Relevant bibliographies by topics / Control of Induction Motors

Academic literature on the topic 'Control of Induction Motors'

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Published: 6 September 2023

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Journal articles on the topic "Control of Induction Motors"

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Rachid, A. "On induction motors control." IEEE Transactions on Control Systems Technology 5, no. 3 (May 1997): 380–82. http://dx.doi.org/10.1109/87.572135.

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Sun, Xiaodong, Long Chen, and Zebin Yang. "Overview of Bearingless Induction Motors." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/570161.

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Bearingless induction motors combining functions of both torque generation and noncontact magnetic suspension together have attracted more and more attention in the past decades due to their definite advantages of compactness, simple structure, less maintenance, no wear particles, high rotational speed, and so forth. This paper overviews the key technologies of the bearingless induction motors, with emphasis on motor topologies, mathematical models, and control strategies. Particularly, in the control issues, the vector control, independent control, direct torque control, nonlinear decoupling control, sensorless control, and so forth are investigated. In addition, several possible development trends of the bearingless induction motors are also discussed.
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Lorenz, R. D., T. A. Lipo, and D. W. Novotny. "Motion control with induction motors." Proceedings of the IEEE 82, no. 8 (1994): 1215–40. http://dx.doi.org/10.1109/5.301685.

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Singh, Yaduvir, Darshan Singh, and Dalveer Kaur. "Performance Comparison of PI and Fuzzy-PI Logic Speed Control of Induction Motor." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 3 (March 5, 2013): 400–413. http://dx.doi.org/10.24297/ijct.v6i3.4464.

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Single-phase induction motors are also used extensively for smaller loads. Speed control of induction motor has beenimplemented using PI (Proportional-Integral) controller and Fuzzy PI controller in Simulink MATLAB. The results showthat induction motor Fuzzy-PI speed control method results in a quicker response with no overshoot than the conventional PI controller. The settling time of induction motor Fuzzy-PI speed is better than the conventional PI controller. The integral time of weighted absolute error (ITEA) performance criteria also shows that the induction motor Fuzzy-PI speed control has better performance. Moreover, the induction motor Fuzzy-PI speed control has a strong ability to adapt to the significant change of system parameters.
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Al Rakib, Md Abdullah, Md Moklesur Rahman, Md Miraj Hossain, Md Ashiqur Rahman, Mousume Samad, and Fysol Ibna Abbas. "Induction Motor Based Speed and Direction Controller." European Journal of Engineering and Technology Research 7, no. 6 (November 28, 2022): 82–86. http://dx.doi.org/10.24018/ejeng.2022.7.6.2868.

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Induction motors are widely employed in a variety of sectors, from household gadgets to industrial machinery. This mandates the deployment of an effective and safe speed control device. Induction motors may also run in either direction, which is beneficial in a variety of applications. The Induction Motor Speed and Direction Controller Project are designed to regulate the induction motor's speed and direction. Induction motors run on straight AC lines, and the amount of power they receive determines how fast they revolve. Through AC driver circuitry, we may regulate the power of the AC line to change the speed of the induction motor. A microcontroller from the Atmega family is utilized to provide PWM power to an opto-coupler, which drives the TRIAC that supplies power to the induction motor. The microcontroller receives instructions via a mobile phone connection to the system. The mobile phone sends DTMF signals to the system, which the system recognizes and responds to appropriately. A button is used to raise the speed of the induction motor, a button to change direction, and a button to lower the speed of the induction motor, according to the video. On the LCD, the entire procedure may be observed in real time. In this way, this project demonstrates how to control the speed and direction of an induction motor.
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Awaar, Vinay Kumar, Sandhya Rani M.N, Pravardh Naragani, Sasidhar Talluri, Samanvita Polisetty, Satya Sreyas Vakkalanka, and Hassan Mohmmed Al-Jawahry. "Speed Control of Induction Motor using Digital Signal Processor TMS320F28027F." E3S Web of Conferences 391 (2023): 01178. http://dx.doi.org/10.1051/e3sconf/202339101178.

