Open AccessRobust neural network‐based control of static var compensator
Author(s) -
Chang YeongChan
Publication year - 2014
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2013.0650
Subject(s) - control theory (sociology) , artificial neural network , weighting , computer science , robust control , electric power system , controller (irrigation) , stability (learning theory) , adaptive control , static var compensator , robustness (evolution) , control system , control engineering , power (physics) , control (management) , engineering , artificial intelligence , machine learning , biochemistry , physics , chemistry , quantum mechanics , biology , gene , electrical engineering , radiology , medicine , agronomy
This study addresses the problem of designing robust stabilisation control for a large class of uncertain single‐machine infinite‐bus electrical power systems with static var compensator (SVC). This class of systems may be perturbed by plant uncertainties, unmodelled perturbations and external disturbances. An adaptive neural network‐based dynamic feedback controller is developed such that all the states and signals of the closed‐loop system are bounded and the stabilisation error can be made as small as possible. As the small perturbations in the input weighting gains are neglected, an H ∞ control performance can be guaranteed. The adaptive neural network approximation systems are designed to learn the behaviours of the unknown functions, and in turn a modified procedure is proposed such that the number of the neural network basis functions can be significantly reduced. Consequently, the intelligent robust control scheme developed here possesses the properties of computational simplicity and easy implementation from the viewpoint of practical applications. The developed robust control scheme not only can handle a large class of uncertain SVC‐driven power systems, but also achieve the aim of enhancing the stability performance. Finally, simulations are provided to demonstrate the effectiveness and performance of the proposed control algorithm.