Effect of rotor resistance and compensation in indirect vector control using

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Effect of rotor resistance and compensation in indirect vector control using

  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 255 EFFECT OF ROTOR RESISTANCE AND COMPENSATION IN INDIRECT VECTOR CONTROL USING ARTIFICIAL NEURAL NETWORKS (ANN) B.Mouli Chandra1 , Dr.S.Tara Kalyani2 1 EEE Department, JNTUH, Hyderabad, INDIA 2 EEE Department, JNTUH, Hyderabad, INDIA ABSTRACT In this paper the effect of rotor resistance in indirect vector control of Induction Motor (I.M) is examined and compensated using Artificial Neural Networks. Since Rotor resistance is prone to error due to temperature rise in machine. Because of this, resistance used in controller differ from resistance used in machine. This leads to performance deterioration of machine unless otherwise it is compensated. Here the rotor resistance estimated from the voltage and current model equations using ANN. The performance of the machine is analysed using MATLAB/SIMULINK in terms of output currents, rotor flux, and torque and verified experimentally. Keywords: Induction motor, Indirect Vector control, Rotor resistance estimation, ANN. 1. INTRODUCTION For the high performance control, Induction motors are controlled from vector control method and because of rugged in construction the I.M were employed for constant speed application. In the few decades back after the development of DSP, microprocessor controllers they were employed in adjustable speed applications. Separately excited D.C machine like performance can be obtained by vector control (decoupling control). Vector control improves the dynamic response of machine by providing independent control channels to torque and flux quantities. Since the rotor flux position is mandatory in vector control, based on the method for which rotor flux position is obtained vector control has subdivided into two methods: 1. Direct Vector Control. 2. Indirect Vector Control. In the first method flux position is directly measured from position sensors which increased the complexity of machine to the manufacturer. In the second method the flux is estimated from voltage and current measurements in the feedforward manner [1]. In the indirect vector control slip speed is added to actual speed from which synchronous speed is obtained there by flux position (θ) is obtained by integration. INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), pp. 255-263 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2013): 5.5028 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 256 But the main drawback of the indirect vector control is parameter variation. Since the slip speed (ωsl) mainly depends on rotor resistance (Rr), which increases with temperature rise in machine. So error in slip speed leads to wrong alignment of torque and flux vectors used in vector rotation. Therefore there is need to estimate the rotor resistance such that estimated resistance track with the changes in resistance used in controller. There are several methods of rotor resistance identification proposed by different authors namely stochastic estimator method known as Extended Kalman Filter [2][3], deterministic estimator method known as Extended Luenberger Observer [ELO] [4][5], since these methods demands high level computations based on 5th order state space model and therefore the algorithms developed increases the complexity. In [6] Model Reference Adaptive Systems (MRAS) scheme was developed which