Investigations on Direct Torque and Flux Control of Speed Sensorless Induction Motor Drive

By: Ramesh, TejavathuContributor(s): Panda, Anup Kumar [Supervisor] | Department of Electrical EngineeringMaterial type: TextTextLanguage: English Publisher: 2016Description: 229 pSubject(s): Engineering and Technology | Electrical Engineering | Power Networks | Non Conventional EnergyOnline resources: Click here to access online Dissertation note: Thesis Ph.D National Institute of Technology, Rourkela Summary: The Induction motors (IM) are used worldwide as the workhorse in most of the industrial applications due to their simplicity, high performance, robustness and capability of operating in hazardous as well as extreme environmental conditions. However, the speed control of IM is complex as compared to the DC motor due to the presence of coupling between torque and flux producing components. The speed of the IM can be controlled using scalar control and vector control techniques. The most commonly used technique for speed control of IM is scalar control method. In this method, only the magnitude and frequency of the stator voltage or current is regulated. This method is easy to implement, but suffers from the poor dynamic response. Therefore, the vector control or field oriented control (FOC) is used for IM drives to achieve improved dynamic performance. In this method, the IM is operated like a fully compensated and separately excited DC motor. However, it requires more coordinate transformations, current controllers and modulation schemes. In order to get quick dynamic performance, direct torque and flux controlled (DTFC) IM drive is used. The DTFC is achieved by direct and independent control of flux linkages and electromagnetic torque through the selection of optimal inverter switching which gives fast torque and flux response without the use of current controllers, more coordinate transformations and modulation schemes. Many industries have marked various forms of IM drives using DTFC since 1980. The linear fixed-gain proportional-integral (PI) based speed controller is used in DTFC of an IM drive (IMD) under various operating modes. However, The PI controller (PIC) requires proper and accurate gain values to get high performance. The PIC gain values are tuned for a specific operating point and which may not be able to perform satisfactorily when the load torque and operating point changes. Therefore, the PIC is replaced by Type-1 fuzzy logic controller (T1FLC) to improve the dynamic performance over a wide speed range and also load torque disturbance rejections. The T1FLC is simple, easy to implement and effectively deals with the nonlinear control system without requiring complex mathematical equations using simple logical rules, which are decided by the expert. In order to further improve the controller performance, the T1FLC is replaced by Type-2 fuzzy logic controller (T2FLC). The T2FLC effectively handles the large footprint of uncertainties compared to the T1FLC due to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, whereas the T1FLC consists only a Type-1 fuzzy sets and single membership function. The training data for T1FLC and T2FLC is selected based on the PIC scheme.
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Thesis (Ph.D/M.Tech R) Thesis (Ph.D/M.Tech R) BP Central Library
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Thesis Ph.D National Institute of Technology, Rourkela

The Induction motors (IM) are used worldwide as the workhorse in most of the industrial applications due to their simplicity, high performance, robustness and capability of operating in hazardous as well as extreme environmental conditions. However, the speed control of IM is complex as compared to the DC motor due to the presence of coupling between torque and flux producing components. The speed of the IM can be controlled using scalar control and vector control techniques. The most commonly used technique for speed control of IM is scalar control method. In this method, only the magnitude and frequency of the stator voltage or current is regulated. This method is easy to implement, but suffers from the poor dynamic response. Therefore, the vector control or field oriented control (FOC) is used for IM drives to achieve improved dynamic performance. In this method, the IM is operated like a fully compensated and separately excited DC motor. However, it requires more coordinate transformations, current controllers and modulation schemes. In order to get quick dynamic performance, direct torque and flux controlled (DTFC) IM drive is used. The DTFC is achieved by direct and independent control of flux linkages and electromagnetic torque through the selection of optimal inverter switching which gives fast torque and flux response without the use of current controllers, more coordinate transformations and modulation schemes. Many industries have marked various forms of IM drives using DTFC since 1980.
The linear fixed-gain proportional-integral (PI) based speed controller is used in DTFC of an IM drive (IMD) under various operating modes. However, The PI controller (PIC) requires proper and accurate gain values to get high performance. The PIC gain values are tuned for a specific operating point and which may not be able to perform satisfactorily when the load torque and operating point changes. Therefore, the PIC is replaced by Type-1 fuzzy logic controller (T1FLC) to improve the dynamic performance over a wide speed range and also load torque disturbance rejections. The T1FLC is simple, easy to implement and effectively deals with the nonlinear control system without requiring complex mathematical equations using simple logical rules, which are decided by the expert. In order to further improve the controller performance, the T1FLC is replaced by Type-2 fuzzy logic controller (T2FLC). The T2FLC effectively handles the large footprint of uncertainties compared to the T1FLC due to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, whereas the T1FLC consists only a Type-1 fuzzy sets and single membership function. The training data for T1FLC and T2FLC is selected based on the PIC scheme.

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