Condition based maintenance of electrical machines offers significant advantages for industry. A large part of the research effort in this area is directed towards the evaluation of fault conditions. This thesis is concerned with analysing and modelling faults in induction motors. A method for evaluating the performance of induction machines with static and dynamic eccentricity is developed, using harmonic analysis of the air gap permeance. Models able to simulate eccentricity are presented. The slip ring model equations are obtained and then used to obtain the commutator models transformed to a single reference frame. A variety of effects accompanying these fault conditions are analysed, for example variation of the eccentricity level due to unbalanced magnetic pull and the possibilities of additional vibration harmonics examined. Damping of eccentricity fields due to current redistribution, saturation and slotting are discussed. Some general steady state calculations are also presented which show that the performance of the machine need not be changed over the operating range, due to such a fault. The characteristics of combined static and dynamic eccentricity are examined and it is shown that the combined asymmetry generates additional harmonic components which are not related to those which occur when the two asymmetries take place in isolation. The development of a simulation model of machines with broken rotor bars, based on the variation in rotor parameters is presented. Experimental investigations focus mainly on observable differences in the torque transient characteristics, due to such a condition. The possibilities for using current monitoring to identify inter-turn short circuits are investigated.
(has links) (PDF)
Diss. Luleå : Luleå tekniska universitet, 2006.
Thèse (M.Sc.)--Université Laval, 2004. / Bibliogr.: f. -165. Publié aussi en version électronique.
Sensorless speed estimation in induction machines (IMs) presents an attractive proposition for eliminating the need for physical speed measurement sensors and thus avoiding the associated reliability and cost issues, such as the requirement of extra wiring, careful mounting, maintenance and adjustment. In this thesis, the feasibility of utilising the stator current and power signals to establish spectral search based (SSB) sensorless speed estimation schemes in wound rotor induction machines (WRIMs) operating in extended slip and open-loop controlled conditions is investigated. The research is performed on three different industrial WRIM designs. The thesis first investigates the spectral content of WRIM electrical and mechanical signals with the principal aim of identifying spectral patterns that can facilitate the development of real-time sensorless speed estimation. The examination is based on detailed harmonic models of the considered machine designs as well as experimental results obtained from tests performed on laboratory test rigs. A generalised theoretical analysis of the possible spectral content of machine signals that enables the derivation of closed form analytical expressions linking individual spectral frequencies to rotor speed is also undertaken. The results demonstrate that it is possible to clearly identify speed dependent components in the stator current and power signals and map the boundaries of the narrowbands maximised by these for extended slip and open-loop operating conditions. To enable improvement in attainable real time SSB estimation rates a dichotomous search algorithm real-time spectral processing method was employed for frequency tracking in this research. The algorithm performance is evaluated in real-time tests performed on a measured steady-state laboratory machine stator current and power signals. The results demonstrate that the dichotomous routine provides an inherent advantage in the frequency estimation rate without compromising the estimation accuracy and can therefore enable significant estimation rate improvement in SSB speed estimation algorithms. Novel sensorless speed SSB estimation techniques are then proposed for WRIM operation in extended slip and voltage/frequency controlled conditions. The algorithms utilise the reported analysis of electrical signals and are separately defined for each assessed operation mode and the stator current, phase power and three-phase power signals. It is shown that, in principle, power signal based estimation algorithms can offer an inherent capability of estimation rate reduction. A novel adaptive sliding window algorithm is defined for open-loop operating conditions that enable estimation in a wide operating speed range while minimising the potential for undesirable overlap with PWM harmonics. The proposed algorithms have been verified and their performance limitations assessed in real-time experiments on three different industrial WRIM designs. It is shown that reliable real-time speed estimation in steady-state and transient operating conditions is possible at an improved estimation rate while maintaining a low estimation error.
Utilization of Genetic Algorithms and Constrained Multivariable Function Minimization to Estimate Load Model Parameters from Disturbance DataMertz, Christopher George 02 July 2013 (has links)
As the requirements to operate the electric power system become more stringent and operating costs must be kept to a minimum, operators and planners must ensure that power system models are accurate and capable of replicating system disturbances. Traditionally, load models were represented as static ZIP models; however, NERC has recently required that planners model the transient dynamics of motor loads to study their effect on the postdisturbance behavior of the power system. Primarily, these studies are to analyze the effects of fault-induced, delayed voltage recovery, which could lead to cascading voltage stability issues. Genetic algorithms and constrained multivariable function minimization are global and local optimization tools used to extract static and dynamic load model parameters from postdisturbance data. The genetic algorithm's fitness function minimizes the difference between measured and calculated real and reactive power by varying the model parameters. The fitness function of the genetic algorithm, a function of voltage and frequency, evaluates an individual\'s difference between measured and simulated real and reactive power. While real measured data was unavailable, simulations in PSS/E were used to create data, and then compared against estimated data to examine the algorithms' ability to estimate parameters. / Master of Science
Modeling, analysis and design of integrated starter generator system based on field oriented controlled induction machinesLiu, Jingbo 02 December 2005 (has links)
No description available.