adjusts rotor resistance by constructing reference model. In [7] with the help of search coils, on the flux axis a sinusoidal current was injected. In [8] least square technique based on recursive algorithm was developed, this method provides 30% approx error between actual and estimated values. In [9] rotor resistance was identified by using Pseudorandom binary sequence into flux axis, the main drawback of this method is convergence method is more than 30sec. In this paper a rotor resistance is estimated from the flux calculated from the voltage model equations and flux calculated from current model. From the error produced by these two models rotor resistance algorithm based on ANN is developed which is used to tune rotor resistance in the form of weights in current model and thus the performance of motor is improved. II. INDIRECT VECTOR CONTROL The ideal vector control of Induction Motor equations considering in synchronously reference frame is given by ߰௤௥ ൌ 0 (1) ߰ௗ௥ ൌ ߰௥ (2) From which we obtain the slip gain Ks which is useful in flux positioning and is given by ‫ܭ‬௦ ൌ ߱௦௟ ݅௤௦⁄ ൌ ‫ܮ‬௠ܴ௥ ‫ܮ‬௥߰௥⁄ (3) It is evident from the equation (3) the slip gain constant Ks which is used to generate slip speed command ωsl depends on rotor resistance and magnetizing inductance and rotor inductance and also rotor flux. Treating all the parameters to be constant under steady state conditions except rotor resistance which varies with temperature rise. From the voltage model the rotor flux linkage equations are given by ‫ݒ‬ௗ௦ ൌ ܴ௦݅ௗ௦ାߪ‫ܮ‬௦ ௗ೔೏ೞ ௗ௧ ൅ ቂ ௅೘ ௅ೝ ቃ ௗట೏ೝ ௗ௧ (4) ‫ݒ‬௤௦ ൌ ܴ௦݅௤௦ା ߪ‫ܮ‬௦ ௗ೔೜ೞ ௗ௧ ൅ ቂ ௅೘ ௅ೝ ቃ ௗట೜ೝ ௗ௧ (5) III. EFFECT OF ROTOR RESISTANCE VARIATION As the temperature of the machine rises during the normal running conditions of induction motor the rotor resistance parameter would increases upto 50-100% of its nominal value. Therefore it firstly effect on slip gain constant, which leads to wrong calculations of slip speed there by rotor flux angle calculated with this value is incorrect. Also it effect on wrong calculation of unit vectors used
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 257 in vector rotation. The block diagram of indirect vector control with rotor resistance estimation using ANN which includes the structure of neural network system is shown in Fig.1. Fig.1. Indirect Vector Control with rotor resistance control using ANN IV. ROTOR RESISTANCE ESTIMATION In the method proposed, the flux developed from the voltage model is compared with flux developed from the current model; the error is given to ANN controller as input quantities and rotor resistance estimated is given to the current model (neural model) in the form of weights. The structure for the rotor resistance estimation is shown in fig.2. The voltage model equations are already shown in (4) and (5). The current model equations are given by ௗ ௗ௧ ߰ௗ௥ ൌ ோೝ௅೘ ௅ೝ ݅ௗ௦ െ ߱௥߰௤௥ െ ோೝ ௅௥ ߰ௗ௥ (6) ௗ ௗ௧ ߰௤௥ ൌ ோೝ௅೘ ௅ೝ ݅௤௦ െ ߱௥߰ௗ௥ െ ோೝ ௅௥ ߰௤௥ (7) Writing the equations (6) and (7) in matrix form ቌ ௗట೏ೝ ೎೘ ௗ௧ ௗట೜ೝ ೎೘ ௗ௧ ቍ ൌ ቌ ିோ௥ ௅௥ െ߱௥ ߱௥ ିோ௥ ௅௥ ቍ ቆ ߰ௗ௥ ௖௠ ߰௤௥ ௖௠ቇ ൅ ௅௠.ோ௥ ௅௥ ൬ ݅ௗ௦ ݅௤௦ ൰ (8)
