AI based design of a fuzzy logic scheme for speed control of induction motors using SVPWM technique

Transcription

1 74 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January 009 AI base esign of a fuzzy logic scheme for spee control of inuction motors using SPWM technique Ashok Kusagur, Dr. S. F. Koa, Dr. B. Sankar Ram Research Scholar, EEE Dept., JNTU, Hyeraba-85, Anhra Praesh, Inia, Professor & HOD, Dept. of EEE, Aurora Engg College, Bhongir , Professor, Dept. of EEE, JNTUCE, Kukatpally, Hyaraba-85, Summary The reliability an performance of the AC rives epens on the progress in power electronics, microelectronics, control methos, artificial intelligent techniques an so on. Fuzzy Logic Concept (FLC), one of the Artificial Intelligent methos has foun high applications in most of the nonlinear systems like the electric motor rives. FLC can be use as controller for any system without requirement of the system mathematical moel unlike that of the conventional electrical rive control, which uses the mathematical moel. Due to the usage of the FLC concept, the efficiency, reliability & performance of the AC rives increases. In view of the previously mentione concepts, this paper presents a rule-base fuzzy logic controller scheme esigne an applie for the spee control of an inuction motor by using the Space ector Pulse With Moulation (SPWM) technique. The close loop spee control of the inuction motor using the above technique, thus provies a reasonable egree of accuracy. Instantaneous space voltage vectors applie by the inverter have reunancy characteristics, which provie some flexibility for selecting the inverter switching moes. Simulink base block moel of inuction motor rive is use for the simulation purposes & its performance is evaluate for the spee control. The inverter uty cycle can also be calculate using the space vector PWM techniques. The propose metho improves the ynamic performance of the inuction machine compare to the conventional spee control of inuction motor rives & has got a faster response time. The simulation results presente in this paper show the effectiveness of the propose metho, which has got a wie number of avantages. Key wors : Artificial Intelligence, Electric rive, Inuction machine, Space vector pulse with moulation, Fuzzy logic control, Torque, Spee, Simulink moel. 1. Introuction Inuction motors are wiely use in various inustries as prime workhorses to prouce rotational motions an forces. Generally, variable-spee rives for inuction motors require both wie operating range of spee an fast torque response, regarless of loa variations. Usually, the classical control is use in majority of the electrical motor rives. Conventional control makes use of the mathematical moel for the controlling system. When there are system parameters variation or environmental isturbance, behavior of system is not satisfactory []. In aition, usual computation of system mathematical moel is ifficult or impossible. The esign an tuning of conventional controller increases the implementation cost an as aitional complexity in the control system & thus, may reuce the reliability of the control system. Hence, the fuzzy base techniques are use to overcome this kin of problems. motors are controllable than AC motors but the implementation cost require is more. In aition, motors has got higher volume an weight compare to the AC motors. Inuction motors (one type of AC motors) require low maintenance an are robust, have many applications in inustry. Usually, the classical control use in motors rive esign an implementation has many ifficulties, which can be liste as follows. It is on the basis of the mathematical accurate moel of system, that usual it is not known. Drives are nonlinear systems an classical control performance with this system ecreases. ariation of machine parameters by loa isturbance, motor saturation or thermal variations o not cause expectation performance. With the chosen coefficients, classical control cannot receive acceptable results []. oltage source inverter-fe inuction motors are most preferre for variable spee rive applications. The controller choice for a SPWM rive is etermine by the requirements of the type of application & is the most successful technique use in meeting the above requirements. Due to avances in power electronics an microprocessors, variable-spee rives for inuction motors are commonly use nowaays. The SPWM control has been wiely use in many applications, such as AC servos, electric vehicle rive systems an so on. Manuscript receive January 5, 009 Manuscript revise January 0, 009

