Induction Motor Drive Using Fuzzy Logic - CiteSeerX

Proceedings of the 7th WSEAS International Conference on Systems Theory and Scientific Computation, Athens, Greece, August 24-26, 2007

Induction Motor Drive Using Fuzzy Logic

SHAHRAM JAVADI Islamic Azad University –Central Tehran Branch Electrical Engineering Department Moshanir Power Electric Company Thermal Power Plant Division IRAN Email: [email protected]

Abstract: Variable speed drives are growing and varying. Drives expanse depend on progress in different part of science like power system, microelectronic, control methods, and so on. Artificial intelligent contains hard computation and soft computation. Artificial intelligent has found high application in most nonlinear systems same as motors drive. Because artificial intelligent techniques can use as controller for any system without requirement to system mathematical model, it has been used in electrical drive control. With this manner, efficiency and reliability of drives increase and volume, weight and cost of them decrease. Due to the improved operating characteristics they give to the equipment control, electronic motor soft starters are increasingly widely applied. Escalators, pumps, elevators and conveyor belts all operate more effectively if they are soft started. However, it is not simply the ergonomics of an airport, process plant or shopping mall that are improved by soft starters. A major factor in the growth in popularity of soft starters is the reduced wear and tear that they place on motors and their associated drive systems. In turn, this reduces maintenance, conserves energy and plays a significant part in improving plant performance and operating costs. Keywords: Power Plant, Fuzzy Logic, Induction Motor, Soft starter

I. INTRODUCTION An important factor in industrial progress during the past five decades has been the increasing sophistication of factory automation which has improved productivity manifold. Manufacturing lines typically involve a variety of variable speed motor drives which serve to power conveyor belts, black start of power plants, robot arms, overhead cranes, steel process lines, paper mills, and plastic and fiber processing lines to name only a few. Prior to the 1950s all such applications required the use of a DC motor drive since AC motors were not capable of smoothly varying speed since they inherently operated synchronously or nearly synchronously with the frequency of electrical input. To a large extent, these applications are now serviced by what can be called general-purpose AC drives. In general, such AC drives often feature a cost advantage over their DC counterparts and, in addition, offer

lower maintenance, smaller motor size, and improved reliability. However, the control flexibility available with these drives is limited and their application is, in the main, restricted to fan, pump, and compressor types of applications where the speed need be regulated only roughly and where transient response and low-speed performance are not critical. More demanding drives used in machine tools, spindles, high-speed elevators, dynamometers, mine winders, rolling mills, glass float lines, and the like have much more sophisticated requirements and must afford the flexibility to allow for regulation of a number of variables, such as speed, position, acceleration, and torque. Such high-performance applications typically require a high speed holding accuracy and fast transient response. Until recently, such drives were almost exclusively the domain of DC motors combined with various

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configurations of AC-to-DC converters depending upon the application. With suitable control, however, induction motors have been shown to be more than a match for DC drives in high-performance applications. While control of the induction machine is considerably more complicated than its DC motor counterpart, with continual advancement of microelectronics, these control complexities have essentially been overcome. Such that power electronic equipment which is used widely in motor drives is IGBTs which is shown in figure 1.

Figure1. IGBT power electronic element

Although induction motors drives have already overtaken DC drives during the next decade it is still too early to determine if DC drives will eventually be relegated to the history book. However, the future decade will surely witness a continued increase in the use of AC motor drives for all variable speed applications.

II. Speed Control AC motor drives can be broadly categorized into two types, thyristor based and transistor based drives. Thyristor posses the capability of self turn-on by means of an associated gate signal but must rely upon circuit conditions to turn off whereas transistor devices are capable of both turn-on and turn-off. Because of their turn-off limitations, thyristor based drives must utilize an alternating EMF to provide switching of the devices (commutation) which requires reactive volt-amperes from the EMF source to accomplish. Induction motors are practically fixed speed devices. There are practically only two methods to change the rotation speed of AC induction motor: use frequency converter or use motor with separate winding for different speeds. In some applications motors with dual speed winging are

