SPEED ESTIMATION OF MRAS BASED INDUCTION MOTOR DRIVE UTILIZING MACHINE S D- AND Q- CIRCUIT IMPEDANCES USING PI AND ANFIS CONTROLLER

SPEED ESTIMATION OF MRAS BASED INDUCTION MOTOR DRIVE UTILIZING MACHINE’S D- AND Q- CIRCUIT IMPEDANCES USING PI AND ANFIS

CONTROLLER

M. ANKA RAO¹, CHERUVU KEERTHANA², M. VIJAYA KUMAR³ & J. SREENIVASULU⁴

,14Assistant Professor, JNTUA College of Engineering (Autonomous) Anantapuramu, A. P, India

2Student, JNTUA College of Engineering (Autonomous) Anantapuramu, A. P, India

3Professor, JNTUA College of Engineering (Autonomous) Anantapuramu, A. P, India ABSTRACT

This paper presents the principle of sensorless speed estimation of induction motor drive based on the model reference ad aptive system (MRAS), using the efficient impedances from d and q circuits. MRAS functional part is given by the difference of the effective working impedances of the stator d and q circuit. This approach shows no flux orientation shift when calculating the speed. This system performs satisfactorily over all four operating quadrants and is also free from the stator resistance effect. The analysis is verified by comparing various MATLAB simulations using both PI and ANFIS controllers.

KEYWORDS: Induction motor drives, Model Reference Adaptive System, PI & ANFIS.

Received: May 25, 2020; Accepted: Jun 27, 2020; Published: Aug 04, 2020; Paper Id.: IJMPERDJUN2020619 1. INTRODUCTION

Induction motor drives have been comprehensively used in different industry and manufacturing processes like rolling mills, mining industry, conveyors, elevators, textile mills and transportation systems as they are enviable, very robust in nature and can be easily maintained [1]. By using vector control theory induction motors are often decoupled into flux and torque subsystem [2]. Vector control is of two types, namely indirect vector control and direct vector control. The indirect field orientated control of IM drive are used very often, as they provide quick dynamic response. There are various induction motor speed control techniques which are used to improve the efficiency such as signal injection method [3], Kalman filter, MRAS techniques, adaptive schemes, sliding mode techniques, direct calculation method. The observer-based methods are profoundly reliant on the machine criterion and spawns the stagnant convergence. Despite, the sliding mode techniques [4]-[7] show rapider convergence, and yet may exhibit chattering problems.

Considering above all methods MRAS method is the best one due to its simple structure and easy implementation for controlling the speed in Induction motor drives. MRAS techniques are distinguished by distant types such as back EMF, rotor flux, reactive power [8], active power, X-MRAS etc. The vector control of Induction machines using MRAS is shown in [8]. Among the above Model reference adaptive system techniques reactive power MRAS[10] and X-MRAS[11] are the plainest ones of speed estimation which yield enhanced low or zero speed performances. Nonetheless, the classic reactive power MRAS technique may exhibit some instability in the regenerating mode, with the X-MRAS dependent on the stator resistance. The current work comes up with a new model of MRAS technique for the estimation of speed of IM drive that can overcome the regenerating mode instability of reactive power (Q) and also the stator resistance influence with X-MRAS technique on the speed

Origin a l Ar ticle

Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001

Vol. 10, Issue 3, Jun 2020, 6505-6516

© TJPR Pvt. Ltd.

estimated while upholding the low speed performance which can be obtained through preferring d and q circuit stator impedances with the mode of Zmras= Vdi_d⁻¹− Vqiq−1 which is taken as the functional component in MRAS estimator.

MRAS consists of two parts named reference and adaptive models in which the steady state values are studies in the adaptive model and the instantaneous values in the reference model. During the speed estimation at nominal value of rotor resistance, the system is independent of flux computation.

2. Z-MRAS SPEED ESTIMATOR

The stator voltages Induction motor are given as V_ds and V_qs as follows in synchronously rotating reference frame, V_ds= R_si_ds+ σL_si̇̇_ds+^Lm

L_rψ̇_dr− σL_sω_ei_qs− ω_e^Lm

L_rψ_qr (1) V_qs= R_si_qs+ σL_si̇̇_qs+Lm

L_rψ̇_qr+ σL_sω_ei_ds+ ω_eLm

L_rψ_dr Where ω_e= ω_r+ ωsl (3)

Under field orientation the rotor d axis flux is ψ_dr = L_mi_qs, and the slip speed (ω_sl) can be obtained as ω_sl= (R_ri_qs)/(L_ri_ds) (4)

