Top PDF Fuzzy Logic Based Direct Power Control of Induction Motor Drive

Fuzzy Logic Based Direct Power Control of Induction Motor Drive

Fuzzy Logic Based Direct Power Control of Induction Motor Drive

Control of the Induction motors can be done using various techniques. Most common techniques are: (a) constant voltage/frequency control (V/F), (b) field orientation control (FOC), and (c) direct torque control (DTC). The first one is considered as scalar control since it adjusts only magnitude and frequency of the voltage or current with no concern about the instantaneous values of motor quantities. It does not require knowledge of parameters of the motor, and it is an open-loop control. Thus, it is a low cost simple solution for low-performance applications such as fans and pumps. The other two methods are in the space vector control category because they utilize both magnitude and angular position of space vectors of motor variables, such as the voltage and flux. They are employed in high performance applications, such as positioning drives or electric vehicles [3, 4].
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Fuzzy Logic Based Speed Control of Induction Motor Considering Core Loss into Account

Fuzzy Logic Based Speed Control of Induction Motor Considering Core Loss into Account

loss into account is complicated [18-21]. Therefore, an IFOC method of induction motor drives taking core loss into account in terms of magnetizing current components has been proposed with PI speed controller loop in [22]. The extension work [22] of literature [18] has been done to regulate speed and rotor flux based on PI controller. In [22], the PI controller gains were changed to obtain de- sired speed under the variation of load torque. One set of fixed PI controller gain is unable to track the desired speed. Moreover, the steady state error cannot be mini- mized to zero by the conventional PI controller [16,21]. An IFOC of induction motor drive to regulate speed based on fuzzy-logic has been proposed in [16] neglecting core loss. To overcome the previously discussed disad- vantages of conventional PI controller and the complica- tion effects of core loss of induction motor drive, it would be desirable to design a well controller. The fuzzy- logic control (FLC) is seemed to be a suitable controller in terms of high dynamic response under the variation of load torque and parameters [16].
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A Comparative Study Based on PI and Fuzzy Control of Inverter fed Induction Motor Drive

A Comparative Study Based on PI and Fuzzy Control of Inverter fed Induction Motor Drive

its moving elements.) of the machine and its impact on applied loads. It also provides self starting of the motor. Due to power electronics commutation the motor is protected and the controller is also protected from, Damages.

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High Power Direct Drive WECS Using Fuzzy Logic Controller

High Power Direct Drive WECS Using Fuzzy Logic Controller

Abstract— Because of energy shortage and environment pollution, the renewable energy, especially wind energy has become more and more considerable all over the world. Direct drive wind energy conversion systems based on multipole permanent magnet synchronous generator (PMSG) have some advantages such as no gearbox, high power density, high precision and easy to control. In our research project, a 2MW direct drive wind energy conversion system is developed. In this paper, an efficient experimental method for high power converter is presented. A large circulating current flows in the converter, but only a small part of the current caused by the losses of the converter flows into the grid. The method can save a lot of energy when the converter is tested and the experiment can
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Direct Torque Control of Induction Motors with Fuzzy   Logic Controller

Direct Torque Control of Induction Motors with Fuzzy Logic Controller

To obtain improved performance of DTC drive during changes in the reference torque, it is possible to use a fuzzy-logic-based switching vector selection process. For this purpose a Mamdani-type fuzzy logic system will be used. The different output voltage states (active and zero states) are selected by using three inputs: flux e ö and

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Direct torque control of three phase induction motor using neural network fuzzy logic techniques

Direct torque control of three phase induction motor using neural network fuzzy logic techniques

In this paper Fuzzy Logic and proposed Neural Network based Direct Torque Control of Induction motor are designed. A Back Propagation Neural Network is used in proposed method. The training patterns of Neural Network are extracted from Fuzzy Logic Controller based Direct Torque Control of Induction Motor. The operating characteristics of the proposed drive is compared with the Direct Torque Control and Fuzzy Logic Control to verify the effectiveness under various conditions by investigating the transient response for the step change of speed command and the load torque. The simulation results are provided in the MATLAB/SIMULINK Environment to demonstrate the effectiveness of the proposed methodology
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A Fuzzy based Direct Torque Control System to Improve the Induction Motor Performance

A Fuzzy based Direct Torque Control System to Improve the Induction Motor Performance

