The theory of field orientation is based on the fact that the three phase supply current inside the motor results in a magnetic field and a torque as depicted in Figure 3.20. Any change in the motor supply (ia, ib and ic) results in change the magnetic field and torque.
The functional block C represents the transformation from stator co-ordinates to field- orientated co-ordinates that occurs inside the motor [87]. Hence, FOC is implemented by compensating for this transformation as shown in Figure 3.21.
Figure 3.20 The orientation inside the ACIM [59, 89].
In a FOC drive, the three phase supply quantities are transformed into two dimensional d-q quantities. The d-q variables are then adjusted based on their reference values and fed through the modulator to the ACIM. The electromagnet torque is regulated by adjusting the q-axis current component, while the motor field is controlled by regulating the d-axis current component [83, 87].
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FOC drives are classified, in terms of flux measurement, into direct and indirect drives. In the direct scheme, the rotor flux and position are directly measured using a sensor located within the motor windings. While in the indirect scheme drives, the rotor flux and position are mathematically calculated from the power supply measurements [89]. Indirect FOC drives can be closed loop vector control where the information about the rotor angle is determined using a pulse encoder. A contrasting approach, known as a Sensorless (encoderless) FOC, does not employ any position or speed sensors. This research focuses on the sensorless type detailed in the next section.
Figure 3.21 Compensating for the coordinate transformation to achieve vector control [59, 89]
Sensorless FOCDrives
Sensorless FOC drives control the IMs speed without any speed feedback devices. Motor model and parameters together with supply measurements are utilised to control
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High motor torque at low output frequencies Automatic slip compensation
Improved dynamic response to demand and load variations Compactness with less maintenance
Simpler application with no need for wires Avoidance of the cost of encoders
Suitability for different environments, including temperature.
The d-q axis voltage components that are required to achieve the magnetising current
demanded for particular load are calculated from motor parameters, e.g. resistances, inductances and name plate data. The motor model is also used to form the right equations for the calculations. The motor equivalent circuit is shown in Figure 3.22 [87, 91].
Figure 3.22 ACIM equivalent circuit, redrawn from [93]
The analysis of the motor stator circuit based on the equivalent circuit in Figure 3.22 can be simplified as shown in figure 3.23.
50 The stator voltage given [87, 91] from:
= � − � (3.9)
= � + + � (3.10)
Based on Equations 3.9 and 3.10, if the d current component is aligned with the rotor
flux, the q-axis of the rotor flux component will be zero, and hence the relations
between flux, torque and current can be represented as follows [92]:
= + � (3.11)
= � (3.12)
Equation 3.11 shows that the ids controls the flux quantity, while equation 3.12 shows
that the q-axis current iqs can be used as a measure of load torque for slip compensation
and electromagnetic torque control [93]. Therefore, to achieve improved dynamic torque performance, the field current component ids is kept constant at a value that
maintains a maximum flux. The torque control is then performed by adjusting the iqs
[87, 91].
To keep a constant flux, the must be relative to the supply frequency for speeds up
to the rated value. While the drive voltage limit restricts the maximum voltage that can
be fed to the motor. Above the rated speed, is kept constant so that the flux falls
with increasing frequency to give flux-weakening operation. This is to ensure that both drive and motor voltage limits are not exceeded and to provide the maximum available torque even at or higher than the rated motor frequency [91, 94, 95]. The VSD scheme is represented in Figure 3.24 [87]. This also shows that the drive can act on the supply voltages based on the changes in the reference values of current components.
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Figure 3.24 A schematic diagram of VSD scheme, redrawn from [87].
Sensorless FOC drive simultaneously estimates rotor speed and flux rather than measuring them. Significant developments in this field have resulted in the availability of several techniques for speed and flux estimation. These approaches imply the terminal measurements only for speed estimation [91]. Among them, Model Reference Adaptive System (MRAS) is one of the most frequently used schemes in industrial applications [85, 87, 94, 96]. The MRAS approach is simple to apply and implies less computational effort compared to other approaches [96]. It is also represented here as it is used in the test rig drive upon which this study is based.
MRAS utilises two independent machine models for estimating the same state variable. These are referred to as the reference model, which lacks incorporation of the estimated variable, and the adjustable model containing the estimated variable. Comparison between the two models outputs generates an error signal that is treated by an adaptation mechanism, normally a PI controller, and used to modify the adjustable model [94, 96]. Figure 3.25 represents an MRAS speed estimator based on voltage (reference model) and current (adjustable or adaptive) model.
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Figure 3.25 Schematic of MRAS and speed loop in a Sensorless drive, redrawn from [87, 96]
Both reference and adaptive models are derived from IM motor equations and its equivalent circuit represented in Figure 3.22 [94; 58] in the stationary reference frame. The reference model is composed of the stator voltage equations as follows [97, 98]:
=
m − � − �
(3.13)
=
− � − � (3.14)
The adjustable (adaptive) model is represented as follows [97, 98]:
� = ( � − − ̂ ) (3.15) � = ( � − + ̂ ) (3.16) σ = − (3.17)
The error between the two models is calculated in the following way [96]:
= − (3.18)
The error is treated by the adaptation mechanism producing an output that is used for adjusting the adaptive model. The most common method used for the adaptation mechanism is the PI controller, although certain other techniques are used such as fuzzy and neural network control systems. The output from the PI controller replaces the estimated value in the adjustable model. The MRAS continually modifies the estimated variable quantity maintaining the error between the two models [96, 99].
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The estimated speed is then used to calculate the rotor flux angle θe as follows:
� = ∫ 3.19
The estimated speed from MRAS is fed into the speed loop for speed control as illustrated in Figure 3.25. The speed loop then compares the estimated signal with the reference and utilises a PI controller to process the error signal. This generates an output represented as a reference torque current component iq*which is sent to the current
control loop. The current loop sets the reference for the quadrature voltage which is adjusted by controlling the switching of the PWM [96, 99]. A block diagram of a general sensorless flux vector control drive is shown in Figure 3.26.
Figure 3.26 A block diagram of a general FOCdrive, adapted from [77]