(a) (b)

Figure 1: Induction Motor Equivalent Circuit with (a) d-axis, (b) q - axis Circuits

The effective d-q circuit stator impedances with the ‘1’-‘2’ terminals at the rotor speed (ωr) is expressed as Zd

and Z_q are given as follows which is derived from the above equivalent circuit, Z_d=^Vds

i_ds (5) Z_q=^Vqs

i_qs (6)

For the nominal parameters of the machine see Appendix 1, the impedances Z_d and Z_q varies with only the rotor speed (ω_r). Hence, the difference of Z_d and Z_q is treated as a functional part of the above mentioned MRAS. It is defined as Z_mras

Z_mras=^Vds

i_ds −^Vqs

i_qs (7)

Apparently, the Z_mras mentioned above represents the instantaneous ohmic value. As, eq (7) is autonomous of the speed terms, this can be employed in reference model.

With the substitution of the voltage equations V_ds and V_qs from eq-1 in eq-7, the new expression for Z_mras is shown below which is dependent upon the speed terms (estimated)

d- and q- Circuit Impedances Using PI and ANFIS Controller Ẑmras = -σL_s(^i̇̇ds

i_ds−^i̇̇qs

i_qs) +^Lm

L_r(^ψ̇dr

i_ds −^ψ̇qr

i_qs) − σL_sω̂_e(^ids

i_qs+^iqs

i_ds) − ω̂_e^Lm

L_r(^ψdr

i_qs +^ψqr

i_ds) (8) At the time of steady state, time derivative terms disappear, and the eq-8 is modified as follows Ẑmras=-σL_sω̂_e(^ids

i_qs+^iqs

i_ds) − ω̂_e^Lm

L_r(^ψdr

i_qs +^ψqr

i_ds) (9)

Further, by utilizing the conditions of field orientation method rotor flux of d axis is given as zero (i.e ψ_dr = L_mi_ds and ψ_qr= 0 ),

Ẑmras= -ω̂_eL_s(^ids

i_qs+ σ^iqs

i_ds ) (10)

The above equation consists of synchronous speed thence is used in the adaptive model of Z-MRAS. The fig shown below gives the proposed MRAS with adaptive and reference models.

Figure 2: Proposed Design of Z-MRAS 3. SIMULATION OF IM USING PI AND ANFIS CONTROLLER

Figure 3: Simulink Block Diagram of IM

A. Simulation Results of Z-MRAS Based IM Using PI- Controller a. The Operation at Zero, Ramp and Low Speeds

(a) (b)

Figure 4: Simulation Results for the Operation of Zero, Ramp and Low Speeds at the Load of 2 Nm.

(a) Speed and(b) Rotor Flux of d-q b. The Operation at No-Load

(a) (b)

Figure 5: Simulation Results at No-Load Condition.

(a) Speed and (b) Rotor Flux of d-q c. The Operation at Rated Load Torque

(a) (b)

(c)

Figure 6: Simulation Results from Zero to Rated Load Operation.

(a) Speed (b) Rotor Flux of d-q (c) Torques a. The operation at High Speed

d- and q- Circuit Impedances Using PI and ANFIS Controller

(b) (b)

Figure 7: Simulation Results for the Step Change in the Rotor Speed at 2 Nm Load.

(a) Speed and (b) Rotor flux of d-q b. The Motoring and the Regenerating Performances

(a) (b)

(c) (d)

Figure 8: Simulation Results for the operation of 1st and 2nd Quadrants.

(a) Speed (b) Rotor Flux of d-q (c) Torque (d) Power Flow

(a) (b)

(c) (d)

Figure 9: Simulation Results for the Operation of Zero to Rated Load in the Third and Fourth Quadrants.

(a) Speed (b) Rotor Flux of d-q (c) Torque (d) Power Flow c. The Stator and the Rotor Resistance Variation Effect

(a) (b)

(c)

Figure10: Simulation Results of the Speed Stator Resistance Sensitivity on Speed Estimated.

(a) The Stator Resistance (b) Speed (c) Rotor Flux of d-q

(a) (b)

(c)

Figure 11: Simulation Results of the Rotor Resistance Sensitivity on Speed Estimated.

(a) The rotor resistance, (b) Speed, (c) Rotor flux of d-q B. Simulation of Z-MRAS Based IM Using ANFIS Controller

The Adaptive neuro fuzzy inference system (ANFIS) is the ANN [artificial neural network] which is the sequence of the Neural Networks and the Fuzzy systems.

