To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Data-driven online temperature compensation for robust field-oriented torque-controlled induction machines

Squirrel-cage induction machines (IMs) with indirect field-oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data-driven torque compensation to eliminate the temperature effect is proposed to enable robust torque-controlled IMs. A model-based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature-torque lookup-table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel-cage induction motor show that the proposed data-driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of <1% remains in a broad operating range

A New Induction Motor Adaptive Robust Vector Control based on Backstepping

In this paper, a novel approach to nonlinear control of induction machine, recursive on-line estimation of rotor time constant and load torque are developed. The proposed strategy combines Integrated Backstepping and Indirect Field Oriented Controls. The proposed approach is used to design controllers for the rotor flux and speed, estimate the values of rotor time constant and load torque and track their changes on-line. An open loop estimator is used to estimate the rotor flux. Simulation results are presented which demonstrate the effectiveness of the control technique and on-line estimation

Control of Induction Motor Drive using Artificial Neural Network

The induction motor drive is a dynamic nonlinear system with uncertainty in the machine parameters. The aim of this study is to improve tracking performance of the induction motor drive. A method for controlling induction motor drive is presented with conventional Proportional-Integral (PI) controller and Artificial Neural Networks (ANNs) controller. MATLAB/SIMULINK software is used to develop a three phase induction motor model. Also the performances of the two controllers have been verified. The ANN is trained so that the speed of the drive tracks the reference speed. It is found that with the use of the ANN controller the performance and dynamics of the induction motor are enhanced as compared with that of a conventional PI controller

Micro-peat as a potential low-cost adsorbent material for COD and NH3-N removal

Micro-peat (M-P) was demonstrated in the present study as a potential low cost natural adsorbent for the removal of COD and ammoniacal nitrogen (NH3-N) from landfill leachate. A series of batch experiments were carried out under fixed conditions and the influence of mixture ratio was investigated. The characteristics of leachate were then determined. Results indicated that leachate is non-biodegradable with high concentration of COD (2739.06 mg/L), NH3-N (1765.34 mg/L) and BOD5/COD ratio (0.09). The optimum ratio for activated carbon (AC) and M-P in the removal of COD and NH3-N obtained were at 2.5:1.5 (87%) and 1.0:3.0 (65%) respectively. The low-cost natural adsorbent used in the present investigation is an attractive alternative to the conventional adsorbent (AC). Thus, M-P can be appropriated for use in leachate treatment that could be cost-effective due its local availability and adsorption property

Model Reference Adaptive System (MRAS) Based Speed Sensorless Vector Control of Induction Motor Drive

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