Browsing by Subject "Induction motor"
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Item Controlling the Torque-Speed Characteristics of a Polyphase Induction Motor Using a Switched Rotor Ballast Network(IEEE, 2001-06) Caprio, M.T.; Buckner, G.D.; Weldon, W.W.This paper describes the development and experimental demonstration of a solid-state switching network that actively modifies the performance characteristics of a wound-rotor induction motor, enabling real-time impedance matching with its mechanical load. This switching network manipulates the rotor currents of the motor to modify the torque and efficiency characteristics as a function of rotor speed. Perturbation and simulation studies establish the active range of induction motor torque that rotor resistance can control. Analytical control strategies are developed that enable the motor to operate at peak torque at all speeds. For implementation, a switched ballast network is inserted into the rotor circuitry of a wound-rotor induction motor. The modified motor was tested and the experimental results confirm the viability of this approachItem Design and development of load emulator using a vector controlled induction motor(2004-12-18) Verma, Vishal; Fernández, Benito R.This thesis is an attempt to examine the area of mechanical load emulation used for Induction motor and drive testing. A test bed is built using commercial-off- the-shelf (COTS) mechanical and electrical equipment to investigate the adequacy as well as limitations of this approach. The Motor under test (MUT), Torque transducer and Load motor (LM) are aligned and the shafts coupled using bellow couplings. The LM drive is set for flux-vector control operation by using feedback from the rotary optical encoder mounted on the LM shaft. A Real-Time (RT) controller is programmed to control the LM drive to perform the dynamic load emulation and to give operational commands to the MUT drive. Steps taken to reduce the Electro Magnetic Interference (EMI) by installing motor filters, opto-couplers and isolation amplifiers were very effective in reducing the noise in control and sensor signals. Load emulation testing was carried out on linear first, second and third order models. The experimental results obtained prove the effectiveness of using this methodItem Fault detection and model-based diagnostics in nonlinear dynamic systems(2010-12) Nakhaeinejad, Mohsen; Bryant, Michael D.; Driga, Mircea D.; Fahrenthold, Eric P.; Fernandez, Benito; Longoria, Raul G.Modeling, fault assessment, and diagnostics of rolling element bearings and induction motors were studied. Dynamic model of rolling element bearings with faults were developed using vector bond graphs. The model incorporates gyroscopic and centrifugal effects, contact deflections and forces, contact slip and separations, and localized faults. Dents and pits on inner race, outer race and balls were modeled through surface profile changes. Experiments with healthy and faulty bearings validated the model. Bearing load zones under various radial loads and clearances were simulated. The model was used to study dynamics of faulty bearings. Effects of type, size and shape of faults on the vibration response and on dynamics of contacts in presence of localized faults were studied. A signal processing algorithm, called feature plot, based on variable window averaging and time feature extraction was proposed for diagnostics of rolling element bearings. Conducting experiments, faults such as dents, pits, and rough surfaces on inner race, balls, and outer race were detected and isolated using the feature plot technique. Time features such as shape factor, skewness, Kurtosis, peak value, crest factor, impulse factor and mean absolute deviation were used in feature plots. Performance of feature plots in bearing fault detection when finite numbers of samples are available was shown. Results suggest that the feature plot technique can detect and isolate localized faults and rough surface defects in rolling element bearings. The proposed diagnostic algorithm has the potential for other applications such as gearbox. A model-based diagnostic framework consisting of modeling, non-linear observability analysis, and parameter tuning was developed for three-phase induction motors. A bond graph model was developed and verified with experiments. Nonlinear observability based on Lie derivatives identified the most observable configuration of sensors and parameters. Continuous-discrete Extended Kalman Filter (EKF) technique was used for parameter tuning to detect stator and rotor faults, bearing friction, and mechanical loads from currents and speed signals. A dynamic process noise technique based on the validation index was implemented for EKF. Complex step Jacobian technique improved computational performance of EKF and observability analysis. Results suggest that motor faults, bearing rotational friction, and mechanical load of induction motors can be detected using model-based diagnostics as long as the configuration of sensors and parameters is observable.