switching modes. The selection is made to restrict the flux linkage and electromagnetic **torque** errors within the respective flux and **torque** hysteresis bands, to obtain fast **torque** response, low **inverter** switching frequency and low harmonic losses. The required optimal switching voltage vectors can be selected by using a so called optimum switching voltage vector look-up table. This can be obtained by simple physical considerations involving the position of the stator flux linkage space vector, the available switching vectors and the required **torque** and flux linkage. In the Fig. 2, Configuration of Conventional DTC drive is shown, in which the comparison between the reference and the actual value is taken and the errors are processed through hysteresis band controllers. The flux loop controller has **two** levels of digital output and the **torque** control loop has three levels of digital output. The feedback flux and **torque** are calculated from the **induction** machine terminal voltages and currents. The three phase terminals quantities are converted into **two** phase stationary d q components, which are used for estimating **motor** **torque** and stator linked flux. Based on the resultant flux position and the errors in flux magnitude and in **torque**, a three-dimensional look up table is referred to decide the **inverter** switching. The „Stator Flux and **Torque** Estimator‟ block shown in Fig. 2 gives the sector number S k

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The **two**-**level** voltage source inverters are not suitable for med iu m and high power applications due to large dv/dt stresses and more harmonic distortion. In order to reduce these problems three -**level** inverters are introduced in 1980s [11]. The detailed survey on mult ileve l **inverter** topologies is given in [12]. In order to obtain controllable three phase power fro m a mu ltilevel **inverter**, various PWM algorith ms can be generated by using both SV and TC approaches. However, in SV approach the comple xity will be increased due to the more number of voltage vectors. Hence, in most number of applications the carrier based PWM algorithms are popular for mult ileve l inverters. Few simp lified approaches by using duty cycle and offset times have been proposed for carrier based SVPWM algorith m based multilevel inverters [13], [14].

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by a hysteresis controller and the speed loop was **controlled** by a fuzzy-logic controllerKashif et al . [17] utilized a three-layer feed-forward back propagation artificial neural network for flux control of a B4 inverterfed IM drive. El Badsi et al . [18] used a DTC scheme for **torque** and flux control of a B4 **inverter**-fed IM drive. Unfortunately, the **two** capacitor voltages were assumed constant in these papers. In fact, as a result of one-phase current flows through the split dc-link voltage sources, the fluctuation will inevitably appear in the **two** capacitor voltages, which deteriorates the output performance of the B4 **inverter** (i.e., **torque** pulsation and unbalanced three-phase currents). More seriously, if the balanced condition of the currents flowing in the **two** capacitor voltages is corrupted, the **two** capacitor voltages will deviate in **two** opposite directions till shutting down of the B4 **inverter**. With the development of fast and powerful microprocessors, increasing attention has been dedicated to the use of model predictive control (MPC) in power electronics [19]. The first ideas about this strategy applied to power converters started in the 1980s [20], [21]. The main concept is based on calculating the system’s future behavior to obtain optimal values for the actuating variables. With this intuitive concept, predictive control can be applied to a variety of systems, in which constraints and nonlinearities can be easily included, multivariable case can be considered, and the resulting controller is easy to implement [22]. These features render the approach very attractive and effective for the control of power electronics system [23], [24], including drive control [25]–[27], especially predictive **torque** control (PTC; particular for a **two**-**level** converter with horizon N = 1). In the PTC, the complete model and future behavior of the **inverter**-fed **drives** are taken into account. A cost function relating to **torque** and flux errors reduction is defined to evaluate the effects of each voltage vector and the one minimizing the cost function is selected [28]–[36]. In spite of the outstanding performance of B6 **inverter**-fed **drives** based on the PTC, PTC for B4 **inverter**-

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The vector control is still very complex to implement. As a consequence of the perseverant efforts of various research engineers, an improvised scalar method known as **Direct** **Torque** Control (DTC) was invented. This method considerably alleviates the computational burden on the control platform while giving a performance which is comparable to that of a vector **controlled** drive. In this paper, the DTC scheme employing a Voltage Source **Inverter** (VSI) is possible to control directly the stator flux linkage and the electromagnetic **torque** by the optimum selection of **inverter** switching vectors. The selection of **inverter** switching vector is made to restrict the flux and **torque** errors within the respective flux and **torque** hysteresis bands. This achieves a fast **torque** response, low **inverter** switching frequency and low harmonic losses. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink.