2 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January Using this type of control, a highly couple, nonlinear, multivariable inuction motor can be simply controlle through linear inepenent ecouple control of the torque an flux, similar to separately excite motors [3]. SPWM metho is an avance, computationintensive PWM metho an possibly the best among all the PWM techniques for variable spee rives application. Because of its superior performance characteristics, it has been fining wiesprea application in recent years. With a machine loa, the loa neutral is normally isolate, which causes interaction among the phases. This interaction was not consiere before in the PWM iscussion. Recently, fuzzy logic control has foun many applications in the past ecaes, which overcomes these rawbacks. Hence, fuzzy logic control has the capability to control nonlinear, uncertain systems even in the case where no mathematical moel is available for the controlle system. The structure of the work presente in this paper is organize in the following sequence. A brief literature survey of the relate work was presente in the previous paragraphs. Section presents about the overview of the block moel of the inuction motor with SPWM technique. Design of the fuzzy logic control scheme is presente in section 3. The section 4 shows the simulink moel for the spee control of the inuction motor. The graphical results of the simulation & the iscussion are presente in section 5. This is followe by the conclusions an the references.. Inuction motor moel The mathematical moel of the system consists of space vector PWM voltage source inverter, inuction motor, irect flux an the torque control. Direct Torque Control (DTC) uses an inuction motor moel to preict the voltage require to achieve a esire output torque [6]. By using only current an voltage measurements, it is possible to estimate the instantaneous stator flux an output torque. Figure 1. Power circuit connection iagram for the IM s sq R s R s ω λ sq L ls L lr ω λ A rq t λ s L m t λ r (a) -axis ω λ s L ls L ω λ lr A r t λ sq L m t λ rq (b) q-axis Figure. Equivalent circuit of inuction motor in -q frame The power circuit of the inuction motor is shown in the Fig. 1. The equivalent circuit use for obtaining the mathematical moel of the inuction motor is shown in the Fig.. An inuction motor moel is then use to preict the voltage require to rive the flux an torque to the emane values within a fixe time perio. This calculate voltage is then synthesize using the space vector moulation. The stator & rotor voltage equations are given by [9,11]. = s Ri + s s s sq t λ ωλ (1) = sq Ri + s sq sq s t λ ωλ () = r Ri + r r r A rq t λ ω λ (3) = rq Rri + rq rq A r t λ ω λ (4) A squirrel-cage inuction motor is consiere for the simulation stuy in this paper, so the an q-axis components of the rotor voltage are zero. The fluxes to currents are relate by the equation [11]. λs is Ls 0 Lm 0 λ sq i sq 0 Ls 0 L m = M ; M = λ r i r Lm 0 Lr 0 λ rq i rq 0 Lm 0 Lr The electrical part of an inuction motor can thus be escribe by a fourth-orer moel [11], which is given in (6), by combining equations (1) - (5): R r R r r (5) rq

3 76 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January 009 is i sq 1 = i r Lm Lr Ls i rq is Ls 0 Lm 0 s i sq 0 Ls 0 L m sq A + i r L m 0 L r 0 r irq 0 Lm 0 L r rq where, A is given by [11] A L R ω L ω L L L L L L R r s A m s r s ( ω ω ) A m s r s r s = LmRs LsLm s A ( ω ω ) LL s m( ωs ωa) LmRs LmRr LrLm( ωs ωa) LL r m( ωs ωa) LmRr LsRr ωslm ωalrls ( ωslm ωalrls) LsRr The instantaneous torque evelope in the inuction motor is given by [11] (6) (7) P Tem = ( λrqir λrirq ) (8) The electromagnetic torque expresse in terms of inuctances is given by [11] P Tem = Lm ( isqir isirq ) (9) The mechanical part of the motor is moele by the equation [11] P L m( i sq i r i s i rq) T T L em TL ω Mech = = (10) t Jeq Jeq where, Equivalent MI, J eq = ωa = ωslip = ωs ωm P ωm = ωmech, ω = ωs, Ls = Lsl + Lm L = L + L r rl m A 3-phase brige inverter has 8 permissible switching states as shown in Fig. 3. The table 1 gives the summary of the switching states an the corresponing phase-toneutral voltage of isolate neutral machine [9, 11]. s4 (011) (010) s3 q-axis Sector (110) s s7 Sector 3 Sector 1 Sector 4 s0 Sector 6 Sector 5 s5 s6 (001) (101) Figure 3. Space vector sequence Table 1 : Switching states -axis s1 (100) a b c An An An /3 /3 / /3 /3 / /3 /3 / /3 /3 / /3 /3 / /3 / /3 3. Design of the fuzzy logic control scheme Fuzzy controllers have got a lot of avantages compare to the classical controllers such as the simplicity of control, low cost an the possibility to esign without knowing the exact mathematical moel of the process. Fuzzy logic is one of the successful applications of fuzzy set in which the variables are linguistic rather than the numeric variables. Linguistic variables, efine as variables whose values are sentences in a natural language (such as large or small), may be represente by fuzzy sets. Fuzzy set is an extension of a crisp set where an element can only belong to a set (full membership) or not belong at all (no membership). Fuzzy sets allow partial membership, which means that an element may partially belong to more than one set. A fuzzy set A of a universe of iscourse X is represente by a collection of orere pairs of generic element x X an its membership function μ : X [ 0