Figure 12: ANFIS Controller

The fuzzy inference system constitutes a fuzzy model which was given by Kang and Takagi-Sugeno to formalize a precise access to spawn the fuzzy rules from the input - output dataset. In this paper we proposed the two inputs and 7

d- and q- Circuit Impedances Using PI and ANFIS Controller

fuzzified values in which 49 fuzzy rules are framed in fuzzy controller system.

Figure 13: ANN Structure with Two Inputs and One Output for ANFIS Controller.

C. Simulation Results of Z-MRAS Based IM Using ANFIS Controller The Operation at Zero, Ramp and Low Speeds

(a) (b)

Figure 14: Simulation Results for the Operation of Zero, Ramp and Low Speeds at the Load of 2 Nm.

(a) Speed and (b) Rotor Flux of d-q The Operation at No-Load

(a) (b)

Figure 15: Simulation Results at No-Load Condition.

(a) Speed and (b) Rotor flux of d-q The Operation at Rated Load Torque

(a) (b)

(c)

Figure 16: Simulation Results from Zero to Rated Load Operation.

(a) Speed (b) Rotor Flux of d-q (c) Torques The Operation at High Speed

(a) (b)

Figure 17: Simulation Results for the Step Change in the Rotor Speed at 2 Nm Load.

(a) Speed and (b) Rotor Flux of d-q The Motoring and the Regenerating Performances

(a) (b)

(c) (d)

Figure 18: Simulation Results for the Operation of 1st and 2nd Quadrants.

(a) Speed (b) Rotor Flux of d-q (c) Torque (d) Power Flow

d- and q- Circuit Impedances Using PI and ANFIS Controller

(a) (b)

(c) (d)

Figure 19: Simulation Results for the Operation of Zero to Rated Load in the Third and Fourth Quadrants.

(a) Speed (b) Rotor Flux of d-q (c) Torque (d) Power Flow The Stator and the Rotor Resistance Variation Effect

(a)

(b)

(c)

Figure 20: Simulation Results of the Speed Stator Resistance Sensitivity on Speed Estimated.

(a) The Stator Resistance (b) Speed (c) Rotor Flux of d-q

(a) (b)

(c)

Figure 21: Simulation results of the rotor resistance sensitivity on speed estimated.

(a) The rotor resistance (b) Speed (c) Rotor flux of d-q 4. CONCLUSIONS

In the proposed work, speed is estimated using model reference adaptive system which utilises the d and q circuit impedances. This is free from the stator resistance which also gives no change in flux orientation. The system works effectively in all the four quadrants. The current work is shown using both the PI controller and ANFIS controller. From the above simulation results of PI and ANFIS controllers we can conclude that with the use of PI controller there are some disturbances in the speed and output power and with the use of ANFIS controller the disturbances are minimised, which clearly shows that with the ANFIS technology the dynamic performance of the system has improved.

APPENDIX

Parameters of the Drive

Rating 4-pole, 3 HP, 3-Φ, 415 V, 50 Hz Power factor, rated speed, rated torque 0.83, 150 rad/s, 14 Nm

Stator resistance 9.195 Ω

Rotor resistance 7.325 Ω

Stator leakage inductances 1.342 H

Rotor leakage inductances 1.342 H

Mutual inductance 0.7 H

Rotor inertia 0.018 kg-m2

Frictional coefficient 0.001

REFERENCES

1. B. K. Bose, Modern power electronics and ac drives, New Delhi: Prentice Hall, 2002.

2. J. Kan, K. Zhang and Z. Wang, “Indirect vector control with simplified rotor resistance adaptation for induction machines,”

IET Power Electronics, vol. 8, no. 7, pp. 1284-1294, July 2015.

3. A. Hasanzadeh, D. M. Reed and H. F. Hofmann, "Rotor Resistance Estimation for Induction Machines Using Carrier Signal Injection With Minimized Torque Ripple," in IEEE Transactions on Energy Conversion, vol. 34, no. 2, pp. 942-951, June 2019.

4. Saravanan, M., K. Suresh, and S. Uma. "Novel reversible code converters using reversible logic gates." International Journal of Electrical and Electronics, Engineering Research 3.3 (2013): 161-166.

d- and q- Circuit Impedances Using PI and ANFIS Controller

5. H. Wang, Y. Liu and X. Ge, "Sliding-mode observer-based speed-sensorless vector control of linear induction motor with a parallel secondary resistance online identification," in IET Electric Power Applications, vol. 12, no. 8, pp. 1215-1224, 9 2018 6. X. Zhang, “Sensorless induction motor drive using indirect vector controller and sliding-mode observer for electric vehicles,”

IEEE Trans. Veh. Technol., vol. 62, no. 7, pp. 3010-3018, Sept. 2013.

