Fast multilayer perceptron neural network-based control algorithm for shunt compensator in distribution systems

In this study, a fast learning method of back-propagation (BP) multilayer perceptron neural network-based control algorithm for shunt compensator in three-phase distribution systems is presented. The proposed method comprises of quadratic linear and non-linear errors to determine optimisation criterion error function to train the BP algorithm while the existing methods have used only linear quadratic error term. The newly developed optimisation criterion error function accelerates the convergence efficiency of BP algorithm for performance improvement of shunt compensator at point of common coupling under non-linear loading conditions. With the help of the proposed algorithm, the weighted amplitude of fundamental active and reactive current components of the load current are extracted from which the reference source currents are estimated. The performance analysis of the proposed algorithm has been evaluated using two case studies for zero-voltage regulation and power factor correction. The total harmonic distortions are improved in comparison with standard BP algorithm which has been validated in above-mentioned two case studies. This is the quite important advantage of the proposed control algorithm to improve the power quality over existing control algorithms for shunt compensator.