Stator current model reference adaptive systems speed estimator for regenerating‐mode low‐speed operation of sensorless induction motor drives. Issue 7 (1st August 2013)

• Record Type: Journal Article
• Authors: Gadoue, Shady M.; Giaouris, Damian; Finch, John W.
• Is Part Of: IET electric power applications. Volume 7:Issue 7(2013)
• Page Start: 597
• Page End: 606
• Publication Date: 2013-08-01
• Subjects: angular velocity control -- electric current control -- electric current measurement -- feedforward neural nets -- induction motor drives -- learning (artificial intelligence) -- model reference adaptive control systems -- neurocontrollers -- observers -- stability -- stators -- voltage control -- sensorless machine control; stator current model reference adaptive system -- speed estimation -- regenerating‐mode low‐speed operation -- sensorless induction motor drive -- MRAS -- stator current measurement -- two‐layer online‐trained neural network stator current observer -- rotor flux estimation information -- offline trained multilayer feedforward neural network -- rotor flux observer -- stator current estimation -- stability problem; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2013.0091
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : The performance of a stator current‐based model reference adaptive systems (MRAS) speed estimator for sensorless induction motor drives is investigated in this study. The measured stator currents are used as a reference model for the MRAS observer to avoid the use of a pure integrator. A two‐layer, online‐trained neural network stator current observer is used as the adaptive model for the MRAS estimator which requires the rotor flux information. This can be obtained from the voltage or current models, but instability and dc drift can downgrade the overall observer performance. To overcome these problems of rotor flux estimation, an off‐line trained multilayer feed‐forward neural network is proposed here as a rotor flux observer. Hence, two networks are employed: the first is online trained for stator current estimation and the second is off‐line trained for rotor flux estimation. Sensorless operation for the proposed MRAS scheme using current model and neural network rotor flux observers are investigated based on a set of experimental tests in the low‐speed region. Using a neural network rotor flux observer to replace the current model is shown to solve the stability problem in the low‐speed regenerating mode of operation.