Modeling and Simulation of Fuzzy Logic Variable Speed Drive Controller
|
|
- Helen Ramsey
- 8 years ago
- Views:
Transcription
1 Chapter 4 Modeling and Simulation of Fuzzy Logic Variable Speed Drive Controller 4.1 Introduction Fuzzy logic is an important part of artificial intelligence. In recent times, artificial intelligence techniques are becoming a significant branch in electrical engineering, predominantly in the area of power electronics and motor drives. Artificial intelligence is principally computer emulation of human thinking. The goal of AI is to imitate human intellect so that a computer can think like a human being. Looking at the complexity of human thinking process, it is possible that the computers may help to solve problems that are difficult to solve by conventional methods. Categorization of Artificial intelligence is, 1, Expert System 2, Fuzzy logic 3, Artificial Neural Network and 4, Genetic Algorithms. Fuzzy Logic is another class of AI, but its history and applications are more recent. In 1965, Lofty Zadeh, a computer scientist, propounds the hypothesis of Fuzzy Logic. He said that thinking of a human being is often fuzzy, vague, or imprecise in nature and, therefore, cannot be expressed by 1 (yes) or 0 (no) type precision as this logic is used in ES. FL mainly based on multi-valued logic between 0 & 1. In 1975, First-time fuzzy logic control was applied for steam generation plant by Manmdani and Assilian in London Queen Mary College. Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth truth values between completely true and completely false. 46
2 Fuzzy expert system: A fuzzy expert system is an expert system that uses a collection of fuzzy membership functions and rules, instead of Boolean logic, to reason about data. The rules in a fuzzy expert system are usually of a form similar to the following: If x is low and y is high then z = medium where x and y are input variables (names for know data values),z is an output variable (a name for a data value to be computed),low is a membership function (fuzzy subset) defined on x, high is a membership function defined on y, and medium is a membership function defined on z. The antecedent (the rule's premise) describes to what degree the rule applies, while the conclusion (the rule's consequent) assigns a membership function to each of one or more output variables. Most tools for working with fuzzy expert systems allow more than one conclusion per rule. The set of rules in a fuzzy expert system is known as the rule base or knowledge base. The general inference process proceeds in three (or four) steps. 1. Under FUZZIFICATION, the membership functions defined on the input variables are applied to their actual values, to determine the degree of truth for each rule premise. 2. Under INFERENCE, the truth value for the premise of each rule is computed, and applied to the conclusion part of each rule. This results in one fuzzy subset to be assigned to each output variable for each rule. Usually only MIN or PRODUCT is used as inference rules. In MIN inferencing, the output membership function is clipped off at a height corresponding to the rule premise's computed degree of truth (fuzzy logic AND). In PRODUCT inferencing, the output membership function is scaled by the rule premise's computed degree of truth. 3. Under COMPOSITION, all of the fuzzy subsets assigned to each output variable are combined together to form a single fuzzy subset for each output variable. Again, 47
3 usually MAX or SUM are used. In MAX composition, the combined output fuzzy subset is constructed by taking the point wise maximum over all of the fuzzy subsets assigned to variable by the inference rule (fuzzy logic OR). In SUM composition, the combined output fuzzy subset is constructed by taking the point wise sum over all of the fuzzy subsets assigned to the output variable by the inference rule. 4. Finally is the (optional) DEFUZZIFICATION, which is used when it is useful to convert the fuzzy output set to a crisp number. There are more defuzzification methods than we can shake a stick at (at least 30). Two of the more common techniques are the CENTROID and MAXIMUM methods. In the CENTROID method, the crisp value of the output variable is computed by finding the variable value of the center of gravity of the membership function for the fuzzy value. In the MAXIMUM method, one of the variable values at which the fuzzy subset has its maximum truth values chosen as the crisp value for the output variable. Implication methods: Mamdani type: Mamdani s fuzzy inference method is the most commonly seen fuzzy methodology. Mamdani s method was among the first control systems built using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani as an attempt to control a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators. Lusing Larson type: In this method output MF is scaled instead of being truncated. Sugeno type: The sugeno method of implication was first introduced in The difference here is that unlike the mamdani and lusing Larson methods, the output MFS are only constants or have linear relations with inputs. With a constant output MF (singleton), it is defined as zero order sugeno method, whereas with a linear relation, it is known as first order sugeno method. 48
4 Defuzzification methods: Conversion of fuzzy output to crisp output is defined as defuzzification. There are various types of defuzzification methods. Some of them are Centre of area method: In the COA method of defuzzification the crisp output z o of the z variable is taken to be the geometric centre of the output fuzzy value z area, where outzis formed by taking the union of all contribution of rules,whose DOF>0.the general expression for COA defuzzification is outz zdz With a discretized universe of discourse, the expression is out z. z0 (4.1) out z 0 n i1 n i1 z i out out z z i i (4.2) COA method is a well known method and it is often used in spite of some amount of complexity in calculation. Height method: In the height method of defuzzification, the COA method is simplified to consider only the height of each contributing MF at the midpoint of base. Mean of maximum method: The height method of defuzzification is further simplified in MOM method, where only the highest membership function in the output is considered. The general expression for MOM is Where, M Z m z0 (4.3) M m1 z m th element in the universe of discourse, where the output MF m is at the maximum value, and M = number of such elements. 49
5 4.2 Fuzzy logic control algorithm A fuzzy algorithm consists of situation and action pairs. Conditional rules expressed in IF and THEN statements are generally used. For example, the control rule might be: if the output is lower than the requirement and the output is dropping moderately then the input to the system shall be increased greatly. Such a rule has to be converted into a more generally statement for application to fuzzy algorithms. To achieve this the following terms are defined: error equals the set point minus the process output, error change equals the error from the process output minus the error from last output: and control input applied to the process. In addition, it is necessary to quantize the qualitative statements and the following linguistic sets are assigned 1. Large Positive (LP) 2. Medium Positive (MP) 3. Small Positive (SP) 4. Zero (ZZ) 5. Small Negative (SN) 6. Medium Negative (MN) 7. Large Negative (LN) Thus the statement of the example control will be: if the error is large positive and the error change is small positive then the input to the system is large positive. Fuzzy control action 1. Specify and store the minimum and the maximum ranges of the error signal E=er(k), the error change de= der(k)and the control input change df. 2. If the minimum and maximum ranges of step one are different then quantize then into a common universe of discourse using scaling factors such that the maximum and minimum of the quantized error signal E, the quantized error change de, and the quantized control input changed f are all the same. 3. Define the symmetrical linguistic fuzzy subsets of E, de, and df. 4. Calculate the error er and the error change der for the current sampling period and find their quantized values E and de respectively in the common universe of discourse. 50
