Due to power electronic switches, variable speed of motor drive system using various control system have been generally used in many applications, such as direct torque control.

DIRECT TORQUE CONTROL:

Efficiency and low sensitive to parameter variation it have been generally accepted in the control of motor speed widely in all industrial applications because of its technique.

Despite its importance, it has a major setback associated with it. That is the large torque and flux ripple at steady state operation of the motor. These ripples can affect the accuracy of speed consideration of motor.

Effort have been made using the space vector modulation and the multi-level inverter methods to reduce these ripples. These methods when used though, achieved some degree of success in reducing the ripples but they are difficult and costly to implement.

In this chapter, a lot of control techniques are deeply treated, the work done in reducing the torque and flux ripples using direct torque control method is highlighted. The proposed fuzzy logic with duty ratio control is equally treated in detail.

In DTC drives, the uncoupling of the torque and flux components are Source Inverter (VSI).achieved by using hysteresis comparators which compares the actual and considered values of the electromagnetic torque and stator flux. The DTC drive consists of DTC controller, torque and flux calculator, and a Voltage

2.2 Principle of direct torque control of induction motor:

In a direct torque controlled induction motor drive, it is possible to control directly the stator flux linkage (s?) or the rotor flux (r?)or the magnetizing flux (m?) and the electromagnetic torque by the selection of an optimal inverter voltage vector. The selection of the voltage vector of the voltage source inverter is made to restrict the flux and torque error within their respective flux and torque hysteresis bands and to get the fastest torque response and highest efficiency at every instant. DTC enables both quick torque response in the transient operation and reduction of the harmonic losses and acoustic noise.

2.2.1 Voltage Source Inverter

A six step voltage source inverter provides the variable frequency AC voltage input to the induction motor in DTC method. The DC supply to the inverter is provided either by a DC source like a battery, or a rectifier supplied from a three phase or single phase AC source. Fig. 2.2 shows a six step voltage source inverter. The inductor L is inserted to limit short circuit through fault current. A large electrolytic capacitor C is inserted to stiffen the DC link voltage.

The switching devices in the voltage source inverter bridge must be capable of being turned OFF and ON. Insulated gate bipolar transistors (IGBT) are used because they can offer high switching speed with enough power rating. Each IGBT has an inverse parallel-connected diode. This diode provide alternate path for the motor current after the IGBT, is turned off.

Figure 2.2 Voltage Source Inverter

Each leg of the inverter has two switches one connected to the high side (+) of the DC link and the other to the low side (-); only one of the two can be ON at any moment. When the high side gate signal is ON the phase is assigned the binary number 1, and assigned the binary number 0 when the low side gate signal is ON. Considering the combinations of status of phases a, b and c the inverter has eight switching modes(Va,Vb,Vc=000-111) V2 (000) are zero voltage vectors V0 (000) and V7 (111) where the motor terminals are short circuited and the others are nonzero voltage vectors V1 to V6

The six nonzero voltages space vectors will have the orientation, and also shows the possible dynamic locus of the stator flux, and its different variation depending on the VSI states chosen. The possible global locus is divided into six different sectors signaled by the discontinuous line. Each vector lies in the center of a sector of width named S1 to S6 according to the voltage vector it contains.

It can be seen that the inverter voltage directly force the stator flux, the required stator flux locus will be obtained by choosing the appropriate inverter switching state. Thus the stator flux linkage move in space in the direction of the stator voltage space vector at a speed that is proportional to the magnitude of the stator voltage space vector. By selecting one after another the appropriate stator voltage vector, is then possible to change the stator flux in the required method. If an increase of the torque is required then the torque is controlled by applying voltage vectors that advance the

same sector depending on the stator flux position.

Figure 2.3.Stator flux vector locus and different possible switching Voltage vectors. FD: flux decrease. FI: flux increase. TD: torque decrease.

TI: torque increase.

Table 2.1.General Selection Table for Direct Torque Control, “k” being the sector number.

Voltage vector Increase Decrease

Stator flux Vk,Vk+1, Vk-1 Vk+2,Vk-2, Vk+3

Torque Vk+1, Vk-1 Vk+2, Vk-2

This can be tabulated in the look-up Table 2.1 (Takahashi look-up table).

