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

Detection and Diagnosis of Motor Stator Faults using Electric Signals from Variable Speed Drives

Motor current signature analysis has been investigated widely for diagnosing faults of induction motors. However, most of these studies are based on open loop drives. This paper examines the performance of diagnosing motor stator faults under both open and closed loop operation modes. It examines the effectiveness of conventional diagnosis features in both motor current and voltage signals using spectrum analysis. Evaluation results show that the stator fault causes an increase in the sideband amplitude of motor current signature only when the motor is under the open loop control. However, the increase in sidebands can be observed in both the current and voltage signals under the sensorless control mode, showing that it is more promising in diagnosing the stator faults under the sensorless control operation

Advanced control of induction motors

The current industrial standard for the control of the induction motor is the so called vector control (VC) or field-orientated control (FOC) which transforms and controls the induction motor as a direct current (DC) motor. Besides its many advantages, such as fast and decoupled dynamics of speed and flux, it is well known that VC depends on the detailed system model and is very sensitive to parameter uncertainties and external disturbance (load torque). To clarify further the VC is a only a partial feedback linearising control which can achieve the decoupling of speed and flux asymptotically. The coupling still exists when flux is not kept in constant, i.e. when flux is weakened in order to operate the motor at a higher speed and keep the input voltage within saturation limits, or when flux is adjusted to maximize power efficiency of the motor with light load. The thesis will summarise research of advanced control approaches of induction motors in Chapter One. The Chapter Two starts on building a fifth-order nonlinear dynamic model of an induction motor and then recalls the principal of traditional VC of induction motors. The differential-geometric technique based nonlinear control has developed for induction motors, which can convert some intractable nonlinear problems into simpler problems by familiar linear system methods. The partial decoupled dynamic of the conventional VC has been investigated via feedback linearisation control (FLC) at first. Then input-output linearisation control is applied to design a fully decoupled control of the dynamics of speed and flux. To remove the weak robustness and the requirement of an accurate model of the VC and FLC, a novel nonlinear adaptive control of induction motor is designed based on feedback linearisation control and perturbation estimation. The induction motor will be represented as a two coupled interconnected subsystems: rotor speed subsystem and rotor flux subsystem, respectively. System perturbation terms are defined to include the lumped term of system nonlinearities, uncertainties, and interactions between subsystems and are represented as a fictitious state in the state equations. Then perturbations are estimated by designing perturbation observers and the estimated perturbations are employed to cancel the real system perturbations, assumed all internal states are measured. The designed nonlinear adaptive control doesn’t require the accurate model of the induction motor and has a simpler algorithm. It can fully decouple the regulation of rotor speed and rotor flux and handle time-varying uncertainties. The parameter estimations based on nonlinear adaptive controls can only deal with unknown constant parameters and are not suitable for handling fast time-varying and functional uncertainties. Nonlinear adaptive control based on output measurements is addressed in Chapter Five, assuming that the rotor speed and the stator volatge/currents are measurable. A sliding mode rotor flux observer has been designed based on the stator voltage and current. Moreover, two third-order state and perturbation observers are designed to estimate the unmeasured states and perturbation, based on the rotor speed and the estimated rotor flux. Simulation studies have been carried out for verifying the effectiveness of the proposed advanced controllers and compared with the conventional VC and model based FLC

Indirect Field Oriented Control of Induction Motors is Robustly Globally Stable

Field orientation, in one of its many forms, is an established control method for high dynamic performance AC drives. In particular, for induction motors, indirect fieldoriented control is a simple and highly reliable scheme which has become the de facto industry standard. In spite of its widespread popularity no rigorous stability proof for this controller was available in the literature. In a recent paper (Ortega et al, 1995) [Ortega, R., D. Taoutaou, R. Rabinovici and J. P. Vilain (1995). On field oriented and passivity-based control of induction motors: downward compatibility. In Proc. IFAC NOLCOS Conf., Tahoe City, CA.] we have shown that, in speed regulation tasks with constant load torque and current-fed machines, indirect field-oriented control is globally asymptotically stable provided the motor rotor resistance is exactly known. It is well known that this parameter is subject to significant changes during the machine operation, hence the question of the robustness of this stability result remained to be established. In this paper we provide some answers to this question. First, we use basic input-output theory to derive sufficient conditions on the motor and controller parameters for global boundedness of all solutions. Then, we give necessary and sufficient conditions for the uniqueness of the equilibrium point of the (nonlinear) closed loop, which interestingly enough allows for a 200% error in the rotor resistance estimate. Finally, we give conditions on the motor and controller parameters, and the speed and rotor flux norm reference values that insure (global or local) asymptotic stability or instability of the equilibrium. This analysis is based on a nonlinear change of coordinates and classical Lyapunov stability theory

