This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Load Flow Analysis in Power System Network Incorporating STATCOM: A Comparison of the Direct and Indirect Algorithm of the Newton-Raphson Method

This article presents the modeling of overcurrent and differential relays in MATLAB/Simulink. The overcurrent relay has the features of instantaneous, time-definite, and inverse definite minimum time (IDMT) characteristics according to IEC 60255 and IEEE C37.112-1996 standards. The 220 kV km transmission systems are selected as examples for fault simulation and relay testing for each relay model and the coordination protection. The simulation results are given and a description of how this case study can be used to compare protection schemes.