4 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January ], which associates a number μ A (x) : X [ 0 1], to each element x of X. Rule base Actual output Plant De-fuzzification Unit Decision Making Unit Data base ωref is the reference spee, ωr is the actual rotor where spee, ek ( ) is the error an Δek ( ) is the change in error. The output of the ecision-making unit is given as input to the e-fuzzification unit an the linguistic format of the signal is converte back into the numeric form of ata in the crisp form [5]. The ecision-making unit uses the conitional rules of IF-THEN-ELSE. In the first stage, the crisp variables e(k) an e(k) are converte into fuzzy variables [7]. The fuzzification maps the error, an the error changes to linguistic labels of the fuzzy sets. The propose controller uses following linguistic labels: {NB Negative Big), NM Negative Meium), NS (Negative Small), ZE (Zero), PS (Positive Small), PM (Positive Meium), PB(Positive Big)}. Each fuzzy label has an associate membership function. The membership functions of triangular type are shown in the Fig. 5. Refer ence Fuzzification Unit Error Figure 5. FIS Fuzzy eitor with inputs an 1 outputs evelope simulink moel The rule base for the ecision-making unit is written as shown in the table. Figure 4. A iagrammatic view of a fuzzy logic controller A fuzzy logic controller is base on a set of control rules calle as the fuzzy rules among the linguistic variables [7]. These rules are expresse in the form of conitional statements. Our basic structure of the fuzzy logic controller to control the spee of the inuction motor consists of 4 important parts, viz., fuzzification, knowlege base, ecision-making logic an the efuzzification. The internal structure of the controller is shown in the Fig. 4. The necessary inputs to the ecisionmaking unit blocks are the rule-base units an the ata base block units. The fuzzification unit converts the crisp ata into linguistic formats. The ecision making unit ecies in the linguistic format with the help of logical linguistic rules supplie by the rule base unit an the relevant ata supplie by the ata base [8, 5]. The error & the change in error is moele using the equation (11) as ek ( ) = ωref ωr (11) Δ ek ( ) = ek ( ) ek ( 1) ΔE E Table : Rule base for controlling the spee NB NM NS ZE PS PM PB NB NB NB NB NB NM NS ZE NM NB NB NM NM NS ZE PS NS NB NM NS NS ZE PS PM ZE NB NM NS ZE PS PM PB PS NM NS ZE PS PS PM PB PM NS ZE PS PM PM PB PB PB ZE PS PM PB PB PB PB 4. Development of the simulink moel The esign of the fuzzy logic controller is evelope using the fuzzy logic toolbox available in Matlab / Simulink. In this paper, fuzzy logic controller employs the spee error an the change of spee error as the inputs [5]. The change in the spee component of current that rives the inuction motor is obtaine as the output. The

5 78 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January 009 evelope simulink moel in MATLAB is shown in the Fig Simulation results & iscussions Simulations are carrie out in Matlab. The response curves of voltage, stator current, torque & the spee v/s time are shown in the Figs respectively. The surface plot of the error, change in error an the spee is shown in the Fig. 11. From the results, it is observe that the stator current oes not exhibit any overshoots & unershoots & the response of the spee curve takes less time to settle & reach the esire value. Figure 7. Plot of control voltage v/s time Figure 8. Plot of stator current v/s time Figure 9. Plot of torque v/s time Figure 6. Develope simulink moel Figure 10. Plot of spee v/s time