Accurate Inverter Error Compensation and Related Self-Commissioning Scheme in Sensorless Induction Motor Drives

This paper presents a technique for accurately identifying and compensating the inverter nonlinear voltage errors that deteriorate the performance of sensorless field-oriented controlled drives at low speed. The inverter model is more accurate than the standard signum-based models that are common in the literature, and the self-identification method is based on the feedback signal of the closed-loop flux observer in dc current steady-state conditions. The inverter model can be identified directly by the digital controller at the drive startup with no extra measures other than the motor phase currents and dc-link voltage. After the commissioning session, the compensation does not require to be tuned furthermore and is robust against temperature detuning. The experimental results, presented here for a rotor-flux-oriented SFOC IM drive for home appliances, demonstrate the feasibility of the proposed solution

Self-Commissioning Algorithm for Inverter Non-Linearity Compensation in Sensorless Induction Motor Drives

In many sensorless field-oriented control schemes for induction motor (IM) drives, flux is estimated by means of measured motor currents and control reference voltages. In most cases, flux estimation is based on the integral of back-electromotive-force (EMF) voltages. Inverter nonlinear errors (dead-time and on-state voltage drops) introduce a distortion in the estimated voltage that reduces the accuracy of the flux estimation, particularly at low speed. In the literature, most of the compensation techniques of such errors require the offline identification of the inverter model and offline postprocessing. This paper presents a simple and accurate method for the identification of inverter parameters at the drive startup. The method is integrated into the control code of the IM drive, and it is based on the information contained in the feedback signal of the flux observer. The procedure applies, more in general, to all those sensorless ac drives where the flux is estimated using the back-EMF integration, not only for IM drives but also for permanent-magnet synchronous motor drives (surface-mounted permanent magnet and interior permanent magnet). A self-commissioning algorithm is presented and tested for the sensorless control of an IM drive, implemented on a fixed-point DSP. The feasibility and effectiveness of the method are demonstrated by experimental result

A general magnetic-energy-based torque estimator: validation via a permanent-magnet motor drive

This paper describes the use of the current–flux-linkage ($i{-}psi$ ) diagram to validate the performance of a general magnetic-energy-based torque estimator. An early step in the torque estimation is the use of controller duty cycles to reconstruct the average phase-voltage waveform during each pulsewidth-modulation (PWM) switching period. Samples over the fundamental period are recorded for the estimation of the average torque. The fundamental period may not be an exact multiple of the sample time. For low speed, the reconstructed voltage requires additional compensation for inverter-device losses. Experimental validation of this reconstructed waveform with the actual PWM phase-voltage waveform is impossible due to the fact that one is PWM in nature and the other is the average value during the PWM period. A solution to this is to determine the phase flux-linkage using each waveform and then plot the resultant $i{-}psi$ loops. The torque estimation is based on instantaneous measurements and can therefore be applied to any electrical machine. This paper includes test results for a three-phase interior permanent-magnet brushless ac motor operating with both sinusoidal and nonsinusoidal current waveforms

Linear motor motion control using a learning feedforward controller

The design and realization of an online learning motion controller for a linear motor is presented, and its usefulness is evaluated. The controller consists of two components: (1) a model-based feedback component, and (2) a learning feedforward component. The feedback component is designed on the basis of a simple second-order linear model, which is known to have structural errors. In the design, an emphasis is placed on robustness. The learning feedforward component is a neural-network-based controller, comprised of a one-hidden-layer structure with second-order B-spline basis functions. Simulations and experimental evaluations show that, with little effort, a high-performance motion system can be obtained with this approach

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities