schemes. Thus, it is proven that for accurately elaborating the modified mathematical **model**, the **voltage** induced in the **stator** open phase winding and its relevant **stator** **flux** linkage should be taken into account although they do not contribute to torque production in the post-fault operating mode. The novelty of the proposed mathematical **model** compared with the previous study [16] is the implementation of the proposed current **model**-**based** and **voltage** **model**-**based** **stator** **flux** estimators requires similar machine parameter information as that in the healthy case [15] except that the leakage inductance is additionally demanded for the proposed **voltage** **model**- **based** **stator** **flux** estimator. Thus, the proposed **voltage** **model**- **based** **stator** **flux** **estimation** scheme does not rely on the rotor position information. It is also shown that the reconfiguration from the SSTP scheme to the ELES scheme for fault-tolerant control purpose results in a significant magnitude imbalance between the α- and β-inverter **voltage** drop (IVD) components. Although this imbalanced issue does not adversely affect the performance of the proposed current **model**-**based** DTC, conventionally neglecting the IVD in the proposed **voltage** **model**-**based** estimator does lead to a significant magnitude imbalance between the α- and β- components of the estimated **stator** **flux** linkages. As a result, phase currents under the proposed **voltage** **model**-**based** DTC become seriously distorted. To solve this problem, a compensation scheme is proposed and verified by experimental results.

Show more
14 Read more

The rotor **flux** **based** MRAS speed estimator, developed by Schauder in [15], is the most popular scheme among MRAS speed observers due to its simplicity. However, this method is sensitive to the **stator** resistance variations. In addition, the existence of pure integrator in the reference **model** leads to problems with initial condition, drift, and offset. To overcome the problem of pure integration, the pure integrator can be replaced with a low pass filter; however, the accuracy of speed **estimation** at low speeds is decreased and a time delay is produced [16, 17]. Another solution for pure integration problem is employing the rotor back Electromotive Force (EMF) **based** MRAS speed estimator, which improves low speed operation, but signal to noise ratio is reduced considerably due to the existence of a derivative operator in the reference **model**. In addition, this method is also sensitive to the variations in the **stator** resistance [3, 18]. The air gap reactive power **based** MRAS speed estimator is independent of **stator** resistance variations in which the outer product of **stator** current and back-EMF represents the air gap reactive power. However, similar to EMF **based** MRAS speed estimator, the derivative operator is used in the reference **model** [18]. In the recent MRAS speed estimators, measured **stator** current is considered as the reference **model**. As a result, in the adjustable **model**, **stator** current is estimated to compare with measured **stator** current. The adaptation mechanism developed so far can be divided into two groups. In one approach known as **stator** current **based** MRAS [19, 20], rotor **flux** vector is multiple to error signal (error between measured **stator** current and estimated value). In another approach, which belongs in reactive power **based** MRAS category [18, 21], **stator** **voltage** vector is multiple to error signal. In these estimators, the rotor **flux** identification is necessary. The rotor **flux** can be estimated by the use of measured **stator** currents (dependent method) or by the use of state space equations of induction motor, which is independent of the measured **stator** current (independent method).

Show more
10 Read more

The indirect field oriented control algorithm does not evaluate the **stator** current components in the rotational reference frame (d,q). Therefore, the position of the space vector of the rotor magnetizing **flux** is not required to be evaluated. This brings the advantage of lower demands on the microcontroller computational resources. Also, because of high sensitivity of the rotor **flux** **model** to motor parameters, it makes the control of the motor torque algorithm less dependent on swinging motor parameters. Because the quadrature axis component of the **stator** current (I sq ) is one of the input quantities for **stator** **voltage** evaluation, it is necessary to estimate this quantity with help of known quantities. For **estimation**, use the same dependency between rotor the slip frequency and the quadrature axis component of the **stator** current, as in the case of feed forward (Equation 19). The formula can be trasnformed in the following way:

Show more
22 Read more

Modern AC drives require a fast digital realization of many mathematical operations concerning control and estimator's algorithms, which are time consuming. Therefore developing of custom-built digital interfaces as well as digital data processing blocks and sometimes even integration of ADC converters into single integrated circuit is necessary. Due to the fact that developing an ASIC chip is expensive and laborious, the FPGA **based** solution should be used on the design stage of the algorithm. In [36], the application of FPGA in DTC of IM drive is presented. In [30], the DTC was implemented in FPGA using fixed point arithmetic with a variable word-size approach. This is proposed as an alternative to 16 or 32-bit choices used before. A separation of the algorithm in functional blocks is used to simplify validation task which was accomplished in comparison with MATLAB results. Following a tendency in the research area, the algorithm proposed is implemented in unique FPGA device, which allows for a faster validation and simplifies the control structure. To ensure a proper **voltage** vector selection by the DTC controller, the **estimation** of **stator** **flux** must be accurate. The calculation of the electromagnetic torque too, depends on the accuracy of **stator** **flux** **estimation**. Most of the **stator** **flux** calculation is **based** on **voltage** **model**, current **model** or the combination of both models. These models require a precise