Finally, the DTC classical look up table is as follows:

Table 2.2 conventional DTC look up table

Flux errorD? Torque error

DT S1 S2 S3 S4 S5 S6

1 1 V2 V3 V4 V5 V6 V¬1

0 V0 V7 V0 V7 V8 V7

-1 V6 V1 V2 V3 V4 V5

0 1 V3 V4 V5 V6 V1 V2

0 V0 V7 V0 V7 V0 V7

-1 V5 V6 V1 V2 V3 V4

2.3 DTC SCHEMATIC:

Figure 2.4 Direct Torque control scheme

A schematic of Direct Torque Control is shown. As it can be seen, there are two different loops corresponding to the magnitudes of the stator flux and torque. The reference values for the flux stator modulus and the torque are compared with the actual values, and the resulting error values are supplied into the two level and three-level hysteresis blocks respectively. The outputs of the stator flux error and torque error hysteresis blocks, together with the position of the stator flux are used as inputs of the look up table. The inputs to the look up table are given in terms of 1,0,-1 depend on whether torque and flux errors within or beyond hysteresis bands and the sector number in which the flux sector presents at that particular moment. In accordance with the figure 1.2, the stator flux modulus and torque errors tend to be restricted within its respective hysteresis bands.

From the schematic of DTC it is cleared that, for the proper selection of voltage sector from lookup table, the DTC scheme require the flux and torque estimations.

2.3.1 Techniques for Quantifications of Stator Flux in DTC:

Accurate flux quantifications in Direct Torque controlled induction motor drives is necessary to ensure proper drive operation and stability. Most of the flux estimation methods proposed was based on voltage model, current model, or the combination of both. The estimation based on current model normally applied at low frequency, and stator current and rotor mechanical speed or position. In some industrial applications, the use of incremental encoder to get the speed or position of the rotor is undesirable since it reduces the robustness and reliability of the drive. It has been generally known that even though the current model has managed to remove the sensitivity to the stator resistance variation. The use of rotor parameters in the estimation introduced error at high rotor speed due to the rotor parameter variations. So in this present DTC control scheme the flux and torque are quantified by using voltage model which does not need a position sensor and the only motor parameter used is the stator resistance. (Oghanna, 2011)

2.4 INTRODUCTION OF FLC

Fuzzy logic has become one of the most successful of today’s technology for developing sophisticated control system. With it aid, complex requirement may be implemented in simply, easily and inexpensive controlling method. The application ranges from consumer products such as cameras, camcorder, washing machines and microwave ovens to industrial process control, medical instrumentation and decision support system .many decision-making and problem solving tasks are too complex to be understand quantitatively however, people succeed by using knowledge that is imprecise rather than precise. Fuzzy logic is all about the relative importance of precision. It has two different meanings. In a narrow sense, fuzzy logic is a logical system which is an extension of multi valued logic, but in wider sense fuzzy logic is synonymous with the theory of fuzzy sets. Fuzzy set theory is originally introduced by LotfiZadeh in the 1960s, resembles approximate reasoning in it use of approximate information and uncertainty to generate decisions.

Several studies shows, both in simulations and experimental results, that Fuzzy Logic control yields superior results with respect to those obtained by conventional control algorithms thus, in industrial electronics the FLC control has become an attractive solution in controlling the electrical motor drives with large parameter variations like machine tools and robots. However, the FL Controllers design and tuning process was often complex because several quantities, such as membership functions, control rules, input and output gains, etc. must be adjusted. The design process of a FLC can be simplified if some of the mentioned quantities are obtained from the parameters of a given Proportional-Integral controller (PIC)for the same application. (Lotfizabeh, 2011).

2.5 Why fuzzy logic controller (FLC)

• Fuzzy logic controller was used to design nonlinear systems in control applications. The design of conventional control system is normally based on the mathematical model. If an accurate mathematical model is available with known parameters it can be analyzed and controller can be designed for specific performances, such procedure is time consuming.

• Fuzzy logic controller has adaptive characteristics. The adaptive characteristics can achieve robust performance to system with uncertainty parameters variation and load disturbances.

The main principles of fuzzy logic controller.

The fuzzy logic system involves three steps fuzzification application of fuzzy rules and decision making and defuzzification. Fuzzification involves mapping input crisp values and decision is made based on these fuzzy rules. These fuzzy rules are applied to the fuzzified input values and fuzzy outputs are calculated in the last step, a defuzzifier coverts the fuzzy output back to the crisp values. The fuzzy controller in this thesis is designed to have three fuzzy input variables and one output variable for applying the fuzzy control to direct torque control of induction motor. There are three variable input fuzzy logic variables. The stator flux error, electromagnetic torque error, and angle of the flux in the stator.