used. The applications where accurate speed control is needed, you need a frequency converter. A frequency converter can run a three phase AC motor at very wide speed range quite well (the performance of motor is usually reduced outside its optimal operation speed). There are variable frequency drives that allow induction motors to run on different speeds. But on those applications mechanical load and the speed range must be considered, because on those applications motors can get very hot very fast. The problem is that a 60 Hz (or 50 Hz) motor does not have enough iron in it to allow efficient 25 Hz operation. The motor will run hot due to not having enough inductive reactance at the reduced frequency. Dropping down to 10 Hz would make it even worse. A motor designed for variable frequency drive has more iron. Also, it might use a different iron/steel alloy to allow efficient operation at higher frequencies (say 400 Hz). With a light mechanical load and a good motor combined with a good variable frequency drive controller, it's sometimes possible to get a reasonable speed range using a variable frequency inverter. A good variable frequency drive device controls both frequency and voltage. The better ones even take into account that at very low speeds the resistance of the coils cannot be neglected. In VFD (variable frequency drive) system the incoming single phase power is rectified and filtered, and three-phase power is generated from the DC rail using three half-bridges. You get to set the frequency over a range so you can vary the speed of your motor, plus a nice digital display etc. It's a bit harder on the motor insulation than just running it from the line, but well-designed motors should be okay. The reason why VFD is harder for motor insulation is that the inductance in the wiring to the motor allows spikes and ringing at the motor itself. The waveforms that go from VFD to motor are typically quite far from ideal sine wave. Frequency converter does not work with AC induction motors that are run from single phase power source, because the operation of the needed motor phase conversion capacitor is very frequency sensitive (works as expected only at normal mains frequency). There are variable frequency drive devices that take in single phase power but can output three-

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phase power to the three phases motor. The problem in this kind of single phase to three phases VFD is that single phase high power to dc conversion is much more expensive than three phase high power to dc conversion. Filter capacitors have to be much larger with lower ESR. Also, you will need at least three times the current on a single phase line (3 phase power of same amps per line carries about 1.73 times of power of the same amps on single phase line). The momentary power requirements when motor spins up are much larger. A brief list of the available drive types is given in Figure2. The drives are categorized according to switching nature (natural or force commutated), converter type and motor type. Naturally commutated devices require external voltage across the power terminals (anode-cathode) to accomplish turn-off of the switch whereas a force commutated device uses a low power gate or base voltage signal which initiates a turn-off mechanism in the switch itself. In this figure the category of transistor based drives is intended to also include other hard switched turn-off devices such as GTOs, MCTs and IGCTs which are, in reality, avalanche turn-on (four-layer) devices. The numerous drive types associated with each category is clearly extensive and cannot be treated in complete detail here. However, the speed control of the four major drive types having differing control principles will be considered namely 1) Voltage controlled induction motor drives 2) Load commutated synchronous motor drives 3) Volts per hertz and vector controlled induction motor drives 4) Vector controlled permanent magnet motor drives.

Figure2. Various Motor Drive Types

A Schematic block diagram of a transistor based controller of an induction motor is shown in figure3.

Figure3. A transistor based induction motor drive

III. Soft Starting In today’s market motors or used across a wide section of the commercial sector. It could be from standard squirrel cage motors in a typical boiler room situation or alternatively it could be a variable speed motor requiring the use of an inverter to ramp the run cycle up and down. Similarly soft starts are becoming more apparent throughout the industry. The current drawn by a three-phase motor at start up is several times more than its rated operating current. This can vary from 3 to 15 times depending on the characteristics of the motor and is typically at least a factor of 7 more than the operating current. In addition, problems associated with torque surge are encountered when a motor is started direct. Extra stress on the gearbox, couplings, belt drives and other parts can soon lead to wear and even failure. To overcome the problems associated with current and torque surges, designers have developed different systems over the years. These can be categorized as follows: Direct-online starting; Star delta starting; Frequency conversion; and Solid state, stepless control, or soft starting. Direct-on-line starting is common up to 7.5kW. However for higher currents, some form of start-up reduction is required. Traditionally, the star delta method of reducing the start-up voltage electro-mechanically has proved popular. The technique, so called because the motor windings are switched from a star connection to a delta connection, reduces both start-up current and torque by about two thirds. However, at the point where switching occurs there is still a current surge that can be as high as those experienced in DOL starting.

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The alternative method of frequency conversion takes the AC voltage, converts it to DC and then to a start-up voltage of any desired frequency. However this can be both complicated and expensive. Soft start systems however, offer an excellent alternative at low cost and complexity. Soft start devices provide stepless motor control, allowing both the start-up torque and current to be adjusted in small increments. Not only can these parameters be controlled; soft starters can also vary the time taken to run the motor up to its normal operating speed. The devices operate by gradually increasing the voltage to the motor during the run-up period, this being done by phase control of the input AC voltage. The starting current is reduced in proportion to the reduction in voltage, the starting torque by the square of the reduction. Soft starting is now finding application in control situations as diverse as water treatment plants and crisp factories. The benefits of longer motor life, reduced maintenance and improved torque control mean that this type of starter is now being used in many areas where inverters would have previously been considered the only option.

unable to maintain satisfactory performance under these conditions. Recently, there has been observed an increasing interest in combining artificial intelligent control tools with classical control techniques. The principal motivations for such a hybrid implementation is that with fuzzy logic and/or neural networks issues such as uncertainty or unknown variations in plant parameters and structure can be dealt with more effectively, hence improving the robustness of the control system. Conventional controls have on their side well-established theoretical backgrounds on stability and allow different design objectives such as steady state and transient characteristics of the closed loop system to be specified. Several works contributed to the design of such hybrid control schemes (Cao et al., 1996; Chen and Chang, 1998; Shaw and Doyle, 1997). In this paper both control methods (Classical PID controller and Fuzzy Control base) are introduced and applied to an indirect fieldoriented induction motor. In the first design approach a classical PID controller is introduced to apply to an induction motor in order to control its speed and also starting situation is investigated. See figure 4 in bellow.