Show more
24 Read more

An approach for conventional DTC **flux** **estimation** is **based** on the **voltage** **model** integrators. The pure integrator has the following drawbacks. 1) input dc offset leads the output into saturation limit; 2) initial condition error produces a constant output dc offset; and 3) it is very sensitive to **stator**-resistance identification, especially at low speeds. Usually, adaptive observers employ the time-variable full order IM **model** to estimate the **flux**. At least one equation of the **model** contains a speed-dependent term, and the observer must always be speed adaptive. In most cases, the rotor speed calculation is the last step of the **estimation** process. Thus, the estimated speed is always affected by cumulative errors, noises, and time delays. This in accurate speed **estimation** is feed back to the adaptive **flux** observer and then the accuracy of **flux** and speed estimator may progressively worsen. Undesirable effects, such as limit cycles, higher noise sensitivity, or delays may occur and deteriorate the system overall performances, especially at very low **stator** frequencies, where the fundamental excitation is low. A solution for this is to use non adaptive observers. A distinct approach for sensor less **flux** **estimation** is **based** on full-order sliding-mode observers (SMOs). The sliding mode control theory presents promising features: disturbances rejection, strong robustness to parameter deviations, system order reduction [11]. Solutions using speed-adaptive SMOs are proposed in [12, 13]. These observers use sliding mode surfaces that combine the **stator**- current errors within the **flux** **estimation**. They are speed adaptive, with the aforementioned disadvantages. Accurate **model** parameters are required, especially in low-speed operation; therefore, online parameter identification is employed. In this paper, SVM-DTC scheme **based** on input output linearization technique for induction machine drives is developed. Furthermore, a sliding-mode observer is used to estimate **flux** is introduced. The observer is inherently sensor less because it does not employ the rotor speed adaptation, and thus they are insensitive to speed **estimation** errors. Moreover, the observer is extremely robust. Simulation results with a sensor

Show more
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.

Show more
11 Read more

various performance aspects (i.e., **stator** current, electromagnetic torque, **stator** **flux**, rotor speed and pump output pressure) through Matlab Simulink environment. In the event of eliminating overshoot and ripples in torque, **flux** and speed, the proposed ANFIS –DTC controller exhibits satisfactory results in comparison with the conventional DTC and DTC with fuzzy logic control. The simulation results confirm that the application of ANFIS for DTC enhances the performance of the drive with improved **flux** and torque control capability. The simplified design and the reduced complexity are the additional features achieved through the proposed control method. In terms of pump output also, DTC-ANFIS results smooth pressure compared to traditional DTC and DTC with fuzzy logic control. The simulation results and the comparison study specify that proposed ANFIS **based** DTC control exhibits improved performance. Acknowledgements

Show more
12 Read more

A three-phase 2-level inverter with dc link configuration can have eight possible switching states, which generates output **voltage** of the inverter. Each inverter switching state generates a **voltage** Space Vector (V1 to V6 active vectors, V7 and V8 zero **voltage** vectors) in the Space Vector plane (Figure: space vector diagram). The magnitude of each active vector (V1to V6) is 2/3 Vdc (dc bus **voltage**).

This venture proposes a prescient torque control (PTC) plot for the B4 inverter-sustained enlistment engine (IM) with the dc-connect **voltage** balance concealment. The **voltage** vectors of the B4 inverter under the change of the two dc-connect capacitor voltages are determined for exact expectation and control of the torque and **stator** **flux**. The three-stage streams are compelled to stay adjust by straightforwardly controlling the **stator** **flux**. The **voltage** counterbalanced of the two dc-interface capacitors is displayed and controlled in the prescient perspective. In this venture fuzzy controller is actualized to decrease the swell substance and contortion in the yield wave shapes. The outcomes checked through MATLAB/SIMULINK condition.