6 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January Figure 11. Surface plot of error, change in error with spee 6. Conclusion The spee control of an inuction motor rive by means of the AI base fuzzy technique using SPWM concept has been investigate in this paper. Uner reference spee trajectory the fuzzy controller has shown goo performances. The settling time of the torque & the spee matche with the esire values that were taken uring the simulations. By the metho presente in this paper, the efficiency, performance an reliability of inuction motor rive increases. Steay state error in spee control is acceptable an there is not any overshoot. References [1] Ashok Kusagur, Jagaish Pujar, Design of A AR Compensator, Proc. International Conference on Trens in Intelligent Electronic Systems, Satyabhama University, Chennai, Tamil Nau, Inia, Nov. 1-14, 007. [] Bose B. K., Moern Power Electronics an AC Drives, Pearson Eucation, Inc., 00. [3] Jae Ho Chang an Byung Kook Kim, Minimum-Time Minimum-Loss Spee Control of Inuction Motors Uner Fiel-Oriente Control, IEEE Trans. Inustrial Electronics, ol. 44, No. 6, Dec. 1997, pp [4] Jagish G. Chauhari, Saneep K. Mue, Prakash G. Gabhane, High Performance Direct Torque Control of Inuction Motor Using Space ector Moulation, Proc. IEEE Int. Conf. CCECE/CCGEI, Ottawa, Canaa, IEEE Catalog No , pp [5] Jagish Pujar, Ashok Kusagur, SF Koa, T.C. Manjunath, Fuzzy Logic Base Flexible Multi-Bus oltage Control of Power Systems, Proc. of the 31 st National Systems Conference, NSC-007, MIT-MAHE Campus, Manipal , Karnataka, Inia, 14-15, Nov [6] Maamoun A., A. M. Soliman, A. M. Kheirelin, Space- ector PWM Inverter Feeing a Small Inuction Motor, Proc. of IEEE International Conference on Mechatronics, Kumamoto Japan, Paper No. TuAl -C-3, X/07, 8-10 May 007. [7] Mao-Fu Lai, Michio Nakano, Guan-Chyun Hsieh, Application of Fuzzy Logic in the Phase-Locke Loop Spee Control of Inuction Motor Drive IEEE Trans. Inustrial Electronics, ol. 43, No. 6, Dec. 1996, pp [8] Mokrani, R. Abesseme, A Fuzzy Self-Tuning PI Controller for Spee Control of Inuction Motor Drive Proc. IEEE Int. Conf., 003, IEEE Catalog No. pp [9] Muhamme H. Rashi, Power Electronics Circuits, Devices an Applications, Martin Brown - Power Supply textbook, Motorola, Butterworth Thine Mann. [10] Rong-Jong Wai an Kuo-Min Lin, Robust Decouple Control of Direct Fiel-Oriente Inuction Motor Drive, IEEE Trans. Inustrial Electronics, ol. 5, No. 3, pp , Jun [11] Yu Zhang, Zhenhua Jiang, Xunwei Yu, Inirect Fiel- Oriente Control of Inuction Machines Base on Synergetic Control Theory, IEEE Paper. Ashok Kusagur was born in the year 1970 an he receive the B.E. egree in EEE from BIET, Davanagere, Karnataka, Inia from Kuvempu University in the year 1996 an the M.Tech. egree in Power Electronics from PDA College of Engg., Gulbarga, Karnataka, Inia from the TU in the year 001. He has got a teaching experience of nearly 10 years. Currently, he is working as Assistant Professor in HMS Institute of Technology, Tumkur, Karnataka, Inia in the Dept. of Electronics & Communications Engg. & simultaneously oing his Ph.D. (Research Scholar) in Electrical & Electronics Engg. from the prestigious Jawaharlal Nehru Technological University (JNTU), Hyeraba, Anhra Praesh, Inia. His area of interests are neural networks, fuzzy logic, artificial intelligence, power electronics, Matlab, etc. Dr. S. F. Koa receive the B.E. egree in EEE from STJ Institute of Technology, Ranebennur, Karnataka, Inia from Karnataka University an the M.Tech. egree in Energy Systems Engg. from JNTU, Hyeraba, Inia in the year 199. He receive his Ph.D. egree in Electrical Engg. from JNTU, Hyeraba, Inia in the year 004. He has got a teaching experience of nearly 0 years. Currently, he is working as Professor & Hea in Aurora College of Engg., Hyeraba, Anhra Praesh, Inia in the Dept. of Electrical & Electronics Engg. He has publishe a number of papers in various national & international journals & conferences & one a number of in-house & inustry projects. He has also presente a number of guest lectures an various seminars an participate in a number of courses, seminars, workshops, symposiums in the various parts of the country in ifferent institutions an also conucte a few courses. He is also guiing a number of Ph.D. stuents. His area of interests are neural networks, fuzzy logic, power electronics, power systems, artificial intelligence, Matlab, Renewable energy sources, etc.

7 80 IJCSNS International Journal of Computer Science an Network Security, OL.9 No.1, January 009 Dr. B.. Sankar Ram receive the B.E. egree in Electrical Engg. from Osmania University & M.E. egree in Power Systems from Osmania University, Hyeraba, Anhra Praesh, Inia. He receive his Ph.D. egree in Electrical Engg. from JNTU, Hyeraba, Inia. He obtaine Diploma in Caniate Management Finance. He has got a teaching experience of more than 0 years. Currently, he is working as Professor in JNTU College of Engg. Hyeraba, Inia in the Dept. of Electrical Engg. He has publishe a number of papers in various national & international journals & conferences & one a number of in-house & inustry projects. He is also guiing a number of research scholars in various topics of engg. He has specialize in power systems. His research interests inclue power system reliability an Flexible AC Transmission systems, power electronics & it is applications.