The induction motor is most widely because of its high reliability, robust in operations, relatively low cost and modest maintenance requirements. But they require much more complex methods of control, more expensive and higher rated power converters than DC and permanent magnet machines. Three phase induction motor is widely used in industrial drive because they are reliable and rugged. Single phase induction motors are widely used for heavier loads for example in fans in household appliances. The fix speed service, induction motors are being increased with variable frequency drives. Induction motor achieves a quick torque response, and has been applied in various industrial applications instead of dc motors. It permits independent control of the torque and flux by decoupling the stator current into two orthogonal components FOC (Field Oriented Control). However it is very sensitive to flux, which is mainly affected by parameter variations. It depends on accurate parameter identification to achieve the expected performance. The vector control of IM drive for speed control is mainly classified into two types such as field oriented control (FOC) and direct torque control (DTC). In FOC, the speed of the induction motor is controlled like a separately excited dc-motor with more transformations and complexity involved in the system. In order to control the induction motor speed in simple way without required any transformations the DTC is used. In the middle of 1980 direct torque control was developed by Takahashi and Depenbrock as an alternative to field oriented control to overcome its problems. Direct torque control is derived from the fact that on the basis of the errors between the reference and the estimated values of torque and flux it is possible to directly control the inverter states in order to reduce the torque and flux errors within the prefixed band limits. Direct torque control is a strategy research for induction motor speed adjustment feeding by variable frequency converter. It controls torque on the base of keeping the flux value invariable by choosing voltage space vector.
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Speed Control of Induction Motor using Fuzzy Logic Controller

Speed Control of Induction Motor using Fuzzy Logic Controller

The block model of the induction motor system with the controller be developed using the power system, power electronics, control system, signal processing toolboxes & from the fundamental functions available in the Simulink library in Matlab / Simulink. In this paper, plots of voltage, torque, speed, load & flux, etc are plotted as functions of time with the controller and the waveforms are observed on the equivalent scopes after running the simulations. The entire system modelled in Simulink is a closed loop feedback control system consisting of the plants, controllers, samplers, comparators, feedback systems, the mux, de-mux, summers, adders, gain blocks, multipliers, clocks, sub- systems, integrators, state-space models, subsystems, the output sinks (scopes), the input sources, etc. The developed simulink model for the control of various parameters of the SCIM is shown in the Fig 2.
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Speed Control of Induction Motor using Fuzzy Logic Approach

Speed Control of Induction Motor using Fuzzy Logic Approach

It can be seen from the above figures that while using the Fuzzy Logic Controller the overshoots obtained a re lesser as compared to the case when the PI Controller is used. The settling time is also less in case of the Fuzzy Logic Controller, but the ris e time is larger. The Fuzzy Logic Controller, however, portraits a better response when the reference speed is changed (either decreased or increased with respect to the base speed). It tends to approach the new reference speed faster and has, comparatively, a very low overshoot. It can be observed from the PI controller diverges from the new reference speed and does not attend a steady state when it is very less as compared to the base speed or greater than the base speed. The Fuzzy Logic Controller on the other ha nd attains a steady state. Even though this attained speed is not exactly equal to the new referenc e speed, it is very much close to it. The torque plots show that while using the Fuzzy Logic Controller oscillations occur during starting while the PI controller doesn’t show any such characteristic. This is because the Fuzzy Logic Controller is based o n random knowledge of data. The machine provides a desirable response after some time as the controller first has to learn from or adjust according to the da ta provided by the user.
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Speed Control of Induction Motor using Fuzzy Logic approach

Speed Control of Induction Motor using Fuzzy Logic approach

This thesis presents a methodology for implementation of a rule-based fuzzy logic controller applied to a closed loop Volts/Hz induction motor speed control. The Induction motor is modeled using a dq axis theory. The designed Fuzzy Logic Controller’s performance is weighed against with that of a PI controller. The pros of the Fuzzy Logic Controllers (FLCs) over the conventional controllers are: (i) they are economically advantageous to develop, (ii) a wider range of operating conditions can be covered using FLCs, and (iii) they are easier to adapt in terms of natural language. Another advantage is that, an initial approximate set of fuzzy rules can be impulsively refined by a self-organizing fuzzy controller. For V/f speed control of the induction motor, a reference speed has been used and the control architecture includes some rules. These rules portray a nonchalant relationship between two inputs and an output, all of which are nothing but normalized voltages. These are:
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Fuzzy Based Approach For Direct Torque Control Of Three Phase Induction Motor

Fuzzy Based Approach For Direct Torque Control Of Three Phase Induction Motor

control of asynchronous motor parameters like speed, torque and flux are of utmost importance. The machine learning algorithms applied in other applications [17-20] can also be used for speed control with certain modifications. From the study of the control approaches, it is identified that torque control of AC motor could be accomplished according to various methods ranging from low-cost Volts/Hz ratio approach to sophisticated sensor less based vector control method. But every scheme has its disadvantages like losses, the requirement of separate current control loop, coordinate transformation, current ripple and torque etc. In this work, the principles of DTC method combined with artificial intelligent controller based on fuzzy logic will be discussed and used. The controller with DTC control will be simulated on an induction machine of squirrel type under different conditions. 3.1 Principle of Vector Control
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An Optimal Fuzzy Logic Control Strategy For Switched Reluctance Motor Drive