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Single-phase induction motors are widely used today. A single-phase induction motor is operated using a single-phase inverter. The Single-phase inverter consists of four MOSFETs, two for the high side and two for the low side. The Single-phase induction motors are a constant speed motor. As a corollary, the speed control of the Single-Phase Induction motor becomes a very important necessity. Speed control of the Single-Phase Induction motor can be done in many ways. The method followed in this paper is voltage control/ (V/F Control). By controlling the duty cycle of the MOSFETs, we can control the output of the inverter and there by control the speed of the Single-Phase Induction motor. The inverter has been interfaced with MATLAB, which generates pulses and sends them to the Digital Signal processor. Texas Instrument’s low-cost Piccolo F2802x MCUs are ideal for a variety of applications, such as washing machines, compressors, pumps, fans, electric bicycles, tools, treadmills, compact drives, sewing and textile machines, lifts and hobby motors. This paper focuses on closed loop control of single-phase induction motor using this Micro Controller, F2802x Digital Signal Processor using feedback obtained through a Hall Sensor.
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Burade, Piyush, Ravi Aurase, Anjali Hirapure, and Rohini Chawardol. "AC Motor Monitoring and Controlling Using IoT." June-July 2023, no. 34 (May 27, 2023): 7–12. http://dx.doi.org/10.55529/jecnam.34.7.12.

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The design of IOT technology is shown to monitor and diagnose the performance of a three-phase induction motor and record crucial operational characteristics. Today's technology is rapidly tied to the Internet of Things (IOT), keeping things connected efficiently. For the purpose of gathering and processing induction motor parameters, the solutions given include an IOT-based platform. The parameters are made up of sensors, including humidity, temperature, voltage, and current sensors. With the help of the pocket IOT application, this data may be shown on a smartphone, accessed via web sites, and stored in a cloud platform. It will be promptly informed if performance limits are exceeded. To prevent motor downtime, an induction motor can be checked and immediate action taken can save money and time. Utilising IOT to monitor induction motors has several benefits, including notification of problem alerts and historical data for preventative maintenance. Recent technological advancements have greatly improved the quality, speed, and ease of our lives. This article explains how to manage and control induction motors (IOT) using the Internet of Things. The IOT is more effective and convenient for controlling systems because it can be used from anywhere via Wi-Fi. This intelligent system's main objective is to prevent induction motor failure by taking preventive measures. Because of its many benefits, including their self-starting nature, low cost, high power factor, and robust construction, induction motors are utilised in a wide range of applications, including those for electric vehicles, businesses, and agricultural areas. In order to maximise motor efficiency and assure safe and reliable operation, it is crucial to identify defects in motors as soon as possible using the best smart protection approach currently available. Remote monitoring is possible for the induction motor's speed, voltage, current, temperature, humidity, and other electrical, mechanical, and environmental parameters because errors in these areas seriously damage the motors and have an impact on the induction motor's ability to function in other applications. In this system for monitoring and controlling Induction using IOT, a number of sensors are employed to acquire the motor data in real-time and a relay is used to control the motor. The proposed system will collect and analyse induction motor parameters in real-time using an IOT-based platform.
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Shrivastava, R. K., Rakesh Misar, Arvind Vaidya, Pawan Kanoje, and Sakesh Hiwrale. "IoT-Based Induction Motor Monitoring System for Industries." Journal of Switching Hub 8, no. 1 (April 29, 2023): 28–37. http://dx.doi.org/10.46610/josh.2023.v08i01.005.

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The most common kind of motor used in industrial applications is still the AC motor. In many applications, it is crucial to monitor and regulate the induction motor's settings, and there are numerous ways to guarantee dependable performance. This research focuses on the remote monitoring and management of a three-phase induction motor's numerous parameters using the Internet of Things (IoT). Short circuit motor temperature, current, and voltage are just a few of the characteristics that the sensor and sensor module keep track of and send to the processing unit, which displays the parameter on the server. To prevent system failures through the server gateway, the system also includes automatic and manual control methods to stop or start the short-circuited motor. With constant monitoring to detect failures and also to identify preventative maintenance, this system's adoption improves the machine's operational efficiency. The most prevalent type of motor in use today across all industries is the AC motor and the brilliant scientist Nikola Tesla's development of an induction motor. The induction motor is responsible for over 50% of the world's electricity consumption. 90% of industries utilize induction motors because they have the necessary properties like being naturally "self-starting" motors, and not requiring permanent magnets, brushes, commutator rings, or position sensors. Moreover, induction motors are more affordable and reliable than other types of motors, retain a strong power factor, require less maintenance, are extremely efficient, and are tiny in size.
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Benbouzid, Mohamed, Abdelkrim Benchaib, Gang Yao, Brice Beltran, and Olivier Chocron. "A Metric Observer for Induction Motors Control." Journal of Control Science and Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/3631254.