7. M. Comanescu, “Single and double compound manifold sliding mode observers for flux and speed estimation of the induction motor drive,” IET Electric Power Appl., vol. 8, no. 1, pp. 29-38, Jan. 2014.

8. Abrol, Sanchita, and Deepak Chhabra. "Experimental investigations of piezoelectric energy harvesting with turbulent flow."

International Journal of Mechanical and Production Engineering Research & Development 8.1 (2018): 703-710.

9. V. Utkin, "Discussion Aspects of High-Order Sliding Mode Control," in IEEE Transactions on Automatic Control, vol. 61, no.

3, pp. 829-833, March 2016.

10. R. Kumar, S. Das, P. Syam and A. K. Chattopadhyay, “Review on model reference adaptive system for sensorless vector control of induction motor drives,” IET Electric Power Applications, vol. 9, no. 7, pp. 496511, July 2015.

11. PANDA, SARADINDU, et al. "Comparative study of thermal noise of Si surrounding gate MOSFET (SGMOSFET) with different gate oxides." Science & Technology (IJSST) 3.2 (2013): 17-22.

12. Benlaloui, S. Drid, L. Chrifi-Alaoui and M. Ouriagli, "Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine," in IEEE Transactions on Energy Conversion, vol. 30, no. 2, pp. 588-595, June 2015.

13. S. Maiti, C. Chakraborty, Y. Hori and C. M. Ta, “Model reference adaptive controller-based rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power,” IEEE Trans. Ind. Electron., vol.

55, no. 2, pp. 594-601, Feb. 2008.

14. Resconi, G., and K. Nagata. "quantum circuit by one step method and similarity with neural network." International Journal of General Engineering and Technology (IJGET) 7.1 (2018): 1-20.

15. A. Teja, C. Chakraborty, S. Maiti and Y. Hori, “A new model reference adaptive controller for four quadrant vector controlled induction motor drives,” IEEE Trans. Ind. Electron., vol. 59, no. 10, pp. 3757-3767, Oct. 2012.

AUTHOR’S PROFILE

Dr. M. Ankarao, He received his Bachelor of Electrical & Electronics engineering degree from the Andhra University, Vishakapattanam, A. P in 2006. In 2010, he received M. Tech degree from JNTU Anantapuramu in Power and Industrial Drives. He received his Ph. D in 2020 from JNTUniversity Ananthapuramu, India. He currently serves as assistant professor at JNTUA College of Engineering, Electrical Department, Ananthapuramu, India. He has a 10 years of teaching experience. His research areas include Electrical Machines, Electrical Drives and Power Electronics.

Cheruvu Keerthana, graduated from Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, India in 2018, from the Electrical and Electronics Engineering. She is currently pursuing Master’s Degree in Power & Industrial Drives specialization from the department of Electrical & Electronics Engineering, from JNTUA college of Engineering, Ananthapuramu, India, in 2020. In fulfilment of her Bachelor’s degree she has done projects on Arduino based underground cable fault detection and E-Toll System. She gave a UGC sponsored National Seminar on SOLAR ENERGY contributed to Emerging Trends in Harnessing Green Energy. Her research interests are speed estimation techniques of Induction motors and different model reference adaptive system techniques.

Prof M. Vijaya Kumar graduated in 1988 from the S. V. University, Tirupathi, A. P, India. He recieved M. Tech degree in 1990 from the Regional Engineering College (NITW), Warangal, India. He also received his Doctorate from the Jawaharlal Nehru Technological University, Hyderabad, India in 2000. Currently he is working as a Professor in Electrical and Electronics Engineering and also as the Director of Academic & Planning, as JNTUniversity Anantapur Registrar, A.

P, India. He has 30 years of experience in teaching. His areas of focus includes Electrical Machines, Electrical Drives, Microprocessors and Power Electronics.

Dr. J. Sreenivasulu, He earned his Bachelor’s degree in Electrical and Electronics engineering in 2007. He received his M. Tech in Electrical Power System in 2009. He received his Ph. D from the JNT University, Anantapuramu, A. P, India, in 2019. Currently he serves as Assistant Professor in EEE Department, JNTUA college of Engineering, Anantapuramu, A.

P, India. His research interest areas are Electrical Power Systems, Reliability Engineering, Drives and Restructured Power Systems.. He has a teaching experience of 9 years.