Intelligent Universal Transformers Online Adaptive Control via Artificial Neural Network

Abstract

Adaptive control is a novel methodology introducing for dynamic identification and control of nonlinear system in case of unknown parameters and absence of precise mathematical model. The Artificial Neural Networks (ANNs) suggest the parallel algorithm in resolving paradigms and result on a robust control fashion in which learning algorithm resembling to the biological brain. Back propagation algorithm is proposed for updating the ANN weighting factors through the on line learning procedure. This research is carry out to investigate the ANN trained algorithm to elaborate the switching angle signals for controlling the Intelligent Universal transformer (IUT) in input and output stages. IUT motive the Advanced Distribution Automation (ADA) with the new invention in automation, management and control. ANN online adaptive scheme is developed for controlling the input current and output voltages of IUT with the major benefits and service option advantages, comprising from a voltage regulation in real time operation, capability on providing three phase power outputs in case of one phase input, energy storage capability,48V DC output option, harmonic Filtering, reliable divers power 240V AC 400HZ for communication usage together with two 240 V AC 60 HZ outputs, automatic sag correction, dynamic system monitoring and system robustness in term of input and load disturbances.