Robust neural network‐based control of static var compensator

Abstract

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.