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 258 Equation (8) can be written as ௗట೅ೝ ೎೘ ௗ௧ ൌ ቄቀ 1 0 0 1 ቁ ቀ ିோ௥ ௅௥ ቁ ൅ ߱௥ ቀ 0 െ1 1 0 ቁቅ ்݀߰௥ ௖௠ ൅ ௅௠.ோ௥ ௅௥ ்݅௦ (9) After sampling the data model (9) is given by ்݀߰௥ ௖௠ሺ݊ሻ ൌ ‫1ݓ‬ ቀ 1 0 0 1 ቁ ்݀߰௥ ௖௠ሺ݊ െ 1ሻ ൅ ‫2ݓ‬ ቀ 0 െ1 1 0 ቁ ்݀߰௥ ௖௠ሺ݊ െ 1ሻ ൅ ‫3ݓ‬ ்݅௦ሺ݊ െ 1ሻ (10) Where, ‫1ݓ‬ ൌ 1 െ ܴ‫ݎܮ/ݏܶݎ‬ ; ‫2ݓ‬ ൌ ߱௥ܶ‫;ݏ‬ ‫3ݓ‬ ൌ ܴ‫ݎܮ/ݏܶ݉ܮݎ‬ Equation (10) can be written as ்݀߰௥ ௖௠ሺ݊ሻ ൌ ‫1ݔ1ݓ‬ ൅ ‫2ݔ2ݓ‬ ൅ ‫3ݔ3ݓ‬ (11) Where, x1=൬ ݀߰ௗ௥ ௖௠ ሺ݊ െ 1ሻ ݀߰௤௥ ௖௠ ሺ݊ െ 1ሻ ൰; x2=൬ െ݀߰௤௥ ௖௠ ሺ݊ െ 1ሻ ݀߰ௗ௥ ௖௠ ሺ݊ െ 1ሻ ൰; x3=൬ ݅ௗ௦ሺ݊ െ 1ሻ ݅௤௦ሺ݊ െ 1ሻ ൰ Here x1, x2, x3 represents inputs to the network, and w1, w2; w3 represents weights to the network. Fig.2. Structure of ANN for Rr estimation Since w2 is already known and updated weights are only w1 and w3. Since the weights w1 and w3 involves rotor resistance parameter it is just sufficient to update either w1 or w3 to find the estimated parameter. The weights update is done by using delta rule with back propagation algorithm the weights w1 and w3 are found from training the network so as to minimize the error function and which is given by ࣟ ൌ ଵ ଶ ሾ்݀߰௥ ௩௠ሺ݊ሻ െ ்݀߰௥ ௖௠ሺ݊ሻሿଶ (12) Weight update is done by using current weight and the correction required which is given by ‫1ݓ‬ሺ݊ሻ ൌ ‫1ݓ‬ሺ݊ െ 1ሻ ൅ ߟ∆‫1ݓ‬ሺ݊ሻ (13) Where ߟ is known as training coefficient ∆‫1ݓ‬ሺ݊ሻ ൌ െ߲ࣟ/߲‫1ݓ‬ =െߜ‫1ݔ‬ where ߜ ൌ డࣟ డట೅ೝ ೎೘ሺ௡ሻ
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 259 It is known that for the convergence of algorithm the current weight is added with most recent weights ‫1ݓ‬ሺ݊ሻ ൌ ‫1ݓ‬ሺ݊ െ 1ሻ െ ߟ ߜ‫1ݔ‬ ൅ ߙ ∆‫1ݓ‬ሺ݊ െ 1ሻ (14) Where, ߙ is known as momentum coffiecient Now rotor resistance Rr can be calculated from w1 as ܴ‫ݎ‬ ൌ ‫ݎܮ‬ሺ1 െ ‫1ݓ‬ሻ/ܶ‫ݏ‬ (15) V. SIMULATION AND EXPERIMENTAL ANALYSIS In order to validate the Indirect Vector control with rotor resistance estimation simulation was done using MATLAB/SIMULINK with 1H.P 3-φ I.M. Initially the machine was simulated with indirect vector control, since the main drawback of the indirect vector control is slip gain constant highly dependent on Rr parameter, in order to examine the effect of rotor resistance a step variation in rotor resistance was done, by varying the Rr the effect of Rr on torque, flux, and currents were analysed and the proposed experimental work was shown in Fig.3. Experimentally in order to show the effect of rotor resistance in IVC and additional resistance of 30 Ohms is connected abruptly in star connected rotor winding and the changes in currents were noted as shown in Fig.4. to Fig. 9. and there by developing the rotor resistance algorithm using VHDL program and embedded to SPATRON 3A FPGA Controller, the parameter is adapted and thereby improving the performance of machine shown in Fig.10. to 15. Fig. 3. Experimental Setup for Rotor resistance Estimation TABLE.1 V 3-Phase Voltage 415V I Rated Current 2.4A Vr Rotor Voltage 230V P Poles 4 Rr Rotor Resistance 18.1 Ohms Rs Stator Resistance 10.6 Ohms Lls Stator Inductance 0.654 H Llr Rotor Inductance 0.311H Tr Rated Torque 33.075 N-m
  6. 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 260 VI. SIMULATION AND EXPERIMENTAL RESULTS Fig.4. Rotor resistance changed from 18.1 to 26.5 Fig.4. Torque is varying from 33.075 N-m to 32.1 N-m Fig.5. Torque is varying from 33.075 N-m to 32.1 N-m (Experimental) Fig.6. Rotor Flux varying from 1 to 1.1 T Fig.7. Rotor Flux varying from 1 to 1.1 T (Experimentl) 0 0.5 1 1.5 2 2.5 10 20 30 Time in Sec RrinOhsms 0 0.5 1 1.5 2 2.5 30 35 40 Time in Sec TorqueinN-m Actual torque Estimated torque 0 0.5 1 1.5 2 2.5 0 1 2 Time in Sec RotorFluxinT
  7. 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 261 Fig.8. Current magnitude change from 2.4A to 2A Fig.9. Current magnitude change from 2.4A to 2A (Experimental) Fig.10. Tracking of Estimated Rr with Actual Rr at 0.9 sec Fig.11. Torque compensates after 0.9 N-m Fig.12. Torque compensates after 0.9 N-m 0 0.5 1 1.5 2 2.5 0 2 4 Time in Sec CurrentinA 0 0.5 1 1.5 2 10 20 30 Time in Sec RrinOhms Actual Rr Estimated Rr 0 0.5 1 1.5 2 30 35 Time in Sec TorqueinN-m
