities. This study was conducted to explore the predisposing factor and suppression measures of low‐frequency oscillation in large‐scale wind power cluster systems by establishing a wind farm cluster mode with wind power fluctuation in a DIgSILENT/power factory. Considering the multiple time scale and the operating characteristics of cluster system, a novel modified fruit fly optimisation algorithm (nMFOA) combined with probabilistic sensitivity indices is proposed to coordinate and optimise static VAR compensator (SVC) damping controller parameters to enhance the power system stability of the wind farm cluster. Adverse effects in the SVC damping controller are eliminated via the nMFOA with probabilistic eigenvalue, which can be used to effectively coordinate and optimise SVC parameters. The proposed scheme was tested on a certain wind farm cluster in Hami, Xinjiang Province.