Over many years, the induction motor drives are widely used in various applications such as paper and textile mills, steel and cement mills, robotics, wind generation system etc. This is because the induction motor is reliable, simple in construction, easy in maintenance and provides high robustness. The overview of the variable speed control methods for induction motor are shown in figure 1.
In the past, induction motors were controlled using scalar control methods. In this control, only the magnitude of control variables is varied. It is simple to implement, but gives poor and sluggish dynamic response. This scheme can control the speed of the motor satisfactorily under steady state only.
With the advent of vector control schemes in the late 1970s, the control of an induction motor is transformed similar to a separately excited DC motor by creating independent channels for flux and torque control. In vector control, both the magnitude and phase alignment of vector variables are controlled and kept valid for steady state as well as transient conditions. The vector control is of two types namely Direct Vector Control (DVC) and Indirect Vector Control (IVC). If the field angle is computed using the flux, it is termed as direct vector control. If the field angle is computed using speed and slip, it is termed as indirect vector control.
Another control method is direct torque control. This method does not require any transformation of vectors and found to have nearly comparable performance with vector control. The vector control and direct torque control methods are better option than the scalar control to obtain the desired dynamic performance. An accurate knowledge of motor flux assumes importance to ensure proper operation and stability of high-performance IM drives. The knowledge of motor flux can be obtained through measurement using flux sensors. But the measurement of flux is difficult and expensive. Hence, the flux is generally estimated. In the literature, numerous applications of flux estimator are available for both vector and direct torque-controlled IM drives. These are dealt in the following sections.
Flux Estimator Applications
The applications of flux estimator could be broadly classified as follows.
- Speed estimation in sensorless vector or direct torque-controlled IM drives
- Rotor resistance estimation in indirect vector-controlled IM drives
- Flux angle or field angle and resultant flux estimation in direct vector-controlled IM drives
- Electromagnetic torque, flux angle and resultant flux estimation in direct torque-controlled IM drives.
Application of flux estimator for speed estimation in Sensorless induction motor drives
Recently, the speed sensor-less control of IM drives whether it is a vector control or direct torque control has received great attention and is popularly used in industries. The sensorless control does not require speed sensor and hence leads to cheaper and more reliable control. The figure 2 shows the overall block diagram of the speed-sensor-less vector-controlled IM drive system of an induction motor. Generally, through a PI controller, the speed error signal is processed and the torque command is generated. Using the torque command, the corresponding reference torque producing component of stator current is generated. Using flux as the reference, corresponding reference flux producing component of stator current is generated. Using the PI controllers, the current error signals are processed and the corresponding two command voltages are generated. The two command voltages are combined to common reference voltages. The reference is used to produce the PWM pulses to trigger the voltage source inverter and control the voltage and frequency applied to the IM drive.
Figure 2: Sensorless Vector Controlled IM Drives showing the requirement of Speed Estimator
The performance of sensorless controlled IM drives to a large extent depends on the accuracy of speed estimation and require the knowledge of flux.
Application of Flux Estimator in State Synthesis equations-based Speed Estimator
The rotor speed can be estimated through state synthesis equation which is given in (1).
(1)
The state equation depends to a large extent on the accuracy of flux estimation. The voltage model and current model equations are popularly used for flux estimation. The current model equations for flux estimation are given in (2) and (3). The rotor fluxes can be calculated using the stator currents and rotor speed.
(2)
(3)
The current model cannot be used because it requires the rotor speed as one of the inputs which is highlighted in red color. Hence voltage model can only be used for flux estimation. The schematic diagram of this scheme is shown in figure 3. The voltage model equations for flux estimation are given in (4) and (5). These equations are independent of rotor speed. The rotor fluxes can be computed using stator voltages and currents. It is noted that the flux estimator assumes importance for the good performance of this speed estimator.
(4) & (5)
Where,
Fig. 3. Block Diagram of state Synthesis Equations based Speed Estimation Scheme showing the importance of Flux Estimator
Application of Flux Estimator in Model Reference Adaptive System based Speed Estimator
The Model Reference Adaptive System (MRAS) consists of a reference model (independent of the parameter to be estimated) which determines the desired states and adaptive (adjustable) model (dependent on the parameter to be estimated) which generates the estimated values of the states. The error between these states is fed to an adaptation mechanism to generate an estimated value of the rotor speed which is used to adjust the adaptive model. This process continues till the error between two outputs tends to minimum. The MRAS with rotor flux as the state variable and stator current as the state variable is popularly used now-a-days. The MRAS with rotor flux as the state variable is shown in figure 4. In rotor flux MRAS scheme, the voltage model independent of rotor speed is used as the reference model. The current model dependent on the rotor speed is used as the adaptive model. The error between two models is minimised using PI-controller, artificial intelligence-based techniques. Thus, both voltage and current model-based flux estimator is required for the good performance of speed estimator.
Fig. 4 Block Diagram of MRAS with Rotor Flux as State Variable
In the stator current based MRAS scheme, the stator current is used as the state variable. The reference model used is the actual induction motor. The stator current obtained from the reference model is independent of the rotor speed. The adaptive model equations can be derived and presented in (6) and (7).