6 5. From the E and de the contribution of each rule given in Table 4.2 in the fuzzy subsets of control input df and scaling of its membership grades using the rule can be found. 6. The result of application of all rules is membership function grades of control input df through the universe of its discourse. To calculate the crisp or numerical value of df the COA Criteria is used as follows: (4.4) Where n is the number of quantization levels of the output. 7. Add the control input change df(k) to the previous value F(k-l) to calculate the new control action to be taken for the kth sample: F(k)=F(k-l)+dF(k). Fuzzy logic tool box: The Fuzzy Logic Toolbox is a collection of functions built on the MATLAB numeric computing environment. It provides tools for you to create and edit fuzzy inference systems within the framework of MATLAB, or if you prefer you can integrate your fuzzy systems into simulations with Simulink, or you can even build stand-alone C programs that call on fuzzy systems you build with MATLAB. This toolbox relies heavily on graphical user interface (GUI) tools to help you accomplish your work, although you can work entirely from the command line if you prefer. There are five primary graphical tools for building, editing and observing fuzzy inference systems in the fuzzy logic tool box. They are Fuzzy inference system (Fig. 4.1) (FIS) editor Membership function (MF) editor Rule editor Rule viewer Surface viewer 51
7 Fig. 4.1 Fuzzy inference system 4.3 Design of PI Speed Controller Considerable attention has to be paid in the selection of the controller as this not only decides the type of optimization but also determines the performance and behaviour of the system. Following factors can be considered in the selection of the type of controller: 1. Accuracy; 2. Time constants in the system; 3. Response of the controller; and noise. Accuracy: The type of the controller to be chosen- depends on the accuracy required by the system. As we know that with a P-controller, there remains a permanent error in the regulating circuit which is however small and tolerable if T and hence under such circumstances a P or a PD controller, depending upon the number and magnitude of delay elements present in the system, can be used. 52
8 Time Constants Present in the System: The presence of delay elements (time constants) in the system affects its response. Thus it is advisable to cancel as many time constants as possible. Since in a system number of small and big time constants are present, attempts should be made to neutralize bigger time constants. With PIDcontroller two time constants and with PD and P1 one time constant each can be neutralized. PID-controller is especially suitable for field circuits having larger time constants. Response of the Controller: P-controller has the fastest response while a I-controller has the slowest response. Thus, under the circumstances, where T, fast response property of a P-controller can be used without the risk of permanent control error. Noise: Derivative controllers give an extremely fast response. They anticipate what is going to happen and apply corrective action. Noise which is always present in any system is intensified because of the derivative action. In thyristor converter application, output from regulating system determines the firing angle. The slightest noise will create considerable trouble and hence such type of regulators is never used in thyristor control circuit and other similar applications Different types of conventional controllers We have the different types of controllers. some of them are I-Controller: Integral control adjusts the output of the controller so that it is proportional to the integral of the error signal. The output of an integral controller is given by: Output = u(t) = k G c t i 0 s s e t dt (4.5) u ki (4.6) e s Due to the introduction of a time constant R this controller is sluggish in response as compared to a P-controller. The integral element helps the controller to have a finite output value even when its input is zero and thus it eliminates permanent control error. 53
9 Proportional controller: A P-controller has got a fast response.this is because of the absence of any time constant in the regulator circuit. The output voltage is at all times proportional to input voltage. Advantages of proportional controller: - Very fast - Simple to design - Good performance Disadvantages of proportional controller: - Performance may not be good enough - High gain may cause instability In general proportional controller is effective in reducing feedback error and tends to give good system performance. However this is not always the case. For examplemotor velocity control. Derivative controller: Like a D-controller it is impossible to get an ideal PD controller because of the presence of input and source resistances. These resistances introduce a time constant into the system which is responsible for the slow decrease of output voltage. On account of the presence of D element the input voltage and the variation has to be limited to a value so that output voltage remains within the permissible limit. Like PI controller here also proportional gain and the time constant can be chosen and the time constant can be used to compensate one of the time constants of the system. A proportional-plus derivative PD control has the transfer function: c s c k k s (4.7) p s k T S p d 1 (4.8) Where K P T D = K D and T D is the Derivative Time D Advantages of PD controller: - Allows greater damping for no change in ɷ n - Allows shorter settling time for no change in ɷ n - Rate of change of error is taken into account - Introduces positive phase (stabilization) 54
10 Disadvantages of PD controller: - Susceptible to noise PI Controller: A P1-controller contains properties of both P- and I-controller and as such finds large application. Presence of I element makes the permanent control deviation Zero, and the proportional element helps in faster response. The proportional gain and the time constant can both be chosen to meet system requirements and thus it offers better adaptability as compared to P- or I-controller. A PI control has the transfer function of form C S K I K P (4.9) S Where K P / T I = K I, and T I = Integral/Reset Advantages of PI control: - High accuracy achieved - Steady-state error eliminated for step input Disadvantages of PI control - Introduces negative phase-changes C.L.T.F to higher order - Need to design for two controller parameters The basic block diagram of PI controller is shown in Fig reference signal + + error signal K p + K i /s C(t) Fig. 4.2 Basic block diagram of PI controller In continuous time domain a PI controller output represented by C t K rt K rt p i (4.10) The reference torque is generated by speed error processed through the PI controller, t t Te ref K pe Ki e (4.11) 55
11 e t Where ref r (4.12) In a digital computer system, the speed sampling is in discrete time and so a discrete time model is desired taking the sampling interval to be t T eref n Teref n KP esn esn i K e t 1 (4.13) i s n1 Similarly the reference source current is generated by the dc link voltage error which is processed another PI controller PID controller: This controller consists of all the three elements and thus offers possibility to choose proportional gain and two time constants present in the controller. The two time constants of the controller can be used to compensate two time constants of the system. Like D and PD controllers, it is also impossible in a PID controller to get ideal D performance. A PID controller can be used to reduce the time constant and thus is preferable when thyristor converter is to be used for field control. 4.4 Comparisons between PI and fuzzy logic speed controllers A standard approach for speed control in industrial drives is to use a PI controller. Recent developments in artificial intelligence based control have brought into focus a possibility of replacing a PI speed controller with a fuzzy logic (FL) equivalent. Fuzzy logic speed control is sometimes seen as the ultimate solution for high performance drives of the next generation. Operation of a drive with speed control by PI and FL techniques has been compared on a number of occasions. The comparison results show that summarized as follows: The only transient in which FL speed control is superior to PI speed control in all the operating conditions is the load rejection transient. However, it has to be noted that even this is valid only if the controllers are designed for zero overshoot. If a small 56
