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 statorflux 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  is the implementation of the proposed current model-based and voltagemodel-basedstatorflux estimators requires similar machine parameter information as that in the healthy case  except that the leakage inductance is additionally demanded for the proposed voltagemodel- basedstatorflux estimator. Thus, the proposed voltagemodel- basedstatorfluxestimation 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 voltagemodel-based estimator does lead to a significant magnitude imbalance between the α- and β- components of the estimated statorflux linkages. As a result, phase currents under the proposed voltagemodel-based DTC become seriously distorted. To solve this problem, a compensation scheme is proposed and verified by experimental results.
The rotor fluxbased MRAS speed estimator, developed by Schauder in , 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 . 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], statorvoltage 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).
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 fluxmodel 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 statorvoltage 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:
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 , the application of FPGA in DTC of IM drive is presented. In , 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 statorflux must be accurate. The calculation of the electromagnetic torque too, depends on the accuracy of statorfluxestimation. Most of the statorflux calculation is based on voltagemodel, current model or the combination of both models. These models require a precise
An approach for conventional DTC fluxestimation is based on the voltagemodel 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 fluxestimation 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 . Solutions using speed-adaptive SMOs are proposed in [12, 13]. These observers use sliding mode surfaces that combine the stator- current errors within the fluxestimation. 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
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 statorflux 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.
various performance aspects (i.e., stator current, electromagnetic torque, statorflux, 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
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 statorflux. The three-stage streams are compelled to stay adjust by straightforwardly controlling the statorflux. 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.
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
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 statorvoltage of induction motor, the ratio of statorvoltage 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 statorvoltage derivatives. Simulation is carried out in MATLAB/SIMULINK environment and results are compared for speed control of induction motor.
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).
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 . 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 - and thus, improving the performance of VM-basedflux estimators. However, these LPF negative effects do not contribute to the aforementioned eccentric estimated statorflux issue. In , it was demonstrated that the scalar product of the estimated statorflux components and the sensed phase currents can be used to form a centre point correction (CPC) method for the estimated statorflux 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-basedflux estimators in Fig. 5. Measurements of estimated statorflux 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
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 . 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.
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, statorvoltage, and overall efficiency . Basically, there are three strategies, which are used in efficiency optimization of induction motor drive: Simple state control, modelbased control, and search control. Search strategy methods have an impor- tant advantage compared to other strategies. It is com-
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.
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  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.
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).
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.