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The nonlinear IM model treated in this paper is fourth order with the state variables: **torque**, stator flux, rotor flux and other flux-dependent state. The obtained linear IM model using FBL is of second order, with only the **torque** and stator flux magnitude as dissociate state variables. Thus the new linear IM model is obtained spontaneously, very simple, and it substantially simplifies the controller design. The flux and **torque** are **controlled** by the new DTC scheme and the proposed controllers include SMC to maintain robust sensorless operation of IM drive. This technique based on the **torque**-flux linearization and control is different from existing methods discussed in [6]-[8], which are depending on current control. The combination of FBL and SMC **techniques** preserves the fast and robust response of conventional DTC while entirely eliminating the **torque** and flux ripple.

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In this paper a high performance advanced discontinuous pulse width modulation (ADPWM) based **direct** **torque** **controlled** (DTC) **induction** **motor** drive operating at high line side voltages is proposed. The proposed ADPWM based IM drive uses a special category of sequences which not only reduces the switching losses but also reduces the line current distortion during high speed operations. However **analysis** in this paper is limited to harmonic ripple in line current and comparison of the proposed method is done with the conventional DTC (CDTC), conventional space vector pulse width modulation (CSVPWM) based DTC and clamping sequences based DTC. The proposed method uses a special category of DPWM sequences, 0121 and 7212. This category of DPWM sequences are referred as double switching clamping sequences as they not only clamp one of the phase to either of the buses but also switches one of the remaining **two** phases twice in every sub cycle. In this paper it is shown that, utilizing DPWM sequences and by changing the zero state at any spatial angle where is between 00 and 600an infinite number of ADPWM methods can be generated which are categorized as “continual clamping” and “split clamping” sequences. It will be shown that steady state line current distortion at higher line side voltages is reduced significantly compared with the CDTC, CSVPWM based DTC as well as the ADPWM based DTC using clamping sequences.

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This review paper studies the most commonly used electric driving methods of **induction** **motor**. Conventionally **two** **level** inverters are used in **direct** **torque** control method for controlling **torque** of **induction** **motor** which produces **torque** ripple. Therefore our main objective is to reduce the **torque** ripples which are produced by **two** **level** **inverter**. The new technique is proposed to minimize the **torque** ripples using three **level** **inverter**. **Direct** **Torque** Control (DTC) method is simple method and having excellent robustness of **torque** control for the drive system. The three-**level** neutral point clamped (NPC) inverters have been mostly used in applications. SQIM used three **level** NPC for controlling **torque** of **induction** **motor**

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3.2 DTC strategy for the DFIM connected to **two** 3LVSIs The development of speed control and DTC of doubly fed **induction** motors has favored the use of three-**level** inverters. The increase in levels number of the latter proves to be a better solution in high power **drives**. The **inverter** is made up of switching cells, generally with transistors or GTO thyristors for large powers [21]. In this section, we present the study DFIM associated with **two** 3LVSIs with neutral point camped structure con- trolled by the DTC algorithm. Figure 3 illustrates the general schema of 3LVSI with NPC structure; it is one of the structures of three-**level** **inverter**. It has a lot of advantages, such as the higher number of voltage vectors generated, less harmonic distortion and low switching frequency [22]. Each arm of the **inverter** consists of 4 switches: S k1 , S k2 , S k3 , S k4 . The S k1 and S k2 have comple-

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increasingly being used in most of the industrial applications. The development of high performance control strategies for AC **drives**, driven by the requirement of industry, has resulted in a rapid evolution during the last **two** decades. The Predictive **Torque** Control (PTC) technique has features of precise and quick **torque** response. This method is gaining popularity in the industry due to its simplicity and high dynamic performance.The control strategy combines the use of classical PI controller to obtain good steady state response and a predictive controller scheme to achieve good dynamic response. The main characteristic of predictive control is the use of a model of the system for predicting the future behaviour of the **controlled** variables. This information is being used by the controller to obtain the optimal actuation, according to a predefined optimization criterion. In predictive control scheme, the control objectives are defined as a cost function, which is to be minimized to have greater flexibility to include constraints which results in low computational complexity compared to **Direct** **Torque** Control (DTC) scheme. PTC offers high dynamic performance, accurate speed response. The PTC based voltage source **inverter** fed **induction** **motor** drive is capable of offering four quadrants in the **torque**-speed plane of operation like, forward motoring, forward generating, reverse generating and reverse motoring. To validate the proposed algorithms mathematical models were developed for **induction** **motor**, estimation of **torque** and flux and control logic. These models were integrated and simulations were carried out using Matlab/Simulink. Variation in stator currents, speed, electro-magnetic **torque** developed and stator flux during different operating conditions such as starting, steady state, sudden change in load and speed reversal are observed with the help of waveforms and results are discussed.