An Optimal Fuzzy Logic Control Strategy For Switched Reluctance Motor Drive

Fig.2. Inverter for three-phase SRMs with single high-side transistor and single low-side free-wheeling power diode. The situation is made even more critical at high speed because during commutation the off-going phase requires that the shared high-side transistor be turned off to speed up its demagnetization stage and, at the same time, the incoming phase requires it turned on for its magnetization and to produce the required torque. However, the particular application in this research of a SRM driving a centrifugal pump required operation for a large number of hours at medium and low speeds, like in central system ground-source heat pump or similar applications. Hence, the limitation of such power converter at high speed was not considered a problem in such specific application.
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Comparative Analysis of PI Controller and Fuzzy Logic Controller for Speed Control of Three Phase Induction Motor Drive

Comparative Analysis of PI Controller and Fuzzy Logic Controller for Speed Control of Three Phase Induction Motor Drive

Abstract - Three-stage induction magnetic motors are utilized as a part of an extensive employed in numerous varieties of mechanical applications. The present day innovation utilizes the speed of stimulation engine and can be effectively controlled by drives. These drives utilize fast power transistors with different switching systems, for the most part PWM plans. For rapid variable rate drives, exact arrangements have been accommodated speed control of three stage induction magnetic motors. The Fuzzy rationale controller gives a compelling answer for accomplishing better execution contrasted with PI controller. The proposed work focused on the particular speed manage connected with three stage induction magnetic motor get dependent relative on fuzzy judgments controller weighed against PI controller. The actual planned work incorporates a voltage resource PWM inverter nourished indirect vector manage method connected with induction magnetic motor. traditional indirect vector manage method connected with induction magnetic motor features presents ordinary PI controller in external speed loop as a result of its effortlessness and steadiness, it is demonstrated that the low exactness of the speed controller corrupts the execution of the entire framework. To beat this issue, alternative connected with PI controller by a very good Controller dependant on fuzzy arranged principle and hypothesis can be planned and proposed. The actual overall performance in the intelligent controller can be simulated as a result of electronic simulation applying MATLAB-SIMULINK within different operating condition. The simulation results reveal that the execution of the proposed controller is superior to anything that of the custom PI controller.
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DIRECT TORQUE CONTROL OF INDUCTION MOTOR USING FUZZY SLIDING MODE CONTROL

DIRECT TORQUE CONTROL OF INDUCTION MOTOR USING FUZZY SLIDING MODE CONTROL

234 | P a g e plant a tuning process must be performed. The most famous, which is frequently used in industrial applications to tune the PID controller is the Ziegler-Nichols(ZN) method which does not require a system model and control parameters are designed from the plant step response. Tuning using this method is characterized by a good disturbance rejection but on the other hand, the step response has a large percentage overshoot in addition to a high control signal that is required for the adequate performance of the system. The model based techniques such as frequency response methods, Root locus and pole assignment design techniques are also proposed in addition to transient response specifications. The efficiency of the tuning law depends on the accuracy of the proposed model as well as the assumed conditions with respect to actual operating conditions.
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Indirect Vector Controlled Induction Motor Drive for High Performance Using Fuzzy Logic

Indirect Vector Controlled Induction Motor Drive for High Performance Using Fuzzy Logic

The speed control of IM issues are traditionally handled by fixed gain PI and PID controllers. However the fixed gain controllers are very sensitive to parameter variations, load disturbances etc. Thus, the controller parameters have to be continuously adapted. The problem can be solved by several adaptive control techniques such as model reference adaptive control, sliding mode control smc, variable structure control VSC and self tuning PI controller etc. The design of the entire above controller depends on the exact system mathematical model. However it is often difficult to develop a accurate mathematical model due to unknown load variation and unavoidable parameter variations due to saturation, temperature variations and system disturbance. To overcome the above problems, Fuzzy logic controller (FLC) is being used for motor control purpose. There is some advantage of fuzzy logic controller as compared to conventional PI, PID and adaptive controller such as it does not require any mathematical model, it is based on linguistic rules within IFTHEN general structure, which is the basic of the human logic.
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Neuro Fuzzy Based Unified Power Quality Conditioner for Power Quality Improvement Fed Induction Motor Drive

Neuro Fuzzy Based Unified Power Quality Conditioner for Power Quality Improvement Fed Induction Motor Drive

The unified power quality conditioner (UPQC) plays an important role in the constrained delivery of electrical power from source to an isolated pool of load or from a source to the grid. In this paper presents neuro fuzzy based unified power quality conditioner. The series converter is used to compensate voltage sag/swell compensation. The shunt converter is used to compensate reactive power compensation present in the linear and nonlinear load. The performance of neuro fuzzy and with artificial neural network controller is compared. This approach eliminates the total harmonic distortions efficiently. The performance of proposed system is analysed using Mat lab/Simulink.
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Direct Field-Oriented Control Using Fuzzy Logic Type -2 for Induction Motor with Broken Rotor Bars