12 overshoot is allowed in the design, load rejection capability of the PI speed control may be better than with FL control at certain speed settings. Robustness of the drive to an increase in inertia is universally claimed as one of the main advantages of the FL speed control. Experimental results show that this is not necessarily the case. The robustness depends on the setting of the speed reference. Speed response to small reference speed change depends on how close the speed responses of the two controllers are for the design point (rated step speed reference with rated inertia here). PI speed controller has much better response to all the reference speed changes, primarily because its response for the design point was slightly slower. Compared to FL, PI speed controller shows better results with reversing transients. 4.5 Simulation Results A block diagram of an indirect-vector-controlled (IVC) induction motor drive is shown in Fig. 4.4 and PI controller is shown in Fig Incorporating the proposed fuzzy speed controller, the feedback speed control loop generates the active or torque current command iqs*, as indicated. The vector rotator receives the torque and excitation current commands iqs* and ids*, respectively. The induction motor is fed by a hysteresis current-controlled pulse width modulated (PWM) inverter. The motor currents are decomposed into i d and i q, components which are respectively flux and torque components in the d-q reference frame rotating with the stator frequency The inputs to the fuzzy logic speed controller have been selected as the speed error and its time variation. The output of the FLC is the variation of command current. The two inputs variables Where, e k and Ce k e, are calculated at every sampling instant as k r k k r s (4.14) k ek ek 1 ce (4.15) k the reference is speed and k r r is the actual rotor speed. 57
13 FLC consists of three stages Fuzzification, rule execution, and defuzzification. In the first stage, the crisp variables e k and k Ce converted into fuzzy variables e and Ce. Membership functions associated to the control variables have been chosen with triangular shapes. The universes of discourse of the input variables e and Ce are established after many simulations as (-180, 180) rad/s and ( ) rad/s respectively. The universe of discourse of the output variable ci is (-2, 2). Each universe of discourse is divided in to seven fuzzy sets: NL (Negative Large), NM (Negative Medium), NS (Negative Small), ZE (Zero), PL (Positive Large), PM (Positive Medium), PS (Positive Small). Each fuzzy variable is a member of the subsets with a degree of membership varying between 0 (non-member) and 1 (full member). In the second stage of the FLC, the variables e and ce are processed by an inference engine that executes 49 rules (7x7). These rules are established using the knowledge of the system behaviour and the experience of the control engineers. Each rule is expressed in the form as in the following example: IF e is Negative Medium) AND ( ce is Positive Small) THEN ( ci is Negative Small). Different inference methods can be used to produce the fuzzy set values for the output fuzzy variable ci, in this paper, the Max-Product inference qs qs qs method is used to calculate the final fuzzy value ci of the output. qs In the defuzzification stage, a crisp value of the output variable ci qs (k) is obtained by using the centre of area defuzzification method. The reference current computed by integrating i qs k that is applied to the vector control system is ci qs i qs (k) k i k ci k 1 (4.16) qs The control performance of the described FLC is evaluated by Simulink. A trapezoidal trajectory for the speed reference has been selected, which is characterized by an initial constant acceleration period, a constant speed period, and finally a constant deceleration. qs 58
14 The various components of the proposed simulation project 1. Three phase IGBT fed Squirrel cage Induction motor. 2. Indirect vector control block 3. Fuzzy controller block 4. PI controller block Fig. 4.3 Main circuit diagram of PI speed controller 59
15 Teta Id* Iq* Iabc* Iabc Iabc* Pulses In Mean Scope7 Scope1 Discrete, Ts = 2e-006 s. Step VDC + - i + - v Product1 Scope2 g + A B - C IGBT Inverter Scope3 Tm A m B C Induction Motor m is_abc is_qd v s_qd wm Te Demux Scope13 Scope6 v z Signal 3 Signal Builder Scope9 Scope Phir* Phir* Id* Iq Teta Phir wm 1 z Phir Id 1 z 0.4 Gain1 1 Gain2 Fuzzy Logic Controller with Ruleviewer 1 s Integrator Scope5 Scope4 Scope15 Phir -K- Te* Iq* 1 z Gain4 Fig. 4.4 Main circuit diagram of proposed fuzzy logic speed controller Three phase IGBT fed Squirrel cage Induction motor In this thesis a 3-phase hysteresis current controlled pulse width modulated inverter is used and it is used to control a 3-phase squirrel cage induction motor of parameters 2.2KVA, 50H Z (Table 4.1). On the basis of the parameters inverter ratings are selected. It is basically a 4-pole motor with synchronous speed of 1500 rpm. The rated speed of the motor as per its characteristics is 1470 rpm. 60
16 Table 4.1 parameters of three phase induction motor used Rotor Type Squirrel cage Reference frame Synchronous Nominal Power 2.2 KVA Voltage Line to Line 208 V rms Frequency 50 Hz Stator resistance 0.59 ohm Stator inductance H Rotor resistance 0.37 ohm Rotor inductance H Mutual inductance H Inertia Friction factor Number of poles Indirect vector control block The induction motor is fed by a current-controlled PWM inverter, which operates as a three-phase sinusoidal current source. The motor speed is compared to the reference * and the error is processed by the speed controller to produce a torque command Te*. The stator quadrature-axis current reference i qs * is calculated from torque reference T e * as i qs p Lr L m T e r est (4.17) Where, Lr is the rotor inductance, L m is the mutual inductance, and r est is the estimated rotor flux linkage given by L i m ds r 1 s est r (4.18) Where, r = L r / R r is the rotor time constant. The stator direct-axis current reference i ds * is obtained from rotor flux reference input r * i ds r L m (4.19) 61
17 The rotor flux position e required for coordinates transformation is generated from the rotor speed m and slip frequency sl: e m sl dt (4.20) The slip frequency is calculated from the stator reference current i qs * and the motor parameters. Lm Rr sl iqs (4.21) L r est r The i qs * and i ds * current references are converted into phase current references i a *, i b *, i c * for the current regulators. The regulators process the measured and reference currents to produce the inverter gating signals. The role of the speed controller is to keep the motor speed equal to the speed reference input in steady state and to provide a good dynamic during transients. It can be of proportional-integral type. The current-controlled PWM inverter circuit is used. The IGBT inverter is modeled by a Universal Bridge block in which the Power Electronic device and Port configuration options are selected as IGBT/Diode and ABC as output terminals respectively. The DC link input voltage is represented by a 780 V DC voltage source the current regulator, which consists of three hysteresis controllers, is built with Simulink blocks (Fig. 4.5). The motor currents are provided by the measurement output of the Asynchronous Machine block. 1 Iabc Demux boolean NOT double boolean NOT double Mux 1 Pulses 2 Iabc* Demux boolean NOT double Fig. 4.5 Hysteresis current control block 62
18 The conversions between abc and dq reference frames are executed by the abc to dq0 transformation as shown in Fig ia 1 Teta 3 Iq* 2 Id* -Kcos(u) sin(u) Mux f(u) f(u) ib -K- ic -K- Mux 1 Iabc* Fig. 4.6 abc to dqo transformation The rotor flux is calculated by the Flux Calculation block (Fig. 4.7). 1 Phir H=1/(1+T.s) T= s Discrete Tranfer Function Lm -K- 1 Id Phir = Lm *Id / (1 +Tr.s) Lr = mh Rr = 0.37 ohms Fig. 4.7 Rotor flux calculation block The rotor flux position ( e) is calculated by the Theta Calculation block as shown in Fig Iq 2 Phir Mux Mux 61.91e-3* u[1]/(u[2]* e-3) 3 wm s Integrator 1 Teta Teta= Electrical angle= integ ( wr + wm) wr = Rotor frequency (rad/s) = Lm *Iq / ( Tr * Phir) wm= Rotor mechanical speed (rad/s) Lm = mh Lr = mh Rr= 0.37 ohms Tr = Lr / Rr = s Fig. 4.8 Theta calculation block 63
19 The stator quadrature-axis current reference (iqs*) is calculated by the iqs* Calculation block (Fig. 4.9 and Fig. 4.10). 2 Te* Mux u[1]*0.348/(u[2]+1e-3) 1 Phir Iq= ( 2/3) * (2/p) * ( Lr/Lm) * (Te / Phir) Iq= * (Te / Phir) 1 Iq* Lm = 61.91mH Lr = 64.72mH p= nb of poles = 4 Fig. 4.9 iqs* calculation block 1 Phir* -K- KF 1 Id* Id* = Phir*/ Lm Lm= mh Fig id* calculation block PI controller block: A PI controller contains properties of both p and I controller and as such finds large applications. Presence of I element makes the permanent control zero, and the proportional element helps in faster response. The inputs to the PI controller is * (reference speed) and (actual speed) as shown in Fig Fig PI controller 64