Show more
In the recent times, in the industrial areas, the Current Alternative rotating machines are more usable especially double-fed Induction machine (DFIM), because of its many advantages over other types of rotating electrical machines. Its advantages can be summarized as following variable speed, its construction is simple, low cost, dependability, durability, and especially its maintenance is simple and economical. These benefits have made it the target of a lot of research, mainly as far as the realization of robust controls and its operation with or without a speed sensor. Double-Fed Induction machine (DFIM), is the nonlinear machine, fed by two **voltage** source the **stator** and rotor, strongly Torqued (the coupling between the electromagnetic Torque and **flux**), they function as multivariate machines, hence the complexity and difficulty of operation and control. With the evolution and development of new technologies of electronics and computers, the problems inherent in the control and the operation of various applications of variable speed DFIM are solved and simplified; it gives opportunities for speed control with or without mechanical sensors, as well as **flux** control for the

Show more
11 Read more

This paper presents design and implementation of vector control of induction motor. This method leads to be able to adjust the speed of the motor by control the frequency and amplitude of the **stator** **voltage** of induction motor, the ratio of **stator** **voltage** to frequency should be kept constant, which is called as V/F or vector control of induction motor drive. This paper presents a comparative study of open loop and close loop V/F control induction motor. The V/F control is **based** on advent of **stator** **voltage** derivatives. Simulation is carried out in MATLAB/SIMULINK environment and results are compared for speed control of induction motor.

Show more
After the success achieved with the application of EL CID to turbogenerators, the next logical step was its application to hydrogenerators. This was demonstrated in the factory of a British manufacturer of large machines in the early 1980's. The demonstration, carried out by John Sutton, was very positive, except in the region of core joints, which had been introduced to allow shipment of such very large **stator** units. At that time an "air-cored coil" was used to provide the reference of the main circumferential **flux**. The Reference Coil was held on the **stator** core bore by a magnetic base, with the plane of its turns in line with the longitudinal (or axial shaft) direction. Consequently, leakage **flux** from the core, which is circumferential in direction, linked with the Reference Coil turns. It was suggested, therefore, that the problem, caused by the extra large leakage **flux** at a core joint , could be alleviated by re-siting the Reference Coil close to the joint. This changed, in effect, the PHASE Reference of the EL CID set-up, which will be shown to be undesirable, unless appropriate compensation is made (See Section 8.3.3).

Show more
200 Read more

The accuracy in calculation of U d , U q is very important as they are used to generate inverter gating pulses such that direct torque and **flux** control for the induction motor drive achieved with minimum torque ripple [11]. SVPWM need to know the reference **voltage** vector in which sector, in order to use the adjacent basic **voltage** vector to synthesis. According to a given reference **voltage** component U , U , using Table 1 we can determine U ref the number of sector.

Up to date, a number of strategies were suggested to mitigate the LPF negative effects [14]-[16] and thus, improving the performance of VM-**based** **flux** estimators. However, these LPF negative effects do not contribute to the aforementioned eccentric estimated **stator** **flux** issue. In [17], it was demonstrated that the scalar product of the estimated **stator** **flux** components and the sensed phase currents can be used to form a centre point correction (CPC) method for the estimated **stator** **flux** in DTC-**based** drive systems, Fig. 6. Similar method is adopted to mitigate the influences of the eccentric estimated **flux** issue for the employed VM-**based** **flux** estimators in Fig. 5. Measurements of estimated **stator** **flux** linkage under the VM-**based** DTC ELSC scheme at low- speed operation (750rpm) and half rated torque (0.15Nm) without and incorporating the relevant compensation methods are illustrated in Fig. 7. As can be seen in Fig. 7(a), without considering the proposed compensation methods, a seriously

Show more
In DTC, the optimum **voltage** space vector for the entire switching period controls the torque and **flux** independently and the hysteresis band maintains the errors. Only one vector is applied for the entire sampling period, in the conventional method. So, for small errors, the upper or lower torque limit may be exceeded by the motor torque. Instead, the torque ripple can be reduced by using more than one vector within the sampling period. The insertion of zero vector precisely controls the slip frequency [8]. For a smaller hysteresis band, the frequency of operation of the PWM inverter could be very high. The width of the hysteresis band causes variation in the switching frequency. Direct torque control **based** on space vector modulation preserve DTC transient merits, furthermore, produce better quality steady state performance in a wide speed range. At each cycle period, SVM technique is used to obtain the reference **voltage** space vector to exactly compensate the **flux** and torque errors. The torque ripple of DTC-SVM in low speed can be significantly improved.

Show more
Induction motors are the most used in industry since they are rugged, inexpensive, and are maintenance free. It is estimated that more than 50% of the world electric en- ergy generated is consumed by electric machines. Im- proving efficiency in electric drives is important, mainly for economic saving and reduction of environmental pollution [1,2]. Induction motors have a high efficiency at rated speed and torque. However, at light loads, motor efficiency decreases dramatically due to an imbalance between the copper and the core losses. Hence, energy saving can be achieved by proper selection of the **flux** level in the motor [3,4]. The main induction motor losses are usually split into: **stator** copper losses, rotor copper losses, core (iron) losses, mechanical and stray losses. To improve the motor efficiency, the **flux** must be reduced, obtaining a balance between copper and core losses. Many minimum-loss control schemes **based** on scalar control or vector control of induction motor drives have been reported in literature [4-8]. Induction motor drive can be controlled according to a number of performance functions, such as input power, speed, torque, airgap **flux**, power factor, **stator** current, **stator** **voltage**, and overall efficiency [9]. Basically, there are three strategies, which are used in efficiency optimization of induction motor drive: Simple state control, **model** **based** control, and search control. Search strategy methods have an impor- tant advantage compared to other strategies. It is com-

