Performance comparison of neural architectures for on-line flux estimation in sensor-less vector-controlled IM drives

Abstract

The sensor-less vector-controlled induction motor drive requires accurate estimation of speed and flux. The speed estimation depends on the motor flux, which has to be measured or estimated. The flux measurement is difficult and expensive and hence generally estimated. Conventional voltage model equations for flux estimation encounter major drawbacks at low frequencies/speed. Neural network-based estimator provides an alternate solution for on-line flux estimation. The on-line flux estimator requires the neural network model to be accurate, simpler in design, structurally compact, and computationally less complex to ensure faster execution time in real-time implementation for effective control. This in turn, to a large extent, depends on the type of neural architecture. This paper investigates three types of neural architectures for on-line flux estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity, and execution time. The suitable neural architecture for on-line flux estimation is identified and the promising results obtained are presented.