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The **direct** **torque** control (DTC) is one of the actively researched control schemes of **induction** machines, that provides a very quick and precise **torque** response without the complex field-orientation block and the inner current regulation loop. In **Direct** **Torque** Control it is possible to control directly the stator flux and the **torque** by selecting the appropriate **inverter** state.. DTC is the latest AC **motor** control method, developed with the goal of combining the implementation of the V/f-based **induction** **motor** **drives** with the performance of those based on vector control [1-3]. It is not intended to vary amplitude and frequency of voltage supply or to emulate a DC **motor**, but to exploit the flux and **torque** producing capabilities of an **induction** **motor** when fed by an **inverter** .CSI permits easy power regeneration to the supply network under the breaking conditions, what is favorable in large power **induction** **motor** **drives**. In a **direct** **torque** **controlled** **induction** **motor** drive supplied by current source **inverter** it is possible to control directly the modulus of the rotor flux-linkage space vector through the rectifier voltage, and the electromagnetic **torque** by the supply frequency of the CSI[4]. In this paper the solution based on a stator flux vector control (SFVC) scheme has been proposed . This scheme may be considered as a development of the basic DTC scheme with the aim of improving the drive performance.

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ABSTRACT: Earlier studies have pointed out the limitations of conventional inverters, especially in high-voltage and high-power applications. In recent years, multilevel inverters are becoming increasingly popular for high-power applications due to their improved harmonic profile and increased power ratings. Several studies have been reported in the literature on multilevel inverters topologies, control **techniques**, and applications. However, there are few studies that actually discuss or evaluate the performance of **induction** **motor** **drives** associated with single-phase multilevel **inverter**. This paper presents then a comparison study for a cascaded H-bridge multilevel **direct** **torque** control (DTC) **induction** **motor** drive. In this case, symmetrical and asymmetrical arrangements of five and seven-**level** H-bridge inverters are compared in order to find an optimum arrangement with lower switching losses and optimized output voltage quality. The carried out experiments show that an asymmetrical configuration provides nearly sinusoidal voltages with very low distortion, using less switching devices. Moreover, **torque** ripples are greatly reduced

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The DTC scheme consists of **torque** and flux comparator (hysteresis controllers), **torque** and flux estimator and a switching table. It is much simpler than the vector control system due to the absence of coordinate transformation between stationary frame and synchronous frame and PI regulators. DTC does not need a pulse width modulator and a position encoder, which introduce delays and requires mechanical transducers respectively. DTC based **drives** are **controlled** in the manner of a closed loop system without using the current regulation loop. DTC scheme uses a stationary d-q reference frame having its d-axis aligned with the stator q- axis. **Torque** and flux are **controlled** by the stator voltage space vector defined in this reference frame [13]. The basic concept of DTC is to control directly both the stator flux linkage and electromagnetic **torque** of machine simultaneously by the selection of optimum **inverter** switching modes. The DTC controller consists of **two** hysteresis comparator (flux and **torque**) to select the switching voltage vector in order to maintain flux and **torque** between upper and lower limit. DTC explained in this paper is closed loop drive. Here flux and **torque** measured from the **induction** **motor** using proper electrical transducer. Then flux and **torque** errors are found out by equation (3) and (4) [14].