Show more
Matlab/Simulink is a systems simulator and unable to direct simulate electrical circuits Therefore for simulation of electrical circuits power system block sets are used which incorporates libraries of electrical blocks and analysis tools which are used to convert electrical circuits into Simulink diagrams. The electrical blocks are electrical models such as electrical machines, current and **voltage** sources, and different electric elements, power electronic switches, connectors, and sensors for measurement purpose. When the simulation starts Simulink use the Pm Blockset and transfers the electrical circuit into a state–space representation with the initial conditions of state variables. The actual simulation starts after this initial conversion, this allows the use of a wide variety of fixed step and variable step algorithms available in Simulink. As variable time step algorithms are faster than fixed time step method because the number of steps are less so these algorithms are used for small- and medium-size systems, And for large systems containing a more number of states and/or power switches, a fixed time step algorithm is used. A Simulink scopes can be used to display the Simulation results or these results can be sent to workspace during the simulation. The variety of MATLAB functions and toolboxes are present for processing and plotting of waveforms from stored data.

Show more
Although GSC to some extent can compensate the unbalanced grid **voltage**, the torque and power pulsations still exist due to 2ωe ripple which superimposed on the dc-link **voltage**. The torque pulsation in a generator increases stress on the rotating shaft of the DFIG which can cause shaft fatigue or other mechanical damages to a WTG. Thus, a control provision is required for the rotor-side converter to mitigate the torque/power pulsations of DFIG. Santos- Martin et al. in [23] show that the simultaneous elimination of the torque and real power pulsations can not be performed under unbalanced grid **voltage** condition. Thus, the proposed control scheme herein is designed to compensate the torque and reactive power pulsations as shown in Fig. 5.

Show more
13 Read more

Seeking an electromechanical system with the shortest **flux** path and the high degree of available space utilization leads to that shown in Fig. 1. The upper and lower parts can be considered as two translating parts of the system supposing the winding and the vertical arms of the core as the stationary parts. Quasi-3D **model** of axial **flux** switched reluctance motor is resulted in by repeating each of the translating and stationary parts in a proper number. Cyclic 3D view of this motor can be seen in Fig. 2 where the 1/4 of the **stator** and rotor are shown. This is considered as the proposed axial **flux** segmental switched reluctance motor in this paper. The **flux** paths are shown in this figure at the both aligned and unaligned positions of the rotor. As mentioned, the **flux** path is as short as possible, and the **stator** can be **flux** reversal free resulting in low core losses. Geometric parameters of the motor are listed in Table 1 (refer to Fig.1). Presented formulas can be easily extracted from this figure. The parameter C is defined as the slot opening factor, and determines the rotor pole arc to rotor pole pitch ratio. Both of the **stator** and rotor are segmented, and no back iron is required for closing the **flux** path or providing a mechanical support. In fact, the **stator** modules are mounted on the shaft utilizing a special interface assembly. The connection regions for the **stator** modules are two surfaces of the module tips at the inner radius of the laminations. Rotor segments are inlayed in a solid disc with a nonmagnetic material (e.g. S316).

Show more
Induction motor mechanical faults such as broken rotor bar faults and air gap eccentricity are analyzed using motor **stator** current (Zhongming Ye et al.,2003) (Liang. B et al., 2002) (Shahin Hedayati Kia et al., 2009). Mechanical faults are responsible for more than 95% of all failures. In many applications inductions motors are driven by **voltage** source inverters (VSI). Broken rotor bar faults and air gap eccentricity in an inverter fed induction motor decreases the performance of an inverter and load (Ilias P et al., 2011). Broken rotor bar fault in an induction motor increases the copper loss and total Loss of the machine (Bashir Mahdi Ebrahimi et al., 2013) so this fault decreases the efficiency of the machine. Induction motor **stator** and rotor parameters are varied during **stator** and rotor fault. Faults in **stator** and rotor can be analyzed using **stator** current (Smail Bachir et al., 2006). Almost 40%–50% of all failures are caused by bearing fault. In early days bearing fault was analyzed **based** on vibration. The main thing to be pointed in the analysis is vibration is also caused by motor body. Modern analysis of bearing fault is predicted from the **stator** current (Lucia Frosini et al.,2010).From the survey of papers it is clear that many authors analyzed individual faults of machine. This paper presents all four faults of MVIM using **stator** current analyses.

Show more