IV. Control Principle AC motors, particularly the squirrel-cage induction motor (SCIM), enjoy several inherent advantages like simplicity, reliability, low cost and virtually maintenance-free electrical drives. However, for high dynamic performance industrial applications, their control remains a challenging problem because they exhibit significant non-linearity and many of the parameters, mainly the rotor resistance, vary with the operating conditions. Field orientation control (FOC) or vector control (Vas, 1990) of an induction machine achieves decoupled torque and flux dynamics leading to independent control of the torque and flux as for a separately excited DC motor. FOC methods are attractive but suffer from one major disadvantage: they are sensitive to motor parameter variations such as the rotor time constant and an incorrect flux measurement or estimation at low speeds (Trzynadlowski, 1994). Consequently, performance deteriorates and a conventional controller such as a PID is

Figure 4. PID Controller

In the second design approach the basic fuzzy logic controller (FLC), regarded as a kind of variable structure controller (VSC) for which stability and robustness are well established is developed. This follows the interpretation of

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linguistic IF–THEN rules as a set of controller structures that are switched according to the process states. Figure 5 shows a typical FLC.

3) Fuzzy logic control mathematics and software are simple to develop and flexible for each modification.

V. Vector Control Drive:

Figure 5. Fuzzy Logic Controller

The mathematical technique called fuzzy logic offers a new approach to improving ASD voltage/frequency/current control. Fuzzy logic has evolved from an esoteric branch of mathematics into a useful engineering tool. By virtue of its adaptability, it can be applied to problems whose nonlinearity and dynamic nature makes them intractable to solution via classical control methods. Motor control has all of the attributes of this class of problems. Fuzzy logic has been implemented in this development of improved motor control because: 1) Fuzzy logic overcomes the mathematical difficulties of modeling highly non-linear systems. 2) Fuzzy logic responds in a more stable fashion to imprecise readings of feedback control parameters, such as the dc link current and voltage.

Although the large majority of variable speed applications require only speed control in which the torque response is only of secondary interest, more challenging applications such a traction applications, servomotors and the like depend critically upon the ability of the drive to provide a prescribed torque whereupon the speed becomes the variable of secondary interest. The method of torque control in ac machines is called either vector control or, alternatively field orientation. Vector control refers to the manipulation of terminal currents, flux linkages and voltages to affect the motor torque while field orientation refers to the manipulation of the field quantities within the motor itself. Since it is common for machine designers to visualize motor torque production in terms of the air gap flux densities and MMFs instead of currents and fluxes which relate to terminal quantities, it is useful to begin first with a discussion of the relationship between the two viewpoints. A complete vector controlled induction motor signal diagram is shown in figure 6. The feedback speed control loop generates the active or torque current command iqs*’. The vector rotator receives the torque and excitation current commands Iqs* and ids* from one of the two positions of a switch: the transient position (1) or the steadystate position (2). The fuzzy controller becomes effective at Steady state condition; i.e., when the speed loop error, dωr approaches zero.

Figure 6. PI Vector Control Induction motor diagram

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A feed-forward pulsating torque compensator has been developed to prevent speed ripple and mechanical resonance during transient operation. As the excitation current is reduced in adaptive steps by the fuzzy controller, the rotor flux decreases exponentially. The decrease of flux causes loss of torque, which normally is compensated for slowly by the speed control loop. Efficiency optimization control is effective only at steady-state conditions. A disadvantage of this control mode is that the transient response becomes sluggish. For any change in load torque or speed command, fast transient response capability of the drive can be restored by establishing the rated flux.

fuzzy logic controller and if it is selected improper, it may be we don't get optimum result or even it leads to instability.