20 4.5.3 Fuzzy controller block: The inputs to the fuzzy logic speed controller are the speed error and its time variation. The output of the FLC is the variation of command current. The two inputs variables e k andce k, are calculated at every sampling instant. A membership function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1 (Fig. 4.12). The input space is sometimes referred to as the universe of discourse, a fancy name for a simple concept. The characteristic of one FLC can be more intuitive represented in the threedimensional space. Fig Membership function of the fuzzy variables ew, cew and ci qs * Table 4.2: Rule base for speed control EW NL NM NS ZE PS PM PL WEW NL NL NL NL NL NM NS ZE NM NL NL NL NM NS ZE PS NS NL NL NM NS ZE PS PM ZE NL NM NS ZE PS PM PL PS NM NS ZE PS PM PL PL PM NS ZE PS PM PL PL PL 65
21 R Fuzzy Logic Program for Development of Fuzzy Rules [System] Name='change1' Type='mamdani' Version=2.0 NumInputs=2 NumOutputs=1 NumRules=49 AndMethod='min' OrMethod='max' ImpMethod='prod' AggMethod='max' DefuzzMethod='centroid' [Input1] Name='input1' Range=[ ] NumMFs=7 MF1='NL':'trapmf',[ ] MF2='NM':'trimf',[ ] MF3='NS':'trimf',[ ] MF4='ZE':'trimf',[ ] MF5='PS':'trimf',[ ] MF6='PM':'trimf',[ ] MF7='PL':'trapmf',[ ] [Input2] Name='input2' Range=[ ] NumMFs=7 66
22 MF1='NL':'trapmf',[ ] MF2='NM':'trimf',[ ] MF3='NS':'trimf',[ ] MF4='ZE':'trimf',[ ] MF5='PS':'trimf',[ ] MF6='PM':'trimf',[ ] MF7='PL':'trapmf',[ ] [Output1] Name='output1' Range=[-2 2] NumMFs=7 MF1='NL':'trapmf',[ ] MF2='NM':'trimf',[ ] MF3='NS':'trimf',[ ] MF4='ZE':'trimf',[ ] MF5='PS':'trimf',[ ] MF6='PM':'trimf',[ ] MF7='PL':'trapmf',[ ] [Rules] 1 1, 1 (1) : 1 1 2, 1 (1) : 1 1 3, 1 (1) : 1 1 4, 1 (1) : 1 1 5, 2 (1) : 1 1 6, 3 (1) : 1 1 7, 4 (1) : 1 2 1, 1 (1) : 1 2 2, 1 (1) : 1 2 3, 1 (1) : 1 67
23 2 4, 1 (1) : 1 2 5, 3 (1) : 1 2 6, 4 (1) : 1 2 7, 5 (1) : 1 3 1, 1 (1) : 1 3 2, 1 (1) : 1 3 3, 2 (1) : 1 3 4, 3 (1) : 1 3 5, 4 (1) : 1 3 6, 5 (1) : 1 3 7, 6 (1) : 1 4 1, 1 (1) : 1 4 2, 2 (1) : 1 4 3, 3 (1) : 1 4 4, 4 (1) : 1 4 5, 5 (1) : 1 4 6, 6 (1) : 1 4 7, 7 (1) : 1 5 1, 2 (1) : 1 5 2, 3 (1) : 1 5 3, 4 (1) : 1 5 4, 5 (1) : 1 5 5, 6 (1) : 1 5 6, 7 (1) : 1 5 7, 7 (1) : 1 6 1, 3 (1) : 1 6 2, 4 (1) : 1 6 3, 5 (1) : 1 6 4, 6 (1) : 1 68
24 6 5, 7 (1) : 1 6 6, 7 (1) : 1 6 7, 7 (1) : 1 7 1, 4 (1) : 1 7 2, 5 (1) : 1 7 3, 6 (1) : 1 7 4, 7 (1) : 1 7 5, 7 (1) : 1 7 6, 7 (1) : 1 7 7, 7 (1) : Fuzzy Rules 1. If (input1 is NL) and (input2 is NL) then (output1 is NL) (1) 2. If (input1 is NL) and (input2 is NM) then (output1 is NL) (1) 3. If (input1 is NL) and (input2 is NS) then (output1 is NL) (1) 4. If (input1 is NL) and (input2 is ZE) then (output1 is NL) (1) 5. If (input1 is NL) and (input2 is PS) then (output1 is NM) (1) 6. If (input1 is NL) and (input2 is PM) then (output1 is NS) (1) 7. If (input1 is NL) and (input2 is PL) then (output1 is ZE) (1) 8. If (input1 is NM) and (input2 is NL) then (output1 is NL) (1) 9. If (input1 is NM) and (input2 is NM) then (output1 is NL) (1) 10. If (input1 is NM) and (input2 is NS) then (output1 is NL) (1) 11. If (input1 is NM) and (input2 is ZE) then (output1 is NL) (1) 69
25 12. If (input1 is NM) and (input2 is PS) then (output1 is NS) (1) 13. If (input1 is NM) and (input2 is PM) then (output1 is ZE) (1) 14. If (input1 is NM) and (input2 is PL) then (output1 is PS) (1) 15. If (input1 is NS) and (input2 is NL) then (output1 is NL) (1) 16. If (input1 is NS) and (input2 is NM) then (output1 is NL) (1) 17. If (input1 is NS) and (input2 is NS) then (output1 is NM) (1) 18. If (input1 is NS) and (input2 is ZE) then (output1 is NS) (1) 19. If (input1 is NS) and (input2 is PS) then (output1 is ZE) (1) 20. If (input1 is NS) and (input2 is PM) then (output1 is PS) (1) 21. If (input1 is NS) and (input2 is PL) then (output1 is PM) (1) 22. If (input1 is ZE) and (input2 is NL) then (output1 is NL) (1) 23. If (input1 is ZE) and (input2 is NM) then (output1 is NM) (1) 24. If (input1 is ZE) and (input2 is NS) then (output1 is NS) (1) 25. If (input1 is ZE) and (input2 is ZE) then (output1 is ZE) (1) 26. If (input1 is ZE) and (input2 is PS) then (output1 is PS) (1) 27. If (input1 is ZE) and (input2 is PM) then (output1 is PM) (1) 28. If (input1 is ZE) and (input2 is PL) then (output1 is PL) (1) 29. If (input1 is PS) and (input2 is NL) then (output1 is NM) (1) 30. If (input1 is PS) and (input2 is NM) then (output1 is NS) (1) 31. If (input1 is PS) and (input2 is NS) then (output1 is ZE) (1) 70
26 32. If (input1 is PS) and (input2 is ZE) then (output1 is PS) (1) 33. If (input1 is PS) and (input2 is PS) then (output1 is PM) (1) 34. If (input1 is PS) and (input2 is PM) then (output1 is PL) (1) 35. If (input1 is PS) and (input2 is PL) then (output1 is PL) (1) 36. If (input1 is PM) and (input2 is NL) then (output1 is NS) (1) 37. If (input1 is PM) and (input2 is NM) then (output1 is ZE) (1) 38. If (input1 is PM) and (input2 is NS) then (output1 is PS) (1) 39. If (input1 is PM) and (input2 is ZE) then (output1 is PM) (1) 40. If (input1 is PM) and (input2 is PS) then (output1 is PL) (1) 41. If (input1 is PM) and (input2 is PM) then (output1 is PL) (1) 42. If (input1 is PM) and (input2 is PL) then (output1 is PL) (1) 43. If (input1 is PL) and (input2 is NL) then (output1 is ZE) (1) 44. If (input1 is PL) and (input2 is NM) then (output1 is PS) (1) 45. If (input1 is PL) and (input2 is NS) then (output1 is PM) (1) 46. If (input1 is PL) and (input2 is ZE) then (output1 is PL) (1) 47. If (input1 is PL) and (input2 is PS) then (output1 is PL) (1) 48. If (input1 is PL) and (input2 is PM) then (output1 is PL) (1) 49. If (input1 is PL) and (input2 is PL) then (output1 is PL) (1) 71
27 4.5.6 Comparison of result between PI and Fuzzy Logic controller Fig Speed of Step input without load (PI controller) Fig Speed of Step input without load (Fuzzy controller) 72
28 Fig Motor torque of Step input without load (PI controller) Fig Motor torque of Step input without load (Fuzzy controller) 73
29 Fig Current of step input without load (PI controller) Fig Current of step input without load (Fuzzy controller) 74
30 Fig Error in speed of step input without load (PI controller) Fig Error in speed of step input without load (fuzzy controller) 75
31 Fig Speed of step input with load (PI controller) Fig Speed of step input with load (fuzzy controller) 76
32 Fig Motor torque of step input with load (PI controller) Fig Motor torque of step input with load (Fuzzy controller) 77
33 Fig Current of step input with load (PI controller) Fig Current of step input with load (Fuzzy controller) 78
34 Fig Error in speed of step input with load (PI controller) Fig Error in speed of step input with load (Fuzzy controller) 79
35 Fig Speed of Non step without load (PI controller) Fig Speed of Non step without load (Fuzzy controller) 80
36 Fig Motor torque of non-step input without load (PI controller) Fig Motor torque of non-step input without load (Fuzzy controller) 81
37 Fig Current of non-step input without load (PI controller) Fig Current of non-step input without load (Fuzzy controller) 82
38 Fig Error of speed in non-step input without load (PI controller) Fig Error of speed in non-step input without load (Fuzzy controller) 83
39 Fig Speed of Non step input with load (PI controller) Fig Speed of Non step input with load (Fuzzy controller) 84
40 Fig Motor torque of non-step input with load (PI controller) Fig Motor torque of non-step input with load (Fuzzy controller) 85
41 Fig Current of non-step input with load (PI controller) Fig Current of non-step input with load (Fuzzy controller) 86
42 Fig Error of speed of non-step input with load (PI controller) Fig Error of speed of non-step input with load (Fuzzy controller) 87
43 4.6 Conclusion A fuzzy logic controller based indirect vector control of induction motor has been presented. Fuzzy logic controller has been designed for speed control loop. The simulation has been carried out using Simulink Fuzzy Logic toolbox user guide (Fig to 4.44). In order to minimize the real-time computational burden simple membership functions and rules have been used (Table 4.2). Since exact system parameters are not required in the implementation of the proposed controller, the performance of the drive system is robust, stable and sensitive to parameters and operating condition variations. In order to prove the superiority of the FLC, a conventional PI controller based IM drive system has also been simulated and the performance has been investigated at different dynamic operating conditions. It is concluded that the proposed fuzzy logic controller has shown superior performances over the PI controller and form the wave form of the stator current, we can say that under all the conditions the fuzzy logic controller optimize the stator current that is input to the motor drive and give considerable saving in energy required. 88
Simulation of VSI-Fed Variable Speed Drive Using PI-Fuzzy based SVM-DTC Technique
Simulation of VSI-Fed Variable Speed Drive Using PI-Fuzzy based SVM-DTC Technique B.Hemanth Kumar 1, Dr.G.V.Marutheshwar 2 PG Student,EEE S.V. College of Engineering Tirupati Senior Professor,EEE dept.
