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Academic literature on the topic 'Control of Induction Motors'
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Dissertations / Theses on the topic "Control of Induction Motors"
1
Zhang, Wei. "Advanced control of induction motors." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/15033/.
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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.
2
Zhang, Zaining. "Sensorless vector control for induction motors." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340849.
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Sevinc, Ata. "Speed sensorless control of induction motors." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364962.
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Khiyo, Sargon. "Neuro/fuzzy speed control of induction motors." Thesis, View thesis, 2002. http://handle.uws.edu.au:8081/1959.7/554.
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The thesis involved the design, implementation and testing of a second order neuro-fuzzy controller for the speed control of an AC induction motor, and a comparison of the neuro-fuzzy controller's performance with that of the PI algorithm. It was found experimentally, that the operating temperature of the AC induction motor affected the ability of the PI controller to maintain the set speed. The linear PI algorithm approximation was observed to produce transient speed responses when sudden changes in load occurred. The neuro-fuzzy design was found to be quite involved in the initial design stages. However, after the initial design, it was a simple matter of fine-tuning the algorithm, to optimize performance for any parameter variations of the motor due to temperature or due to sudden changes in load. The neuro-fuzzy algorithm can be developed utilising one of two methods. The first method utilises sensor-less control by detailed modeling of the induction motor; where all varying parameters of the motor are modeled mathematically. This involves using differential equations, and representing them in the form of system response block diagrams. When the overall plant transfer function is known, a fuzzy PI algorithm can be utilised to control the processes of the plant. The second method involves modeling the overall output response as a second order system. Raw data can then be generated in a text file format, providing control data according to the modeled second order system. Using the raw data, development software such as FuzzyTECH is utilised to perform supervised learning, so to produce the knowledge base for the overall system. This method was utilised in this thesis and compared to the conventional PI algorithm. The neuro-fuzzy algorithm implemented on a PLC was found to provide better performance than the PI algorithm implemented on the same PLC. It provided also in the added flexibility for further fine-tuning and avoided the need for rigorous mathematical manipulation of linear equations
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Khiyo, Sargon, University of Western Sydney, of Science Technology and Environment College, and School of Engineering and Industrial Design. "Neuro/fuzzy speed control of induction motors." THESIS_CSTE_EID_Khiyo_S.xml, 2002. http://handle.uws.edu.au:8081/1959.7/554.
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The thesis involved the design, implementation and testing of a second order neuro-fuzzy controller for the speed control of an AC induction motor, and a comparison of the neuro-fuzzy controller's performance with that of the PI algorithm. It was found experimentally, that the operating temperature of the AC induction motor affected the ability of the PI controller to maintain the set speed. The linear PI algorithm approximation was observed to produce transient speed responses when sudden changes in load occurred. The neuro-fuzzy design was found to be quite involved in the initial design stages. However, after the initial design, it was a simple matter of fine-tuning the algorithm, to optimize performance for any parameter variations of the motor due to temperature or due to sudden changes in load. The neuro-fuzzy algorithm can be developed utilising one of two methods. The first method utilises sensor-less control by detailed modeling of the induction motor; where all varying parameters of the motor are modeled mathematically. This involves using differential equations, and representing them in the form of system response block diagrams. When the overall plant transfer function is known, a fuzzy PI algorithm can be utilised to control the processes of the plant. The second method involves modeling the overall output response as a second order system. Raw data can then be generated in a text file format, providing control data according to the modeled second order system. Using the raw data, development software such as FuzzyTECH is utilised to perform supervised learning, so to produce the knowledge base for the overall system. This method was utilised in this thesis and compared to the conventional PI algorithm. The neuro-fuzzy algorithm implemented on a PLC was found to provide better performance than the PI algorithm implemented on the same PLC. It provided also in the added flexibility for further fine-tuning and avoided the need for rigorous mathematical manipulation of linear equations<br>Master of Engineering (Hons)
6
Wong, D. "Speed control of three-phase induction motors." Thesis, University of Reading, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376194.
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Lüdtke, Ingo. "The direct torque control of induction motors." Thesis, University of South Wales, 1998. https://pure.southwales.ac.uk/en/studentthesis/the-direct-torque-control-of-induction-motors(5b85e666-04b6-493b-b615-c5e2144d03c6).html.
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This thesis is mainly devoted to the investigation of speed control methods of three phase, cage rotor induction motors with particular emphasis being given to vector control and direct torque control techniques. Modern control strategies such as vector control and direct torque control are investigated as well as the conventional methods such as open loop (constant V/f) operation. A number of different pulse width modulation (p.w.m.) waveform generation strategies are simulated and discussed and their application to the above speed control systems fully investigated. A 3kW, three phase induction motor drive has been designed and experimental data obtained from it in order to verify the results achieved by simulation. It is shown that direct torque control achieves decoupling of the motor torque and the motor flux without the use of a co-ordinate transform. A variation of the direct torque control algorithm has also been developed and implemented. It is shown, that by using different switching tables for the selection of voltage vectors, the performance of direct torque control can be further improved. Further insight into the nature of direct torque control has been gained from the study of the effect of the application of inverter switch settings, or the application of corresponding voltage vectors, on the motor flux and torque. It has been found that the range of torque variation of the motor drive system depends strongly on both the motor load torque and the motor speed. The results of the work reported indicate that the range of torque variation for a drive system which strongly depends on motor load torque and motor speed is considerably reduced by the novel direct torque control system resulting from the research. The control algorithms have been implemented on 32 bit micro processors which facilitate the use of parallelism in both the hardware and the software design. The resulting system is capable of controlling a three phase induction motor with variable voltage and variable frequency with control strategies such as six step operation, symmetric and asymmetric regular and natural sampled p.w.m. waveforms, sigma delta modulation methods, space vector modulation techniques, flux vector control and direct torque control.