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This paper deals with metric observer application for induction motors. Firstly, assuming that stator currents and speed are measured, a metric observer is designed to estimate the rotor fluxes. Secondly, assuming that only stator currents are measured, another metric observer is derived to estimate rotor fluxes and speed. The proposed observer validity is checked throughout simulations on a 4 kW induction motor drive.
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Faizal, Ahmad Ahmad, Agustiawan Agus, Nanda Putri Miefthawati, Mulyono Mulyono, Rudy Kurniawan, Elfira Safitri, Corry Corazon Marzuki, and Rahmadeni Rahmadeni. "Direct Torque Control (DTC) Design With Fuzzy Sugeno-Proportional Derivative for 3-Phase Induction Motor Speed Control." Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) 10, no. 1 (April 21, 2023): 111–20. http://dx.doi.org/10.33019/jurnalecotipe.v10i1.3925.

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In general, in the industrial world, induction motors are more widely used than direct current motors, due to the characteristics of induction motors that are sturdy, reliable, easy to maintain, and relatively inexpensive. The phenomenon of changing rotational speed when the load changes results in regulation of the speed of the induction motor, which risks slow response time, there is overshoot which should still be minimized, and there are disturbances caused by external environmental factors, so that a controller is needed that is able to work effectively to optimize performance. 3 phase induction motor. The purpose of this study is to design a sugeno-PD fuzzy DTC controller, where the DTC provides a fast and strong response mounted on an AC motor. Fuzzy Sugeno provides a short calculation time and its reasoning includes wide enough data and PD to speed up the response time results. So that the proposed method produces an induction motor rotating speed according to the given setpoint of 100 rad/s with a settling time of 0.45 seconds, a rise time of 0.2 seconds and no steady state error. From the state of the plan output response before being given the controller there is a steady state error of 5 rad/s, a maximum overshoot of 5.4111%, a settling time of 0.1554 seconds and a rise time of 0.1554 seconds
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Dissertations / Theses on the topic "Control of Induction Motors"

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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.
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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
Master of Engineering (Hons)
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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.
"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.
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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.
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.
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
Simulated results are shown in order to compare the classical DTC and the Fuzzy Logic based DTC.
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.
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Books on the topic "Control of Induction Motors"

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Control of induction motors. San Diego, Calif: Academic, 2001.

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Hansen, Irving G. Induction motor control. [Washington, DC]: National Aeronautics and Space Administration, 1990.

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Marino, Riccardo. Induction motor control design. London: Springer, 2010.

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S, Zinger Donald, Roth Mary Ellen, and United States. National Aeronautics and Space Administration., eds. Field oriented control of induction motors. [Washington, D.C.]: NASA, 1990.

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Amin, Bahram. Induction motors: Analysis and torque control. Berlin: Springer, 2001.

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Senty, Steve. Motor control fundamentals. Australia: Delmar, 2013.

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Keli, Shi, ed. Applied intelligent control of induction motor drives. Hoboken, N.J: Wiley, 2011.

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Trzynadlowski, Andrzej. The field orientation principle in control of induction motors. Boston: Kluwer Academic, 1994.

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Marino, Riccardo, Patrizio Tomei, and Cristiano M. Verrelli. Induction Motor Control Design. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-284-1.

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Alacoque, Jean Claude. Direct Eigen Control for Induction Machines and Synchronous Motors. Chichester, UK: A John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118460641.

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Book chapters on the topic "Control of Induction Motors"

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Lyshevski, Sergey Edward. "Induction Motors." In Mechatronics and Control of Electromechanical Systems, 127–96. Boca Raton : CRC Press, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155425-5.

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Amin, Bahram. "Induction Motors Torque Control." In Power Systems, 157–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04373-8_7.

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Zanasi, Roberto, and Giovanni Azzone. "Multiphase Induction Motor Control." In AC Electric Motors Control, 233–52. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118574263.ch12.

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Pilloni, Alessandro, Alessandro Pisano, Martin Riera-Guasp, Ruben Puche-Panadero, and Manuel Pineda-Sanchez. "Fault Detection in Induction Motors." In AC Electric Motors Control, 275–309. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118574263.ch14.

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Fadili, Abderrahim El, Fouad Giri, and Abdelmounime El Magri. "Control Models for Induction Motors." In AC Electric Motors Control, 15–40. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118574263.ch2.

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Ortega, Romeo, Antonio Loría, Per Johan Nicklasson, and Hebertt Sira-Ramírez. "Voltage-fed induction motors." In Passivity-based Control of Euler-Lagrange Systems, 311–80. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3603-3_10.

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Ortega, Romeo, Antonio Loría, Per Johan Nicklasson, and Hebertt Sira-Ramírez. "Current-fed induction motors." In Passivity-based Control of Euler-Lagrange Systems, 381–439. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-3603-3_11.

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Khorrami, Farshad, Prashanth Krishnamurthy, and Hemant Melkote. "Adaptive Control of Induction Motors." In Modeling and Adaptive Nonlinear Control of Electric Motors, 315–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08788-6_13.