Burnham, David James
03 September 2009
Many utility-scale wind turbine generators use wound-rotor induction machines. By adding an external rotor resistance to the rotor circuit it is possible to control the wind turbine output power and, with proper control, maintain a constant power for wind speeds between rated and cut-out. The external resistance modifies the generator torque-speed curve and changes the angular velocity of the rotor, resulting in a greater power extraction from the wind. A number of control strategies can achieve this objective. These include controlling the rotor resistance to maintain a constant generator equivalent circuit, and control based on the aerodynamic torque. It is also possible to use a lookup table instead of a feedback controller. These options all have the same steady-state result as direct output power control, but differing transient performance. Computer simulations and hardware experiments are used to investigate and characterize the different control methods. / text
Bade, Rajesh Kumar
12 April 2006
Recent research has demonstrated the use of electrical signature analysis (ESA), that is, the use of induction motor currents and voltages, for early detection of motor faults in the form of embedded algorithms. In the event of multiple motors energized by a common voltage bus, the cost of installing and maintaining fault monitoring and detection devices on each motor may be avoided, by using bus level aggregate electrical measurements to assess the health of the entire population of motors. In this research an approach for detecting commonly encountered induction motor mechanical faults from bus level aggregate electrical measurements is investigated. A mechanical fault indicator is computed processing the raw electrical measurements through a series of signal processing algorithms. Inference of an incipient fault is made by the percentage relative change of the fault indicator from the ÂhealthyÂ baseline, thus defining a Fault Indicator Change (FIC). To investigate the posed research problem, healthy and faulty motors with broken rotor bar faults are simulated using a detailed transient motor model. The FIC based on aggregate electrical measurements is studied through simulations of different motor banks containing the same faulty motor. The degradation in the FIC when using aggregate measurements, as compared to using individual motor measurements, is investigated. For a given motor bank configuration, the variation in FIC with increasing number of faulty motors is also studied. In addition to simulation studies experimental results from a two-motor setup are analyzed. The FIC and degradation in the FIC in the case of load eccentricity fault, and a combination of shaft looseness and bearing damage is studied through staged fault experiments in the laboratory setup. In this research, the viability of using bus level aggregate electrical measurements for detecting incipient faults in motors energized by a common voltage bus is demonstrated. The proposed approach is limited in that as the power rating fraction of faulty motors to healthy motors in a given configuration decreases, it becomes far more difficult to detect the presence of incipient faults at very early stages.
Investigation of single and multiple faults under varying load conditions using multiple sensor types to improve condition monitoring of induction machines.Ahmed, Intesar January 2008 (has links)
Condition monitoring involves taking measurements on an induction motor while it is operating in order to detect faults. For this purpose normally a single sensor type, for example current is used to detect broken rotor bar using fault frequency components only under the full-load condition or a limited number of load cases. The correlations among the different types of sensors and their ability to diagnose single and multiple faults over a wide range of loads have not been the focused in previous research. Furthermore, to detect different faults in machines using any fault frequency components, it is important to investigate the variability in its amplitude to other effects apart from fault severity and load. This area has also often been neglected in the literature on condition monitoring. The stator current and axial flux have been widely used as suitable sensors for detecting different faults i.e. broken rotor bar and eccentricity faults in motors. Apart from detecting the broken rotor bar faults in generalized form, the use of instantaneous power signal has often been neglected in the literature condition monitoring. This thesis aims to improve machine condition monitoring and includes accurate and reliable detection of single and multiple faults (faults in the presence of other faults) in induction machines over a wide range of loads of rated output by using current, flux and instantaneous power as the best diagnostic medium. The research presents the following specific tasks: A comprehensive real database from non–invasive sensor measurements, i.e. vibration measurements, axial flux, 3-phase voltage, 3-phase current and speed measurements of induction motor is obtained by using laboratory testing on a large set of identical motors with different single and multiple faults. Means for introducing these faults of varying severity have been developed for this study. The collected data from the studied machines has been analysed using a custom-written analysis programme to detect the severity of different faults in the machines. This helps to improve the accuracy and reliability in detecting of single and multiple faults in motors using fault frequency components from current, axial flux and instantaneous power spectra. This research emphasises the importance of instantaneous power as a medium of detecting different single and multiple faults in induction motor under varying load conditions. This enables the possibility of obtaining accurate and reliable diagnostic medium to detect different faults existing in machines, which is vital in providing a new direction for future studies into condition monitoring. Another feature of this report is to check the variability in healthy motors due to: test repeatability, difference between nominally identical motors, and differences between the phases of the same motor. This has been achieved by conducting extensive series of laboratory tests to examine fault frequency amplitudes versus fault severity, load, and other factors such as test repeatability and machine phases. The information about the variations in the amplitudes of the fault frequency components is used to check the accuracy and reliability of the experimental set-up, which is necessary for the practical application of the results to reliably detect the different faults in the machines reliably. Finally, this study also considers the detection of eccentricity faults using fault frequency amplitudes as a function of average eccentricity, instead of as a function of load under different levels of loading. This has not been reported in previous studies. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1298314 / Thesis (Ph.D.)-- University of Adelaide, School of Electrical and Electronic Engineering, 2008
02 August 2005
No description available.
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