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Several studies have suggested the application of the ANN technique to select the states of the voltage **inverter** switches used to power the DTC-**controlled** IM [107– 112]. The idea is always to replace the conventional switching table that determine the **inverter** states by neural selector capable of managing control signals in the same way. Fig. 8 shows the block diagram of **Direct** **Torque** Neural Control (DTNC). The architecture in- cludes a multilayer neural network allowing replacing both hysteresis comparators and the selection table. This neural network is composed of an input layer, **two** hidden layers and an output layer. The input layer is composed of three neurons, designated respectively by the **torque** error, the flux error and the angular position (θ) of the stator flux vector. The **two** hidden layers each consist of ten neu- rons. The output layer consists of three neurons that Fig. 6 Synoptic schema of DTC-Fuzzy control of the

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The **induction** **motor** or asynchronous is the most widely used electrical drive. [1] Has explained the complete **analysis** of electrical machinery drive system. Actually because independent control of **torque** and flux separately excited dc **drives** are simpler in control. Due to ruggedness, efficiency and simplicity the **induction** motors have been used in several applications for over a century. [2-3] has presented the **analysis** and simulation model development of **induction** **motor** in MATLAB / SIMULINK software. In AC **drives** control the **direct** **torque** control scheme is considered as one of the most advanced technology in the modern world. The **direct** **torque** control scheme is a simple technique compare to other **techniques**. In this technique by selecting optimum **inverter** switching modes the **motor** **torque** and flux are **controlled** independent and also **direct**. The primary input of the **motor** is stator voltage and stator current. From this the stator flux and electromagnetic torques are calculated. The **torque** errors and flux errors are restricted within the hysteresis band. The **two** important merits of this **direct** **torque** control technique is improved in steady state efficiency and quick **torque** response in transient operation.

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provides a simple control structure. Since it was introduced in the middle of 1980’s [1], [2] many researchers have been working in this area and several modifications and improvements have been made in order to overcome the **two** major disadvantages of the hysteresis-based of DTC scheme, namely the high **torque** ripple and variable switching frequency of the **inverter**. Previous proposed **techniques** to overcome these problems include the use of variable hysteresis band, **controlled** duty cycle technique and use of space vector modulation (DTC-SVM) based. All these **techniques** have managed to improve the performance of DTC, in the expense of loosing the simple structure of DTC. In [3]-[5], a simple approach to solve the problems and at the same time retaining the simple structure of DTC was introduced. In this approach, a constant frequency **torque** controller was used to replace the hysteresis **torque** controller.

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The classical **direct** **torque** control strategyis a closed loop control scheme, the important elements of the control structure being: the power supply circuit, a three phase voltage source **inverter**, the **induction** **motor**, the speed controller to generate the **torque** command and the DTC controller. The CDTC controller again consists of **torque** and flux estimation block, **two** hysteresis controllers and sector selection block, the output of the CDTC controller is the gating pulses for the **inverter**.

With the **torque** and flux producing components of stator current command and rotor field angle, we get d-q axis currents. d-q axis current commands in rotating frame are then converted to stationary reference frame using transformation. In case of indirect vector control, rotor flux and **torque** can be independently **controlled** by stator axis current components i ds and i qs [1,3].

Most of the faults in three-phase **induction** motors have relationship with air-gap eccentricity which is the condition of the unequal air-gap between the stator and the rotor. This fault can result from variety of sources such as incor- rect bearing positioning during assembly, worn bearings, a shaft deﬂection, heavy load and so on. In general, there are **two** forms of air-gap eccentricity: radial (where the axis of the rotor is parallel to the stator axis) and axial. Each of them can be static (where the rotor is displaced from the stator bore centre but is still turning upon its own axis) or dynamic eccentricity (where the rotor is still turning upon the stator bore centre but not on its own centre) (Siddiqui et al., 2015a; Hegde and Maruthi, 2012; Intesar et al., 2011; Sahraoui et al., 2008).

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The classical DTC technique is in terms of hysteresis-loop controller with single vector switching table. Its switching frequency differs with speed and load **torque**, which can bring out high **torque** pulsation particularly in low speed due to the low switching frequency, which greatly restricts its application [9][10]. Common disadvantages of conventional DTC are high **torque** ripple and slow transient response to the step changes in **torque** during start-up [11]. Therefore, intelligent methods are used such as Artificial Neural Networks (ANN), Fuzzy Logic (FL) and Sliding mode control (SMC) theory [12][13]. Majority of them are concerned with enhancement of the flux and **torque** estimator and combined operation of DTC with a space vector-modulation (SVM) technique [14].

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