More informationSPEED CONTROL OF INDUCTION MACHINE WITH REDUCTION IN TORQUE RIPPLE USING ROBUST SPACE-VECTOR MODULATION DTC SCHEME
International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 7, Issue 2, March-April 2016, pp. 78 90, Article ID: IJARET_07_02_008 Available online at http://www.iaeme.com/ijaret/issues.asp?jtype=ijaret&vtype=7&itype=2
More informationOnline Tuning of Artificial Neural Networks for Induction Motor Control
Online Tuning of Artificial Neural Networks for Induction Motor Control A THESIS Submitted by RAMA KRISHNA MAYIRI (M060156EE) In partial fulfillment of the requirements for the award of the Degree of MASTER
More informationModelling, Simulation and Performance Analysis of A Variable Frequency Drive in Speed Control Of Induction Motor
International Journal of Engineering Inventions e-issn: 78-7461, p-issn: 319-6491 Volume 3, Issue 5 (December 013) PP: 36-41 Modelling, Simulation and Performance Analysis of A Variable Frequency Drive
More informationINDUCTION MOTOR PERFORMANCE TESTING WITH AN INVERTER POWER SUPPLY, PART 2
INDUCTION MOTOR PERFORMANCE TESTING WITH AN INVERTER POWER SUPPLY, PART 2 By: R.C. Zowarka T.J. Hotz J.R. Uglum H.E. Jordan 13th Electromagnetic Launch Technology Symposium, Potsdam (Berlin), Germany,
More informationHow to Turn an AC Induction Motor Into a DC Motor (A Matter of Perspective) Steve Bowling Application Segments Engineer Microchip Technology, Inc.
1 How to Turn an AC Induction Motor Into a DC Motor (A Matter of Perspective) Steve Bowling Application Segments Engineer Microchip Technology, Inc. The territory of high-performance motor control has
More informationSimulation and Analysis of PWM Inverter Fed Induction Motor Drive
Simulation and Analysis of PWM Inverter Fed Induction Motor Drive C.S.Sharma, Tali Nagwani Abstract Sinusoidal Pulse Width Modulation variable speed drives are increasingly applied in many new industrial
More informationDynamic Simulation of Induction Motor Drive using Neuro Controller
Int. J. on Recent Trends in Engineering and Technology, Vol. 1, No. 2, Jan 214 Dynamic Simulation of Induction Motor Drive using Neuro Controller P. M. Menghal 1, A. Jaya Laxmi 2, N.Mukhesh 3 1 Faculty
More informationSimulation and Analysis of Parameter Identification Techniques for Induction Motor Drive
International Journal of Electronic and Electrical Engineering. ISSN 0974-2174 Volume 7, Number 10 (2014), pp. 1027-1035 International Research Publication House http://www.irphouse.com Simulation and
More informationEE 402 RECITATION #13 REPORT
MIDDLE EAST TECHNICAL UNIVERSITY EE 402 RECITATION #13 REPORT LEAD-LAG COMPENSATOR DESIGN F. Kağan İPEK Utku KIRAN Ç. Berkan Şahin 5/16/2013 Contents INTRODUCTION... 3 MODELLING... 3 OBTAINING PTF of OPEN
More informationABSTRACT. Keyword double rotary inverted pendulum, fuzzy logic controller, nonlinear system, LQR, MATLAB software 1 PREFACE
DESIGN OF FUZZY LOGIC CONTROLLER FOR DOUBLE ROTARY INVERTED PENDULUM Dyah Arini, Dr.-Ing. Ir. Yul Y. Nazaruddin, M.Sc.DIC, Dr. Ir. M. Rohmanuddin, MT. Physics Engineering Department Institut Teknologi
More informationA Fuzzy-Based Speed Control of DC Motor Using Combined Armature Voltage and Field Current
3rd IFAC International Conference on Intelligent Control and Automation Science. A Fuzzy-Based Speed Control of DC Motor Using Combined Armature Voltage and Field Current A. A. Sadiq* G. A. Bakare* E.
More informationSTATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC
STATOR FLUX OPTIMIZATION ON DIRECT TORQUE CONTROL WITH FUZZY LOGIC Fatih Korkmaz1, M. Faruk Çakır1, Yılmaz Korkmaz2, İsmail Topaloğlu1 1 Technical and Business Collage, Çankırı Karatekin University, 18200,
More informationPrinciples of Adjustable Frequency Drives
What is an Adjustable Frequency Drive? An adjustable frequency drive is a system for controlling the speed of an AC motor by controlling the frequency of the power supplied to the motor. A basic adjustable
More informationMathematical Modeling and Dynamic Simulation of a Class of Drive Systems with Permanent Magnet Synchronous Motors
Applied and Computational Mechanics 3 (2009) 331 338 Mathematical Modeling and Dynamic Simulation of a Class of Drive Systems with Permanent Magnet Synchronous Motors M. Mikhov a, a Faculty of Automatics,
More informationUSE OF ARNO CONVERTER AND MOTOR-GENERATOR SET TO CONVERT A SINGLE-PHASE AC SUPPLY TO A THREE-PHASE AC FOR CONTROLLING THE SPEED OF A THREE-PHASE INDUCTION MOTOR BY USING A THREE-PHASE TO THREE-PHASE CYCLOCONVERTER
International Journal of Electrical Engineering & Technology (IJEET) Volume 7, Issue 2, March-April, 2016, pp.19-28, Article ID: IJEET_07_02_003 Available online at http:// http://www.iaeme.com/ijeet/issues.asp?jtype=ijeet&vtype=7&itype=2
More informationTransient analysis of integrated solar/diesel hybrid power system using MATLAB Simulink
Transient analysis of integrated solar/diesel hybrid power system using ATLAB Simulink Takyin Taky Chan School of Electrical Engineering Victoria University PO Box 14428 C, elbourne 81, Australia. Taky.Chan@vu.edu.au
More informationHITACHI INVERTER SJ/L100/300 SERIES PID CONTROL USERS GUIDE
HITACHI INVERTER SJ/L1/3 SERIES PID CONTROL USERS GUIDE After reading this manual, keep it for future reference Hitachi America, Ltd. HAL1PID CONTENTS 1. OVERVIEW 3 2. PID CONTROL ON SJ1/L1 INVERTERS 3
More informationAnalysis of Space Vector Pulse Width Modulation VSI Induction Motor on various conditions
Analysis of Space Vector Pulse Width Modulation VSI Induction Motor on various conditions Padma Chaturvedi 1, Amarish Dubey 2 1 Department of Electrical Engineering, Maharana Pratap Engineering College,
More informationPower Electronics. Prof. K. Gopakumar. Centre for Electronics Design and Technology. Indian Institute of Science, Bangalore.
