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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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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.

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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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

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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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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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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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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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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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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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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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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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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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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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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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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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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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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