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Khiyo, Sargon. "Neuro/fuzzy speed control of induction motors /." View thesis, 2002. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030925.144725/index.html.
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Thesis (M. E. (Hons))--University of Western Sydney, 2002.<br>"A thesis submitted for Master of Engineering (Honours), School of Engineering & Industrial Design, University of Western Sydney, October 2002" Bibliography: leaves 147 - 149.
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Zhang, Pinjia. "Active thermal protection for induction motors fed by motor control devices." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34811.
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Induction motors are widely used in industrial processes. The malfunction of a motor may not only lead to high repair costs, but also cause immense financial losses due to unexpected process downtime. Since thermal overload is one of the major root causes of stator winding insulation failure, an accurate and reliable monitoring of the stator winding temperature is crucial to increase the mean time to catastrophic motor breakdown, and to reduce the extraordinary financial losses due to unexpected process downtime.
To provide a reliable thermal protection for induction motors fed by motor control devices, a dc signal-injection method is proposed for in-service induction motors fed by soft-starter and variable-frequency drives. The stator winding temperature can be monitored based on the estimated stator winding resistance using the dc model of induction motors. In addition, a cooling capability monitoring technique is proposed to monitor the cooling capability of induction motors and to warn the user for proactive inspection and maintenance in the case of cooling capability deterioration. The proposed cooling capability monitoring technique, combined with the proposed stator winding temperature monitoring technique, can provide a complete thermal protection for in-service induction motors fed by motor control devices.
Aside from online thermal protection during a motor's normal operation, the thermal protection of de-energized motors is also essential to prolong a motor's lifetime. Moisture condensation is one of the major causes to motor degradation especially in high-humidity environments. To prevent moisture condensation, a non-intrusive motor heating technique is proposed by injecting currents into the motor stator winding using soft-starters. A motor's temperature can be kept above the ambient temperature due to the heat dissipation, so that the moisture condensation can be avoided.
To sum up, active stator winding temperature estimation techniques for induction motors under both operating and de-energization conditions are proposed in this dissertation for both thermal protection and optimizing the operation of a motor system. The importance of these proposed techniques lies in their non-intrusive nature: only the existing hardware in a motor control device is required for implementation; a motor's normal operation is not interrupted.
10
Arias, Pujol Antoni. "Improvements in direct torque control of induction motors." Doctoral thesis, Universitat Politècnica de Catalunya, 2001. http://hdl.handle.net/10803/6317.
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This thesis is mainly devoted to the investigation of speed control methods for three phase cage induction motors with particular emphasis being given to Direct Torque Control (DTC) improved techniques.<br/>Classical Direct Torque Control has inherent disadvantages such as: problems during starting resulting from the null states, the compulsory requirement of torque and flux estimators, and torque ripple. In the classical DTC induction motor drive a voltage vector is applied for the entire period, and this causes the stator current and electromagnetic torque exceeds its reference value early during the cycle, causing a high torque ripple. Switching cycles then follows this, in which the zero switching vectors are applied in order to reduce the electromagnetic torque to reference value. This thesis suggests a technique based on applying to the inverter the selected active states just enough time to achieve the torque and flux references values. The rest of the switching period a null state is selected which won't almost change both the torque and the flux. Therefore, a duty ratio has to be determined each switching time. By means of varying the duty ratio between its extreme values (0 up to 1) it is possible to apply any voltage to the motor. The optimum duty ratio per sampling period is a non-linear function of the electromagnetic torque error, the stator flux position and the working point, which is determined by the motor speed and the electromagnetic torque. It is obvious that it is extremely difficult to model such an expression since it is a different non-linear function per working point. Therefore, this thesis is focused on performing a fuzzy-logic-based duty-ratio controller, where the optimum duty ratio is determined every switching period. Additionally, this Fuzzy Controller is adaptive and may be applied to any induction motor.<br/>A stator flux reference optimum controller is also designed, which not only helps to achieve a smaller torque ripple, but also reduces the reactive power consumption of the drive taken from the main supply. This is achieved by changing the stator flux reference value with reference being made to the correspondent torque reference value. Therefore, the stator flux reference value chosen is to be just of sufficient value to produce the desired torque <br/>Simulated results are shown in order to compare the classical DTC and the Fuzzy Logic based DTC.<br/>The control algorithms have been implemented on a PC/DSP based board that facilitates the use of parallelism in software design. A 1.5kW, three-phase induction motor drive has been designed and experimental data obtained from it in order to verify the results achieved by simulation.