  8. 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 262 Fig.13. Rotor Flux compensates at 0.9sec Fig.14. Rotor Flux compensates at 0.9sec (Experimental) Fig.15. Current compensates at 0.9 Sec Fig.15. Current compensates at 0.9 Sec (Experimental) VII. CONCLUSION The problem of rotor resistance variation in indirect vector control of induction motor is analysed using MATLAB/SIMULINK software, and also experimentally by using SPATRON 3A controller board. The changes in rotor flux, torque, and currents were compensated by developing Rotor resistance algorithm using Artificial Neural Networks. 0 0.5 1 1.5 2 2.5 0 0.5 1 Time in Sec RotorFluxinT 0 0.5 1 1.5 2 2.5 0 2 4 Time in Sec CurrentinA
  9. 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME 263 REFERENCES [1] B.K.Bose, Modern power electronics and AC drives (Pears Education, Singapore 2002) [2] T. Kataoka, S.Toda, and Y.Sato, “On line estimation of induction motor parameters by extended Kalman filter,” in Proc. Europe. Conf. Power Electron. Applicat., vol.4, 1993, pp. 325-329. [3] R.S. Pena and G.M.Asher, “Parameter sensitivity studies for induction motor parameter identification using extended Kalman filter,” in Proc. Europe. Conf. Power Electron. Applicat., vol.4, 1993, pp.306-311. [4] T.Du, and M.A. Brdys, “Implementation of extended Luenberger observers for joint state and parameter estimation of PWM induction motor drive,” in Proc. Europe. Conf. Power Electron. Applicat., vol.4, 1993, pp.439-444. [5] T.Du, P.Vas and F.Stronach, “Design and application of extended observers for joint state and parameter estimation in high-performance AC drives,” Proc. Inst. Elect. Eng-Elect. Power Applicat., vol.142, no.2, 1995, pp.71-78. [6] G.Griva, M.C. Ficcara, and F. Profumo, “Design of a speed regulator for induction motor drives based on model reference robust control,” in Proc. IEEE Int. Symp. Ind. Electron., 1997, pp. 484-488. [7] D.Y.Ohm, Y.Khersonsky, and J.R.Kimzey, “Rotor time constant adaptation method for induction motors using DC link power measurement,” in Proc. IEEE Ind. Applicat. Soc. Annu. Metting, 1989, pp.588-593. [8] N.R. Klaes, “Least Square Technique based online tuning of the rotor resistance in an inverter fed induction machine with diret-self-control,” Europe. Trans. Electr. Power, vol.4, no.1, 1994, pp. 5-11. [9] K.tungpimolrut, F.Z.Peng, and T.Fukao, “A robust rotor time constant estimation method for vector control of induction motor under any operating condition”, in Proc. IEEE Ind. Electron. Soc. Annu. Meeting, 1994, pp. 275. [10] Prof. Hemant chouhan, Ritesh Kumawat and Dr. H. K. Verma, “Comparative Analysis of Scalar and Vector Control Induction Machine Drive Through Modeling and Simulation”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2, 2012, pp. 39 - 50, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [11] Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “The Scheme of Three-Level Inverters Based on Svpwm Overmodulation Technique for Vector Controlled Induction Motor Drives”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 245 - 260, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [12] Pradeep B Jyoti, J.Amarnath and D.Subbarayudu, “Application of Neuro-Fuzzy Controller in Torque Ripple Minimization of Vector Controlled Vsi Induction Motor Drive”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013, pp. 121 - 127, ISSN Print : 0976-6545, ISSN Online: 0976-6553.

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