Direct Field-Oriented Control Using Fuzzy Logic Type -2 for Induction Motor with Broken Rotor Bars

In the paper an analysis of the Direct Field Control Fuzzy logic type-2 of induction motor drive with broken rotor bars is presented. The simplicity of traditional regulators makes them popular and the most used solution in the nowadays industry. However, they suffer from some limitations and cannot deal with nonlinear dynamics and system parameters variation. In the literature, several strategies of adaptation are developed to alleviate these limitations. Artificial intelligent has found high application in most nonlinear systems same as motors drive. Because it has intelligence like human but there are no sentimental against human like angriness and.... Artificial intelligent is used for various points like approximation, control, and monitoring. Because artificial intelligent techniques can use as controller for any system without requirement to system mathematical model, it has been used in electrical drive control. With this manner, efficiency and reliability of drives increase and volume, weight and cost of them decrease.
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Speed Control For Direct Current (DC) Motor With An Approach Of Fuzzy Logic

Speed Control For Direct Current (DC) Motor With An Approach Of Fuzzy Logic

Now imagine a light bulb with a switch. When the switch is closed, the bulb goes on and is at full brightness, says 100 Watts and when the switch is closed is 0 Watts. Now if the switch is closed in a fraction of a second and then opens for the same amount of time, the filament would not have time to cool down and heat up. This will just get an average glow of 50 Watts. This is how lamp dimmers work, and the same principle is used by speed controllers to drive a motor. When the switch is closed, the motor sees 12 Volts, and when the switch is open it sees 0 Volts. If the switch is open for the same amount of time as it is closed, the motor will see an average of 6 Volts, and will run more slowly accordingly. As the amount of time that the voltage is on increases compared with the amount of time that it is off, the average speed of the motor increases.
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Efficiency optimization of a direct torque control (DTC) induction motor drive

Efficiency optimization of a direct torque control (DTC) induction motor drive

In early 1970s, the appearance of the Field oriented control (FOC) allowed a considerable increase of dynamic performance of the induction motors [44]. Theoretically, FOC that based on Fleming's law [45] makes the control performance of induction motor as good as the DC motor’s where torque and flux are decoupled and hence could be controlled independently. However, during the practical practice of engineering application, the actual performance of vector control will be worse than predicted due to the effect of factors such as inaccurate control model and variable motor parameters [46]. Several methods are investigated to inquire into this problem and some improved techniques such as flux observer, rotor resistance identification are adopted in order to reduce the effect of this variation so that the control performance of FOC can be satisfied in most of applications [44], [45]. The Direct Torque Control was first introduced by Takahashi around the mid-1980s has found great success with the notion to reduce the dependence on parameters of induction motor and increase the precision and the dynamic of flux and torque response [47].
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Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

Modelling and Control Strategy of Induction Motor Using Fuzzy Logic Control Technique

Abstract - An induction motor is an asynchronous AC (alternating current) motor. The least expensive and most widely used induction motor is the squirrel cage motor. The interest in sensor less drives of induction motor (IM) has grown significantly over the past few years due to some of their advantages, such as mechanical robustness, simple construction, and less maintenance. These applications include pumps and fans, paper and textile mills, subway and locomotive propulsions, electric and hybrid vehicles, machine tools and robotics, home appliances, heat pumps and air conditioners, rolling mills, wind generation systems, etc. So, Induction motors have been used more in the industrial variable speed drive system with the development of the vector control technology. This method requires a speed sensor such as shaft encoder for speed control. This paper presents a novel design of a Takagi-Sugeno fuzzy logic control scheme for controlling some of the parameters, such as speed, torque, flux, voltage, etc. of the induction motor. Induction motors are characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements, and hence it can be considered as a challenging engineering problem. The development of advanced control techniques has partially solved induction motor’s speed control problems; because they are sensitive to drive parameter variations. Fuzzy logic based controllers are considered as potential candidates for such an application. Further, the Takagi-Sugeno control strategy coupled with rule based approach in a fuzzy system when employed to the induction motor yields excellent results compared to the other methods as this becomes a hybrid & integrated method of approach. Such a mixed implementation leads to a more effective control design with improved system performance, cost- effectiveness, efficiency, dynamism, & reliability. The closed loop speed control of the induction motor using the above technique thus provides a reasonable degree of accuracy which can be observed from the results depicted at the end. The simulation results presented in this paper show the effectiveness of the method developed & have got a wide number of advantages in the industrial sector & can be converted into a real time application using some interfacing cards.
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