Power Electronics Prof. K. Gopakumar Centre for Electronics Design and Technology Indian Institute of Science, Bangalore Lecture - 1 Electric Drive Today, we will start with the topic on industrial drive
More informationSpeed Control Methods of Various Types of Speed Control Motors. Kazuya SHIRAHATA
Speed Control Methods of Various Types of Speed Control Motors Kazuya SHIRAHATA Oriental Motor Co., Ltd. offers a wide variety of speed control motors. Our speed control motor packages include the motor,
More informationSaumil Navalbhai Patel B.E., Gujarat University, India, 2007 PROJECT. Submitted in partial satisfaction of the requirements for the degree of
POWER LOAD BALANCING USING FUZZY LOGIC Saumil Navalbhai Patel B.E., Gujarat University, India, 2007 PROJECT Submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in
More informationIntroduction to Fuzzy Control
Introduction to Fuzzy Control Marcelo Godoy Simoes Colorado School of Mines Engineering Division 1610 Illinois Street Golden, Colorado 80401-1887 USA Abstract In the last few years the applications of
More informationReal time MATLAB Interface for speed control of Induction motor drive using dspic 30F4011
Real time MATLAB Interface for speed control of Induction motor drive using dspic 30F4011 R. Arulmozhiyal Senior Lecturer, Sona College of Technology, Salem, TamilNadu, India. K. Baskaran Assistant Professor,
More informationDCMS DC MOTOR SYSTEM User Manual
DCMS DC MOTOR SYSTEM User Manual release 1.3 March 3, 2011 Disclaimer The developers of the DC Motor System (hardware and software) have used their best efforts in the development. The developers make
More informationFuzzy Adaptive PI Controller for Direct Torque Control Algorithm Based Permanent Magnet Synchronous Motor
Website: www.ijetae.com (ISSN 225-2459, ISO 91:28 Certified Journal, Volume 3, Issue 5, May 213) Adaptive PI Controller for Direct Torque Control Algorithm Based Permanent Magnet Synchronous Motor R.Senthil
More informationEDUMECH Mechatronic Instructional Systems. Ball on Beam System
EDUMECH Mechatronic Instructional Systems Ball on Beam System Product of Shandor Motion Systems Written by Robert Hirsch Ph.D. 998-9 All Rights Reserved. 999 Shandor Motion Systems, Ball on Beam Instructional
More informationWIND TURBINE TECHNOLOGY
Module 2.2-2 WIND TURBINE TECHNOLOGY Electrical System Gerhard J. Gerdes Workshop on Renewable Energies November 14-25, 2005 Nadi, Republic of the Fiji Islands Contents Module 2.2 Types of generator systems
More informationOptimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR
International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:5, No:, 20 Optimized Fuzzy Control by Particle Swarm Optimization Technique for Control of CSTR Saeed
More information8 Speed control of Induction Machines
8 Speed control of Induction Machines We have seen the speed torque characteristic of the machine. In the stable region of operation in the motoring mode, the curve is rather steep and goes from zero torque
More informationFUZZY Based PID Controller for Speed Control of D.C. Motor Using LabVIEW
FUZZY Based PID Controller for Speed Control of D.C. Motor Using LabVIEW SALIM, JYOTI OHRI Department of Electrical Engineering National Institute of Technology Kurukshetra INDIA salimnitk@gmail.com ohrijyoti@rediffmail.com
More informationAC Induction Motor Slip What It Is And How To Minimize It
AC Induction Motor Slip What It Is And How To Minimize It Mauri Peltola, ABB Oy, Helsinki, Finland The alternating current (AC) induction motor is often referred to as the workhorse of the industry because
More informationDrivetech, Inc. Innovations in Motor Control, Drives, and Power Electronics
Drivetech, Inc. Innovations in Motor Control, Drives, and Power Electronics Dal Y. Ohm, Ph.D. - President 25492 Carrington Drive, South Riding, Virginia 20152 Ph: (703) 327-2797 Fax: (703) 327-2747 ohm@drivetechinc.com
More informationMICRO HYDRO POWER PLANT WITH INDUCTION GENERATOR SUPPLYING SINGLE PHASE LOADS
MICRO HYDRO POWER PLANT WITH INDUCTION GENERATOR SUPPLYING SINGLE PHASE LOADS C.P. ION 1 C. MARINESCU 1 Abstract: This paper presents a new method to supply single-phase loads using a three-phase induction
More information300 MW Variable Speed Drives for Pump-Storage Plant Application Goldisthal
May 24 MW Variable Speed Drives for Aurélie Bocquel APCG / 4BOC4 (MW-Goldisthal 1-5-24).PPT MW Variable Speed Drives for Content Major benefits of the cyclo-converter driven doubly-fed induction machines
More informationAdvance Electronic Load Controller for Micro Hydro Power Plant
Journal of Energy and Power Engineering 8 (2014) 1802-1810 D DAVID PUBLISHING Advance Electronic Load Controller for Micro Hydro Power Plant Dipesh Shrestha, Ankit Babu Rajbanshi, Kushal Shrestha and Indraman
More informationFREQUENCY CONTROLLED AC MOTOR DRIVE
FREQUENCY CONTROLLED AC MOTOR DRIVE 1.0 Features of Standard AC Motors The squirrel cage induction motor is the electrical motor motor type most widely used in industry. This leading position results mainly
More informationMathematical Modelling of PMSM Vector Control System Based on SVPWM with PI Controller Using MATLAB
Mathematical Modelling of PMSM Vector Control System Based on SVPWM with PI Controller Using MATLAB Kiran Boby 1, Prof.Acy M Kottalil 2, N.P.Ananthamoorthy 3 Assistant professor, Dept of EEE, M.A College
More informationCurrent Loop Tuning Procedure. Servo Drive Current Loop Tuning Procedure (intended for Analog input PWM output servo drives) General Procedure AN-015
Servo Drive Current Loop Tuning Procedure (intended for Analog input PWM output servo drives) The standard tuning values used in ADVANCED Motion Controls drives are conservative and work well in over 90%
More informationStabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller
Stabilizing a Gimbal Platform using Self-Tuning Fuzzy PID Controller Nourallah Ghaeminezhad Collage Of Automation Engineering Nuaa Nanjing China Wang Daobo Collage Of Automation Engineering Nuaa Nanjing
More informationTechnical Guide No. 100. High Performance Drives -- speed and torque regulation
Technical Guide No. 100 High Performance Drives -- speed and torque regulation Process Regulator Speed Regulator Torque Regulator Process Technical Guide: The illustrations, charts and examples given in
More informationA FUZZY LOGIC APPROACH FOR SALES FORECASTING
A FUZZY LOGIC APPROACH FOR SALES FORECASTING ABSTRACT Sales forecasting proved to be very important in marketing where managers need to learn from historical data. Many methods have become available for
More informationDually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current
Summary Dually Fed Permanent Magnet Synchronous Generator Condition Monitoring Using Stator Current Joachim Härsjö, Massimo Bongiorno and Ola Carlson Chalmers University of Technology Energi och Miljö,
More informationCONVENTIONALLY reduced order models are being
Co-Simulation of an Electric Traction Drive Christoph Schulte and Joachim Böcker Abstract For the simulation of electrical drives, reducedorder models or simple look-up tables are often used in order to
More informationActive Vibration Isolation of an Unbalanced Machine Spindle
UCRL-CONF-206108 Active Vibration Isolation of an Unbalanced Machine Spindle D. J. Hopkins, P. Geraghty August 18, 2004 American Society of Precision Engineering Annual Conference Orlando, FL, United States
More informationApplications of Fuzzy Logic in Control Design
MATLAB TECHNICAL COMPUTING BRIEF Applications of Fuzzy Logic in Control Design ABSTRACT Fuzzy logic can make control engineering easier for many types of tasks. It can also add control where it was previously
More informationTime Response Analysis of DC Motor using Armature Control Method and Its Performance Improvement using PID Controller
Available online www.ejaet.com European Journal of Advances in Engineering and Technology, 5, (6): 56-6 Research Article ISSN: 394-658X Time Response Analysis of DC Motor using Armature Control Method
More informationSIMULATION AND SPEED CONTROL OF INDUCTION MOTOR DRIVES
SIMULATION AND SPEED CONTROL OF INDUCTION MOTOR DRIVES A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor of Technology In ELECTRICAL ENGINEERING By AMITPAL SINGH I.S.
More informationdspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor
dspace DSP DS-1104 based State Observer Design for Position Control of DC Servo Motor Jaswandi Sawant, Divyesh Ginoya Department of Instrumentation and control, College of Engineering, Pune. ABSTRACT This
More informationBrushless DC Motor Speed Control using both PI Controller and Fuzzy PI Controller
Brushless DC Motor Speed Control using both PI Controller and Fuzzy PI Controller Ahmed M. Ahmed MSc Student at Computers and Systems Engineering Mohamed S. Elksasy Assist. Prof at Computers and Systems
More informationNew Pulse Width Modulation Technique for Three Phase Induction Motor Drive Umesha K L, Sri Harsha J, Capt. L. Sanjeev Kumar
New Pulse Width Modulation Technique for Three Phase Induction Motor Drive Umesha K L, Sri Harsha J, Capt. L. Sanjeev Kumar Abstract In this paper, various types of speed control methods for the three
More informationAvailable online at www.sciencedirect.com Available online at www.sciencedirect.com
Available online at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Procedia Engineering Engineering () 9 () 6 Procedia Engineering www.elsevier.com/locate/procedia International
More informationLeran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk
BMAS 2005 VHDL-AMS based genetic optimization of a fuzzy logic controller for automotive active suspension systems Leran Wang and Tom Kazmierski {lw04r,tjk}@ecs.soton.ac.uk Outline Introduction and system
More informationElectric motor emulator versus rotating test rig
DEVELOPMENT E l e c t r i c m o t o r s Electric motor emulator versus rotating test rig A controversial issue among experts is whether real-time model-based electric motor emulation can replace a conventional
More informationOutline Servo Control
Outline Servo Control Servo-Motor Drivers Control Modes orque Capability Servo-control Systems Direct/Indirect Control System Control Algorithm Implementation Controller Design by Emulation Discretization
More informationChapter 9: Controller design
Chapter 9. Controller Design 9.1. Introduction 9.2. Effect of negative feedback on the network transfer functions 9.2.1. Feedback reduces the transfer function from disturbances to the output 9.2.2. Feedback
More informationSimulation of Electric Drives using the Machines Library and the SmartElectricDrives Library
Simulation of Electric Drives using the Machines Library and the SmartElectricDrives Library J.V. Gragger, H. Giuliani, H. Kapeller, T. Bäuml arsenal research, Vienna 04.09.2006 1 Contents Chapter 1: The
More informationTwinCAT NC Configuration
TwinCAT NC Configuration NC Tasks The NC-System (Numeric Control) has 2 tasks 1 is the SVB task and the SAF task. The SVB task is the setpoint generator and generates the velocity and position control
More informationClosed Loop PWM Control for Induction Motor Drive Using Dual Output Three Phase Inverter
Closed Loop PWM Control for Induction Motor Drive Using Dual Output Three Phase Inverter Archana.P 1, Karthick.R 2 Pg Scholar [PED], Department of EEE, CSI College of Engineering, Ketti, Tamilnadu, India
More informationThe Use of Hybrid Regulator in Design of Control Systems
World Applied Sciences Journal 23 (10): 1291-1297, 2013 ISSN 1818-4952 IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.23.10.13144 The Use of Hybrid Regulator in Design of Control Systems Vladimir
More informationReliable World Class Insights Your Silicon Valley Partner in Simulation ANSYS Sales, Consulting, Training & Support
www.ozeninc.com info@ozeninc.com (408) 732 4665 1210 E Arques Ave St 207 Sunnyvale, CA 94085 Reliable World Class Insights Your Silicon Valley Partner in Simulation ANSYS Sales, Consulting, Training &
More informationPulse Width Modulated (PWM) Drives. AC Drives Using PWM Techniques
Drives AC Drives Using PWM Techniques Power Conversion Unit The block diagram below shows the power conversion unit in Pulse Width Modulated (PWM) drives. In this type of drive, a diode bridge rectifier
More informationInduction Motor Theory
PDHonline Course E176 (3 PDH) Induction Motor Theory Instructor: Jerry R. Bednarczyk, P.E. 2012 PDH Online PDH Center 5272 Meadow Estates Drive Fairfax, VA 22030-6658 Phone & Fax: 703-988-0088 www.pdhonline.org
More informationPulse Width Modulated (PWM)
Control Technologies Manual PWM AC Drives Revision 1.0 Pulse Width Modulated (PWM) Figure 1.8 shows a block diagram of the power conversion unit in a PWM drive. In this type of drive, a diode bridge rectifier
More informationUnit 33 Three-Phase Motors
Unit 33 Three-Phase Motors Objectives: Discuss the operation of wound rotor motors. Discuss the operation of selsyn motors. Discuss the operation of synchronous motors. Determine the direction of rotation
More informationSYNCHRONOUS MACHINES
SYNCHRONOUS MACHINES The geometry of a synchronous machine is quite similar to that of the induction machine. The stator core and windings of a three-phase synchronous machine are practically identical
More informationJAVA FUZZY LOGIC TOOLBOX FOR INDUSTRIAL PROCESS CONTROL
JAVA FUZZY LOGIC TOOLBOX FOR INDUSTRIAL PROCESS CONTROL Bruno Sielly J. Costa, Clauber G. Bezerra, Luiz Affonso H. G. de Oliveira Instituto Federal de Educação Ciência e Tecnologia do Rio Grande do Norte
More informationAMZ-FX Guitar effects. (2007) Mosfet Body Diodes. http://www.muzique.com/news/mosfet-body-diodes/. Accessed 22/12/09.