(6) & (7)
The equation (6) and (7) depends on the flux. Here also current model cannot be used. The voltage model-based flux estimator can only be used. The block diagram of stator current based MRAS showing the importance of flux estimator is presented in figure 5. Thus, in this MRAS scheme also, flux estimator is required.
Fig. 5 Block Diagram of MRAS with Stator Current as State Variable
Application of flux estimator for Rotor Resistance estimation in Indirect Vector Controlled IM drives
The indirect field orientation utilises an inherent slip frequency and speed relation for field angle computation. The equation for field angle ( ) and slip frequency ( ) is presented in (8) and (9).
(8) & (9)
The calculation of the slip depends on the rotor resistance. Rotor resistance may vary up to 100 per cent due to rotor heating and recovering this information with a thermal model or a temperature sensor is difficult and not desirable. Hence, rotor resistance is estimated. Similar to rotor speed estimation methods, the rotor resistance estimation methods can be used and use the knowledge of flux.
Application of Flux Estimator in State Synthesis equations-based Rotor Resistance Estimator
The rotor resistance can be estimated through state synthesis equations (10).
(10)
It is obvious from the equation (10) that the rotor resistance to a large extent depends on the accuracy of flux estimation. In this application also, current model cannot be used because it depends on the rotor resistance. Hence, voltage model can only be used because it is independent of the rotor resistance. The block diagram of this scheme is presented in figure 6. The speed required is obtained through speed sensor. Thus, flux estimator plays a vital role in this rotor resistance estimation scheme.
Fig. 6. Block Diagram of state Synthesis based Rotor Resistance Estimation Scheme showing the importance of Flux Estimator
Application of Flux Estimator in Model Reference Adaptive System based Speed Estimator
For rotor resistance estimation, the MRAS with rotor flux as the state variable is the popular scheme. The block diagram of MRAS with rotor flux as the state variable for rotor resistance estimation is shown in figure 7. Both the voltage and current model are used. The voltage model is used as the reference model as it is independent of rotor resistance.
Fig. 7. Block Diagram of rotor flux-MRAS based rotor resistance estimator showing the importance of Flux Estimator
The current model is used as the adaptive model as it is dependent on the rotor resistance. The speed required is obtained through speed sensor. The error between the reference and adaptive model is minimized using adaptive mechanism. Thus, in this scheme also, both voltage and current model-based flux estimator is required for proper operation of MRAS.
Application of flux estimator for Field Angle and Resultant Flux Estimation in direct Vector Controlled IM drives
The block diagram for the DVC IM drive is shown in the figure 8. Generally, through a PI controller, the speed error signal is processed and the torque command is generated. Using the torque command, the reference torque producing component of stator current is generated. Using the PI controller, the flux error signal is processed and the flux command is generated. Using the flux command, the reference flux producing component of stator current is generated. The current error signals are processed through PI controllers and the corresponding two command voltages are generated. The two command voltages are combined to common reference voltages. The reference is used to produce the PWM pulses to trigger the voltage source inverter and control the voltage and frequency applied to the IM drive. The principal vector control parameters ids* and iqs*, which are dc values in synchronously rotating frame, are converted to stationary frame as phase current commands for the inverter with the help of a field angle. The field angle & resultant flux can be computed using equation (11) and (12) through the flux components Ψdrs and Ψqrs.
(11)
(12)
The performance of DVC IM drive to a large extent depends on the accuracy of field angle and resultant flux and in turn depends on the flux estimation as it is clear from equation (11) and (12). In this application, either voltage or current model-based flux estimator can be used. To make direct vector control speed sensorless, then current model cannot be used. Only voltage model can be used as it is discussed earlier in the section 2.1
Fig. 8 Direct Vector Controlled IM Drive showing the Requirement for Flux Estimator to compute Field Angle and resultant flux
Application of flux estimator for Electromagnetic Torque, Field Angle and Resultant Flux Estimation in direct Torque Controlled IM drives
The block diagram of Direct Torque Control (DTC) is shown in figure 9. The direct torque requires the knowledge of electromagnetic torque, field angle and resultant flux for speed control. These parameters can be computed using (13), (14) and (15) respectively. It is again obvious that these equations depend on the flux. In this application, either voltage or current model can be used. But for speed sensorless DTC drives, the current model cannot be used. Only voltage can be used.
Fig. 9 Direct Torque Controlled IM Drive showing the Requirement for Flux Estimator to compute torque, Field Angle, resultant flux
Thus through this review, it is understood that the flux estimator is required in almost all the applications in high performance drives. The type of flux estimator used for various applications in high performance induction motor drives is tabulated in the Table I.
CONCLUSION
The detailed survey of flux estimator applications for the high-performance Induction motor drives is carried out and presented. From the study, it is understood that the flux estimator finds enormous applications in both vector and direct torque-controlled IM drives and it plays vital role in various applications of high-performance drives. Thus, the study and addressing the issues involved in the design of flux estimator for various applications in high performance IM drives is still challenging, emerging area and open area of research.
