(untitled) (International Journal of Engineering and Computer Science, 2017)
: With an increasing usage of sensitive electronic equipment power quality has become a major concern now. One critical aspect of power quality studies is the ability to perform automatic power quality data analysis. The impact of power quality on the operation of sensitive equipment has been illustrated through simulations in MATLAB SIMULINK. Such study is essential to predict the performance of modern loads and also to be able to explain why a specific load fails during a power quality event. The findings are reported in detail in this paper. The paper proposes a neural network solution to the indirect vector control of three phase induction motor including a real-time trained neural controller for the IM angular velocity, which permitted the speed up reaction to the variable load. The basic equations and elements of the indirect field oriented control scheme are given. The control scheme is realized by one recurrent and two feed-forward neural networks. The first one is learned in real-time by the dynamic BP method and the two FFNNs are learned off-line by the Levenberg-Marquardt algorithm with data taken by PIcontrol simulations. The final set up MSE of the LM algorithm is of 10-10. The graphical results of modeling show a better performance of the adaptive NN control system with respect to the PI controlled system realizing the same computational control scheme with variable load.