Stator Resistance Identification based on Neural and Fuzzy Logic Principles in an Induction Motor Drive

Abstract

This paper presents a method for stator resistance identification of an induction motor in an indirect rotor field oriented control system. This method is based on a simple artificial neural network, in which the rotor time constant is no longer considered to be a constant parameter, but is instead identified using an adaptive model reference system-based procedure. The neural network outputs the estimated rotor speed. The difference between the actual and the estimated rotor speed is used as a signal for either manual or automated fuzzy logic stator resistance identification. Simulations and experiments show the effectiveness of the described approach.

Dinko Vukadinović

Full Professor | Department of Power Electronics and Control

Full professor at the Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture in Split, specialized in modern control systems for power electronic converters, electric motors, and generators. At the Power Electronics Research Laboratory, he leads experimental projects and develops advanced methods for regulating electrical machines and converters, while supervising doctoral research in these areas.

Mateo Bašić

Full Professor | Department of Power Electronics and Control

Full professor at the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture in Split, with recent research interests related to the fields of power electronics and renewable energy sources, with a special focus on energy-efficient control of inverters, battery systems, wind turbines, photovoltaic sources and self-excited induction generators in microgrids - both in island operation and in grid-tie operation.