Open AccessImproving power system stability in the presence of wind farms using STATCOM and predictive control strategy
Author(s) -
Darabian Mohsen,
Jalilvand Abolfazl
Publication year - 2018
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2016.0812
Subject(s) - control theory (sociology) , model predictive control , weighting , stability (learning theory) , rotor (electric) , laguerre polynomials , matlab , electric power system , controller (irrigation) , wind power , ac power , computer science , power (physics) , engineering , control engineering , voltage , control (management) , mathematics , artificial intelligence , medicine , mechanical engineering , mathematical analysis , agronomy , physics , electrical engineering , quantum mechanics , machine learning , biology , radiology , operating system
In this study, a multi‐objective predictive control strategy is presented for the stability improvement of a power system in the presence of wind farms and STATCOM. The main contribution of this study is in the multi‐objective consideration for controlling the active and reactive powers of the rotor‐side converter in each of the induction generators, controlling the voltage of the synchronous generators’ excitation system, and designing the damping controller of STATCOM using the predictive strategy. To reduce the computational burden, and to accurately choose the input paths into the predictive control, the Laguerre functions are used. Also, for reducing the sampling time in the selection of large prediction horizons, the exponential data weighting has been employed. The simulation results were evaluated using MATLAB software in the field of time and frequency under different scenarios. Moreover, the obtained results of each domain are compared using the two techniques of the predictive strategy, i.e. the classic model, Laguerre functions, and also the conventional proportional integral controller. The comparison of these three methods reveals that the functional predictive control outfits the two other controllers in damping of the oscillations.