Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method

On the identifiability of steady-state induction machine models using external measurements

A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose

Improved flux pattern by third harmonic injection for multiphase induction machines using neural network

This paper presents a modified V/f control strategy using neural network with an improved flux pattern using third harmonic injection for multiphase induction machines. The control objective is to generate a nearly rectangular air–gap flux, resulting in an improved machine power density for the required speed range. If just a proportional relation is used between the third harmonic and fundamental plane voltage magnitudes with zero phase shift, variable misalignment between fundamental and third air–gap flux components occurs with varying mechanical loading as a result of stator voltage drop. Due to this misalignment, saturation may take place which affects the total flux and increases machine iron losses. Neural network is used to obtain the required injected voltage phasors magnitudes and angles to ensure that the air–gap flux is near rectangular with a maximum value of 1 pu for all loading conditions. Simulations are carried out on an eleven-phase induction machine to validate the proposed controller using MATLAB/Simulink

Hybrid Model-Based Fuzzy Logic Diagnostic System for Stator Faults in Three-Phase Cage Induction Motors

The widespread use of three-phase cage induction motors in so many critical industrial, commercial and domestic applications means that there is a real need to develop online diagnostic systems to monitor the state of the machine during operation. This paper presents a hybrid diagnostic system that combines a model-based strategy with a fuzzy logic classifier to identify abnormal motor states due to single-phasing or inter-turn stator winding faults. Only voltage and current measurements are required to extract the fault symptoms, which are represented as model parameters variations in an equivalent virtual healthy motor, negating the need to use complex models of faulty machines. A trust-region method is used to estimate the machine model parameters, with the final decision on the type, location and extent of the fault being made by the fuzzy logic classifier. The proposed diagnostic system was experimentally verified using a 1.0 hp three-phase test induction motor. Results show that the proposal method can efficiently diagnose single phasing and inter-turn stator winding faults even when operating with unbalanced supply voltages and in the presence of significant levels of measurement noise