Pulse width modulation Pulse width modulation is a pulsed DC square wave, commonly used to control the on-off switching of a silicon controlled rectifier via the gate. There are many types of SCR s, most
More informationControl of a Three Phase Induction Motor using Single Phase Supply
Control of a Three Phase Induction Motor using Single Phase Supply G. R. Sreehitha #1, A. Krishna Teja *2, Kondenti. P. Prasad Rao #3 Department of Electrical & Electronics Engineering, K L University,
More informationTHIS paper reports some results of a research, which aims to investigate the
FACTA UNIVERSITATIS (NIŠ) SER.: ELEC. ENERG. vol. 22, no. 2, August 2009, 227-234 Determination of Rotor Slot Number of an Induction Motor Using an External Search Coil Ozan Keysan and H. Bülent Ertan
More informationE x p e r i m e n t 5 DC Motor Speed Control
E x p e r i m e n t 5 DC Motor Speed Control IT IS PREFERED that students ANSWER THE QUESTION/S BEFORE DOING THE LAB BECAUSE THAT provides THE BACKGROUND information needed for THIS LAB. (0% of the grade
More informationMODELLING AND SIMULATION OF SVPWM INVERTER FED PERMANENT MAGNET BRUSHLESS DC MOTOR DRIVE
MODELLING AND SIMULATION OF SVPWM INVERTER FED PERMANENT MAGNET BRUSHLESS DC MOTOR DRIVE Devisree Sasi 1, Jisha Kuruvilla P Final Year M.Tech, Dept. of EEE, Mar Athanasius College of Engineering, Kothamangalam,
More informationThree phase circuits
Three phase circuits THREE PHASE CIRCUITS THREE-PHASE ADVANTAGES 1. The horsepower rating of three-phase motors and the kva rating of three-phase transformers are 150% greater than single-phase motors
More informationMotor Fundamentals. DC Motor
Motor Fundamentals Before we can examine the function of a drive, we must understand the basic operation of the motor. It is used to convert the electrical energy, supplied by the controller, to mechanical
More informationController Design in Frequency Domain
ECSE 4440 Control System Engineering Fall 2001 Project 3 Controller Design in Frequency Domain TA 1. Abstract 2. Introduction 3. Controller design in Frequency domain 4. Experiment 5. Colclusion 1. Abstract
More informationUnderstanding Power Impedance Supply for Optimum Decoupling
Introduction Noise in power supplies is not only caused by the power supply itself, but also the load s interaction with the power supply (i.e. dynamic loads, switching, etc.). To lower load induced noise,
More informationTORQUE RIPPLES MINIMIZATION ON DTC CONTROLLED INDUCTION MOTOR WITH ADAPTIVE BANDWIDTH APPROACH
TORQUE RIPPLES MINIMIZATION ON DTC CONTROLLED INDUCTION MOTOR WITH ADAPTIVE BANDWIDTH APPROACH Fatih Korkmaz 1,Yılmaz Korkmaz 2,İsmail Topaloğlu 1 and Hayati Mamur 1 ABSTRACT 1 Department of Electric and
More informationIntroduction. Three-phase induction motors are the most common and frequently encountered machines in industry
Induction Motors Introduction Three-phase induction motors are the most common and frequently encountered machines in industry - simple design, rugged, low-price, easy maintenance - wide range of power
More informationApplication Information
Moog Components Group manufactures a comprehensive line of brush-type and brushless motors, as well as brushless controllers. The purpose of this document is to provide a guide for the selection and application
More informationImplementation of Fuzzy and PID Controller to Water Level System using LabView
Implementation of Fuzzy and PID Controller to Water Level System using LabView Laith Abed Sabri, Ph.D University of Baghdad AL-Khwarizmi college of Engineering Hussein Ahmed AL-Mshat University of Baghdad
More informationDevelopment of High Frequency Link Direct DC to AC Converters for Solid Oxide Fuel Cells (SOFC)
Development of High Frequency Link Direct DC to AC Converters for Solid Oxide Fuel Cells (SOFC) Dr. Prasad Enjeti Power Electronics Laboratory Department of Electrical Engineering College Station, TX -
More informationSensorless Field Oriented Control (FOC) for Permanent Magnet Synchronous Motors (PMSM)
ensorless Field Oriented Control (FOC) for Permanent Magnet ynchronous Motors (PMM) 2007 Microchip Technology Incorporated. All Rights Reserved. ensorless FOC for PMM lide 1 Welcome to the ensorless Field
More informationELECTRICAL ENGINEERING
EE ELECTRICAL ENGINEERING See beginning of Section H for abbreviations, course numbers and coding. The * denotes labs which are held on alternate weeks. A minimum grade of C is required for all prerequisite
More informationNO LOAD & BLOCK ROTOR TEST ON THREE PHASE INDUCTION MOTOR
INDEX NO. : M-142 TECHNICAL MANUAL FOR NO LOAD & BLOCK ROTOR TEST ON THREE PHASE INDUCTION MOTOR Manufactured by : PREMIER TRADING CORPORATION (An ISO 9001:2000 Certified Company) 212/1, Mansarover Civil
More informationTamura Closed Loop Hall Effect Current Sensors
Tamura Closed Loop Hall Effect Current Sensors AC, DC, & Complex Currents Galvanic Isolation Fast Response Wide Frequency Bandwidth Quality & Reliability RoHs Compliance Closed Loop Hall Effect Sensors
More informationIV. Three-Phase Induction Machines. Induction Machines
IV. Three-Phase Induction Machines Induction Machines 1 2 3 4 5 6 7 8 9 10 11 12 13 Example 1: A 480V, 60 Hz, 6-pole, three-phase, delta-connected induction motor has the following parameters: R 1 =0.461
More informationVLT AutomationDrive for Marine winch applications
MAKING MODERN LIVING POSSIBLE VLT APPLICATION NOTE VLT AutomationDrive for Marine winch applications This Application note is meant to be a guideline for using Danfoss VLT AutomationDrive in winch applications.
More informationProblems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment,
Uncertainty Problems often have a certain amount of uncertainty, possibly due to: Incompleteness of information about the environment, E.g., loss of sensory information such as vision Incorrectness in
More informationSpeed Control of field oriented induction motor using DsPIC Controller
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 3 Ver. IV (May Jun. 2014), PP 45-50 Speed Control of field oriented induction motor
More informationChapter 11 Current Programmed Control
Chapter 11 Current Programmed Control Buck converter v g i s Q 1 D 1 L i L C v R The peak transistor current replaces the duty cycle as the converter control input. Measure switch current R f i s Clock
More informationBasics of Electricity
Basics of Electricity Generator Theory PJM State & Member Training Dept. PJM 2014 8/6/2013 Objectives The student will be able to: Describe the process of electromagnetic induction Identify the major components
More informationControl System Definition
Control System Definition A control system consist of subsytems and processes (or plants) assembled for the purpose of controlling the outputs of the process. For example, a furnace produces heat as a
More informationDigital Control of Acim Using dspic
Digital Control of Acim Using dspic P.Santhosh, M.Muthazhagi, D.Karthik Assistant Professor, Karpagam College of Engineering, Coimbatore, India Assistant Professor, Karpagam College of Engineering, Coimbatore,
More informationLab 8: DC generators: shunt, series, and compounded.
Lab 8: DC generators: shunt, series, and compounded. Objective: to study the properties of DC generators under no-load and full-load conditions; to learn how to connect these generators; to obtain their
More informationReactive Power Control of an Alternator with Static Excitation System Connected to a Network
Reactive Power Control of an Alternator with Static Excitation System Connected to a Network Dr. Dhiya Ali Al-Nimma Assist. Prof. Mosul Unoversity Dr. Majid Salim Matti lecturer Mosul University Abstract
More informationDHANALAKSHMI COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING EE2302 - ELECTRICAL MACHINES II UNIT-I SYNCHRONOUS GENERATOR
1 DHANALAKSHMI COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING Constructional details Types of rotors EE2302 - ELECTRICAL MACHINES II UNIT-I SYNCHRONOUS GENERATOR PART A 1.
More informationDEVELOPMENT OF FUZZY LOGIC MODEL FOR LEADERSHIP COMPETENCIES ASSESSMENT CASE STUDY: KHOUZESTAN STEEL COMPANY
DEVELOPMENT OF FUZZY LOGIC MODEL FOR LEADERSHIP COMPETENCIES ASSESSMENT CASE STUDY: KHOUZESTAN STEEL COMPANY 1 MOHAMMAD-ALI AFSHARKAZEMI, 2 DARIUSH GHOLAMZADEH, 3 AZADEH TAHVILDAR KHAZANEH 1 Department
More informationLab 14: 3-phase alternator.
Lab 14: 3-phase alternator. Objective: to obtain the no-load saturation curve of the alternator; to determine the voltage regulation characteristic of the alternator with resistive, capacitive, and inductive
More informationSimulation of Ungrounded Shipboard Power Systems in PSpice
Simulation of Ungrounded Shipboard Power Systems in PSpice Haibo Zhang IEEE Student Member Karen L.Butler IEEE Member Power System Automation Lab Electrical Engineering Department Texas A&M University
More informationZiegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System
Ziegler-Nichols-Based Intelligent Fuzzy PID Controller Design for Antenna Tracking System Po-Kuang Chang, Jium-Ming Lin Member, IAENG, and Kun-Tai Cho Abstract This research is to augment the intelligent
More information