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

Identification of induction machine parameters using only no-load test measurements

Several methods have been used to estimate the parameters of induction machines. The basic method is the standard no-load and block rotor test. Although accurate results are obtained using this method; however, performing the locked rotor test is difficult, requiring full control of the voltage by using appropriate instrument to mechanically secure the rotor in the locked condition. Therefore, in this paper, a method requiring only a no-load test to extract the parameters of the induction machine is presented. The proposed method is based on the modification of the third impedance calculation of the IEEE standard 112. To validate the proposed method, parameters of a standard 7.5kW induction machine are estimated. Based on the experimental results, the maximum recorded error in the parameter estimation is less than -2.881% when compared to the reference parameters obtained from the conventional no-load and blocked rotor test.Keywords: induction motor, no-load tests, machine parameters, third impedance calculation, blocked-rotor tes

Parameter identification of induction motor using a genetic algorithm

High performance variable-speed machines incorporate a model for the system in either the controller or state estimation stages. The accuracy and general robustness of the machine is dependant on this model. Therefore, it must accurately represent both the electrical and electromagnetic interactions within the machine and associated mechanical systems. Recently, some new technologies have been tested in the field of electromechanics like neural networks, fuzzy logic, simulated annealing and genetic algorithms. These methods are increasingly being utilized in solving electric machine problems.;In this thesis, a genetic algorithm (GA)---a form of artificial intelligence---is employed to identify the electric parameters of induction motors. The variables used to calculate the electric parameters are the measured stator currents, stator voltages and rotor speed. The variables are acquired by using Data Acquisition System and Lab VIEW Software. Free acceleration test is performed on 7.5 hp induction motor, using a constant frequency power supply. The performance of the identification scheme is demonstrated with simulated and measured data, and electric parameters obtained using this method are compared with parameters obtained from IEEE standard tests. Based on the results, the method proved to be worth considering in optimizing induction machines and can be applied to a variety of induction motor parameter estimation problems

An invertible dependence of the speed and time of the induction machine during no-load direct start-up

Novel expression for time–speed curve of IM during no-load direct start-up An invertible dependence for speed–time curve of IM during no-load direct start-up Simulation results Experimental results Conclusion Disclosure statement References Full Article Figures & data References Citations Metrics Licensing Reprints & Permissions PDF AbstractFormulae display:MathJax Logo? In this paper, an invertible dependence of the speed and time of the induction machine during no-load direct start-up is presented. Namely, based on the parameters of the induction machine equivalent circuit as well as on the basic, well-known, equation for machine torque, the analytical expression for the induction machine time-speed dependence during direct start-up is derived. On the other hand, in order to obtain inverse i.e. speed-time dependence, the derived time-speed expression is rearranged in one nonlinear equation. As the derived nonlinear equation does not have an analytical solution, a novel iterative procedure, based on the usage of Lambert W function, is proposed for its solving. The results obtained by using the developed expressions for speed-time or time-speed curves are compared with the corresponding results obtained by using expressions known in the literature as well as with the results obtained by using a numerical time-domain computation method. Moreover, the results obtained by using the developed expressions have been compared with the corresponding experimental results to demonstrate the accuracy of the derived expressions. The Matlab code developed for solving the presented iterative procedure, as well as the Matlab code for induction machine speed-time curve determination, is also provided

Power quality disturbances assessment during unintentional islanding scenarios. A contribution to voltage sag studies

This paper presents a novel voltage sag topology that occurs during an unintentional islanding operation (IO) within a distribution network (DN) due to large induction motors (IMs). When a fault occurs, following the circuit breaker (CB) fault clearing, transiently, the IMs act as generators due to their remanent kinetic energy until the CB reclosing takes place. This paper primarily contributes to voltage sag characterization. Therefore, this novel topology is presented, analytically modelled and further validated. It is worth mentioning that this voltage sag has been identified in a real DN in which events have been recorded for two years. The model validation of the proposed voltage sag is done via digital simulations with a model of the real DN implemented in Matlab considering a wide range of scenarios. Both simulations and field measurements confirm the voltage sag analytical expression presented in this paper as well as exhibiting the high accuracy achieved in the three-phase model adopted.Postprint (published version

Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment

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

Parameter identification of induction motor

Numerous recent techniques of induction motor parameters calculating are hard to be done and expensive. Accurate calculations of the parameters of these motors would allow savings in different prospective like energy and cost. The major problem in calculating induction motor parameters is that it\u27s hard to measure output power precisely and without harm during the operation of the machines. It will be better to find other way to find out the output power with certain amount of inputs like input voltage and current.;Particle swarm optimization (PSO) and genetic algorithms (GAs) are often used to estimate quantities from limited information. They belong to a class of weak search procedures, that is, they do not provide the best solution, but one close to it. It is a randomized process in which follows the principles of evolution.;In this thesis genetic algorithm and partial swarm optimization are used to identify induction motor parameters. The inputs used to estimate electrical and mechanical parameters are measured stator voltages and currents. The estimated parameters compare well with the actual parameters. Data Acquisition (DAQ) is used to obtain these variable with the help of LABVIEW software. The induction motor used is a 7.5-hp with a constant frequency and in free acceleration. IEEE standard test of 7.5-hp induction motor is used to compare with performance of the simulated and measured data obtained. According to the output results, method of optimizing induction machine can be used in different models of induction motor