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Lorenz, R. D., T. A. Lipo, and D. W. Novotny. "Motion Control with Induction Motors." In Power Electronics and Variable Frequency Drives, 209–76. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9780470547113.ch5.

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Trzynadlowski, Andrzej M. "Scalar Control of Induction Motors." In The Field Orientation Principle in Control of Induction Motors, 43–86. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2730-5_2.

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Conference papers on the topic "Control of Induction Motors"

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Atkinson, D. "Vector control of cascaded induction motors." In IEE Seminar on Advances in Induction Motor Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000384.

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Hughes, A. "Visualising vector control in cage motors." In IEE Seminar on Advances in Induction Motor Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000381.

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Ludtke, I. "Direct torque control of induction motors." In IEE Colloquium on Vector Control and Direct Torque Control of Induction Motors. IEE, 1995. http://dx.doi.org/10.1049/ic:19951113.

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Curiac, Radu S., and Sumit Singhal. "Magnetic Noise in Induction Motors." In ASME 2008 Noise Control and Acoustics Division Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ncad2008-73077.

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Abstract:
Noise in large high voltage induction motors (500Hp–18000Hp) may be airborne or magnetic in nature. Usually, large high voltage induction motors are custom built and tailored to meet customer’s demand. Since every motor is unique in its design, it is imperative to predict accurately the magnetic noise generation during design phase, this way avoiding expensive rework cost and not loosing the customer confidence. Stator – rotor mechanical design, along with careful electrical coil design, can significantly cut down magnetic noise in an induction motor. This paper discusses the various causes and control of magnetic noise in large induction motors. Theoretical noise predictions in large induction motors, along with measured experimental noise data, are presented.
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Schofield, J. R. G. "A 3.3 kV variable frequency converter for retrofitting to existing motors." In IEE Seminar on Advances in Induction Motor Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000382.

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Thomas, J. L. "Advanced torque control of induction motors fed by a floating capacitor multilevel VSI actuator." In IEE Seminar on Advances in Induction Motor Control. IEE, 2000. http://dx.doi.org/10.1049/ic:20000385.

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Schofield, J. R. G. "Direct torque control - DTC." In IEE Colloquium on Vector Control and Direct Torque Control of Induction Motors. IEE, 1995. http://dx.doi.org/10.1049/ic:19951108.

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Pankhurst, A. "Control of induction motors." In IET 13th Professional Development Course on Electric Traction Systems. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.1437.

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Pankhurst, A. "Control of induction motors." In IET Professional Development Course on Electric Traction Systems. IET, 2010. http://dx.doi.org/10.1049/ic.2010.0190.

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Pankhurst, A. "Control of induction motors." In IET Professional Development Course on Electric Traction Systems. Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/ic.2012.0076.

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Reports on the topic "Control of Induction Motors"

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McJunkin, Timothy R., Vivek Agarwal, and Nancy Jean Lybeck. Online Monitoring of Induction Motors. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1239881.

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Otaduy, P. J. Real Time Flux Control in PM Motors. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/885965.

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Sangster, T. C., and L. Ahle. Beam Control for Ion Induction Accelerators. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/823903.

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Weeks, G. E. Cylindrical Induction Melter Modicon Control System. Office of Scientific and Technical Information (OSTI), April 1998. http://dx.doi.org/10.2172/656898.

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Upadhyay, Piyush. Manufacturing Hybrid Copper-Aluminum Rotors for High Power Induction and Permanent Magnet Electric Motors - CRADA 475. Office of Scientific and Technical Information (OSTI), December 2020. http://dx.doi.org/10.2172/1867254.

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McKeever, John W., Niranjan Patil, and Jack Lawler. Control of Surface Mounted Permanent Magnet Motors with Special Application to Fractional-Slot Motors with Concentrated Windings. Office of Scientific and Technical Information (OSTI), July 2007. http://dx.doi.org/10.2172/931748.

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Kelley, J. B., and R. D. Skocypec. Control technology for surface treatment of materials using induction hardening. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/494129.

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Saethre, R., H. Kirbie, B. Hickman, B. Lee, and C. Ollis. Optical control, diagnostic and power supply system for a solid state induction modulator. Office of Scientific and Technical Information (OSTI), June 1997. http://dx.doi.org/10.2172/562329.

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Lawler, J. S. Control of Surface Mounted Permanent Magnet Motors with Special Application to Fractional-Slot Concentrated Windings. Office of Scientific and Technical Information (OSTI), December 2005. http://dx.doi.org/10.2172/886007.

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Wheeler, Grant, and Michael Deru. Evaluation of High Rotor Pole Switched Reluctance Motors to Control Condenser Fans in a Commercial Refrigeration System. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1525771.

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