VI. Fuzzy Logic: Three interactive efficiency-optimizing (input power minimizing) controllers have been developed for Type 1 ASDs. These controllers are 1) voltage perturbation for input power minimization, 2) speed correction, and 3) slip compensation. The voltage perturbation controller is based on changes in input power and stator voltage. Fuzzy logic control has been emphasized for voltage perturbation. The fuzzy logic membership functions for both inputs and the output are partitioned using seven MF for inputs and nine MF for output fuzzy sets and are shown in figure 7. The input variables are: E = ω r* − ω dE ∆E CE = = dt ∆t TS The output variable is: DU = ∆Te* = K1E + K 2CE Triangular fuzzy sets are used for both inputs and outputs, with a restriction that the output fuzzy sets must be isosceles to simplify defuzzification. Input and output values are represented linguistically (i.e., NM=negative medium, NS=negative small, ZE=zero, PS=positive small, and PM=positive medium). As it is shown in figure7, both inputs and output are normalized between -1 and 1. So it is necessary to define proper gains for all parameters, i.e. ke , kde and kdu in order to change parameters in per unit. Selecting these gains is one of challenging part of

Figure 7. Fuzzy Membership functions for 2 inputs (e & de) and output (du)

The rule base table can be read according to the following example: IF ERROR (E) is ZERO (Z) AND CHANGE IN ERROR (CE) is NEGATIVE SMALL (NS), THEN OUTPUT (DU) is NEGATIVE SMALL (NS). FAM table of such rules is brought in table 1: Table1: FAM Table for Fuzzy Speed Controller e ce NB NM

NB

NM

NS

Z

NB

NB

NB

NM

NB

NB

NM

NS

PS

PM

PB

NS

NVS

Z

NVS

Z

PVS

NS

NB

NM

NS

NVS

Z

PVS

PS

Z

NM

NS

NVS

Z

PVS

PS

PM

PS

NS

NVS

Z

PVS

PS

PM

PB

PM

NVS

Z

PVS

PS

PM

PB

PB

PB

Z

PVS

PS

PM

PB

PB

PB

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Speed correction control is needed because the perturbation approach alters motor speed and output power. The motor's output rotor speed should be maintained as constant as possible. The input/output mapping of the FLC is shown in figure 8.

Figure 8. Crisp input/output map

VII. Simulation In this paper two case studies have been studied. In Both simulations, it is used simulink and powersym toolboxes of MATLAB software. In the first case study, a 50 HP induction motor is started and controlled by a PID controller. 3 phase voltages and currents are measured and plotted in the first 3 seconds of its action. Also acceleration curve and output torque are investigated. All graphs are shown in figure 9. In the second case, the same motor is started and controlled by a Fuzzy Logic Based controller. The results are shown in figure 10. As it is shown the outputs are improved regarding to magnitude of starting currents and also time response of acceleration. For example amplitude of current with a classic PID controller is about 500 A during startup while with fuzzy logic controller this value reduced to 200 A.

Figure 9. Output Variable of a classic controlled induction Motor from Top to Bottom: Voltage, Current, Rotor Speed, Output Torque

Figure 10. Output Variable of a Fuzzy Logic Controlled induction Motor from Top to Bottom: Voltage, Current, Rotor Speed, Output Torque

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VIII. Conclusion

IX. Acknowledgment

An AC induction motor can consume more energy than it actually needs to perform its work, particularly when operated at less than full load conditions. This excess energy is given off by the motor in the form of heat. Idling, cyclic, lightly loaded or oversized motors consume more power than required even when they are not working. With a fuzzy logic controller we can control the amplitude of starting current and also save more energy during this time. In addition the cost and complexity of controller is reduced when it is designed by fuzzy method, because it does not need the exact model of system. The complete schematic of controller is shown in figure 11.

The author thanks MOSHANIR, the most important consultant company in electrical engineering in IRAN for using real data of one of its projects and also Islamic AZAD University for whole helps.

References [1] Bimal Bose, Power Electronics and Motor Drives, Elsevier, Academic Press, 2006 [2] M. AZZEDINE DENAI, S. "AHMED ATTIA, FUZZY AND NEURAL CONTROL OF AN INDUCTION MOTOR"

Int. J. Appl. Math. Computer. Sci., 2002, Vol.12. [3] John G. Cleland and M. Wayne Turner, "Fuzzy Logic Control of Electric Motors and Motor Drives Feasibility Study", United States Air and Energy Engineering Environmental Protection Research Laboratory Agency Research Triangle Park, [4] J. X. Shen, Z. Q. Zhu, and D. Howe, "Hybrid PI and Fuzzy Logic Speed Control of PM Brushless AC Drives", EPE 2001 – Graz [5] Andreas Dannenberg, "Fuzzy Logic Motor Control with MSP430x14x", Texas Instruments, Application Report, SLAA235–February 2005. [6] Y. Miloud, A. Draou, "Performance Analysis of a Fuzzy Logic Based Rotor Resistance Estimator of an Indirect Vector Controlled Induction Motor Drive", Turk J Elce Engine, VL 13, NO. 2, 2005

Figure 11. Top: Complete system Bottom: Fuzzy Logic Controller

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