Interval Type-2 Fuzzy Logic PID Excitation Control System with AVR in Power System Stability Analysis

Interval Type-2 Fuzzy Logic PID Excitation Control System with AVR in Power System Stability Analysis

Manoj Kumar Sharma* R.P. Pathak Manoj Kumar Jha M.F. Qureshi

NIT Raipur, Chattisgarh, India

Mathematics Dept, NIT Raipur, Chattisgarh, India

Naveen K.T.C. College Salni, Janjgir-Champa, Chattisgarh, India

Department of electrical Engg., DTE, Raipur, Chattisgarh, India

Corresponding Author Email: 
Page: 
208-218
|
DOI: 
https://doi.org/10.18280/ama_c.730411
Received: 
26 June 2018
|
Accepted: 
15 October 2018
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

The application of a simple microcontroller to deal with a three variable input and a single output interval Type-2 fuzzy logic controller (IT2FLC), with Proportional Integral Derivative (PID) response control built-in has been tested for an automatic voltage regulator (AVR). The interval Type-2 fuzzifiers are based on fixed range of the variables of output voltage. The control output is used to control the wiper motor of the auto transformer to adjust the voltage, using interval Type-2 fuzzy logic principles, so that the voltage is stabilized. In this report, the author will demonstrate how interval Type-2 fuzzy logic might provide elegant and efficient solutions in the design of multivariable control based on experimental results rather than on mathematical models. This works aims to develop a controller based on PID and Interval Type-2 Fuzzy Logic Controller (IT2FLC) to simulate an automatic voltage regulator (AVR) in transient stability power system analysis. It was simulated a one machine control to check if the Interval Type-2 Fuzzy Logic Controller (IT2FLC) and PID controller implementation was possible. After that the developed controller was applied in field excitation system to show its behavior, which results were compared to the results obtained with the AVR itself.

Keywords: 

interval Type-2 fuzzy logic controller (IT2FLC), PID controller, control systems, controlled AVR

1. Introduction
2. Dynamic Power System Model
3. Transient Stability Analysis
4. PID Controller
5. Artificial Intelligence and Interval Type-2 Fuzzy Logic Controller
6. Simulation Results and Discussion
7. Numerical Simulation
8. Discussion and Conclusion
9. Results
10. Conclusion
  References

[1] Clerc M, Kennedy J. (2002). The particle swarm explosion, stability and convergence in a multidimentional complex space. IEEE Transactions on Evolutionary Computation 6(1): 58-73. https://doi.org/10.1109/4235.985692

[2] Gaing ZL. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion 19(2): 384-391. https://doi.org/10.1109/TEC.2003.821821

[3] Anderson PM, Fouad AA. (1995). Power system control and stability. IEEE Press. https://doi.org/10.1109/TSMC.1979.4310158

[4] Hadi S. (1999). Power System Analysis. Tata McGraw-Hill.

[5] Mamdani EH. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Human-Computer Studies 51(1): 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2

[6] Lee CC. (1990). Fuzzy logic in control systems. IEEE Transactions on Systems, Man and Cybernetics 20(2). https://doi.org/10.1109/21.52551

[7] Zadeh LA. (1965). Fuzzy Sets. Informat and Control 8(3): 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

[8] Chalmers BJ. (1992). Influence of saturation in brushless permanent-magnet motor drives. Electric Power Applications, IEE Proceedings B [see also IEE Proceedings-Electric Power Applications] 139(1): 51-52. https://doi.org/10.1049/ip-b.1992.0007

[9] Johnson CT. (1992). Experimental identification of friction and its compensation in precise, position controlled mechanisms. IEEE Transactions on Industry Applications 28(6): 1392-1398. https://doi.org/10.1109/28.175293

[10] Canudas C, Astrom KJ, Braun K. (1987). Adaptive friction compensation in DC-motor drives. IEEE Journal on Robotics and Automation RA-3(6): 1556-1561. https://doi.org/10.1109/ROBOT.1986.1087407

[11] Wishart MT, Harley RG. (1995). Identification and control of induction machines using artificial neural networks. IEEE Transactions on Industry Applications 31(3): 612-619. https://doi.org/10.1109/28.382123

[12] Kung YS, Liaw CM, Ouyang MS. (1995). Adaptive speed control for induction motor drives using neural networks. Industrial Electronics, Control, and Instrumentation 42(1): 25-32. https://doi.org/10.1109/IECON.1993.339083

[13] Kundur P. (1994). Power System Stability and Control. McGraw-Hill.

[14] Kundur P, Klein M, Rogers GJ, Zywno MS. (1989). Application of pss for enhancement of overall system stability. IEEE Power Engineering Review 9(5): 614-626. https://doi.org/10.1109/MPER.1989.4310703

[15] Toliyat HA, Sadeh J, Ghazi R. (1996). Design of augmented fuzzy logic power system stabilizers to enhance power systems stability. IEEE Transactions on Energy Conversion 11(1): 97-103. https://doi.org/10.1109/60.486582

[16] Anderson PM, Fouad AA. (1977). Power system control and stability. The Iowa State University Press. https://doi.org/10.1109/TSMC.1979.4310158

[17] Lee CC. (1990). Fuzzy logic in control systems. IEEE Transactions on Systems, Man, and Cybernetics 20(2). https://doi.org/10.1109/21.52551

[18] Jang JR, Sun C, Mijutani E. (2004). Neuro-Fuzzy and Soft Computing. Pearson Education.

[19] Yadiah N, Ganga Dinesh Kumar A, Bhattacharya JL. (2004). Fuzzy based coordinated controller for power system stability and voltage regulation. Electric Power Systems Research 69(2): 169-177. https://doi.org/10.1016/j.epsr.2003.08.008

[20] Nallathambi N, Neelakantan PN. (2004). Fuzzy logic based power system stabilizer. E-Tech 2004: 68-73. https://doi.org/10.1109/ETECH.2004.1353846

[21] Naceri A. (2002). Application of the advanced robust H2 and H∞ frequency control techniques on the AVR- PSS for SM. Ph.D. Dissertation, Dept. of Electrical Eng.

[22] Manoj J, Qureshi MF, Srivastav P. (2013). Design of adaptive grey fuzzy pid controller with variable prediction step-size for power system dynamic stability control and its on-line rule tuning. AMSE Journals, Series Advances C, (Automatic Control, Theory and Application) 68(1): 1-21.

[23] Manoj J, Qureshi MF, Srivastav P. (2013). Designing power system stabilizer for system damping for transient disturbances using grey ANFIS technique. AMSE Journals, Series Advances C (Automatic Control: Theory and Application) 68(2): 36-53.

[24] Dewangan DN, Manoj J, Qureshi MF, Banjare YP. (2012). Real-time fault diagnostic and rectification system for bearing vibration of steam turbine by using adaptive neuro-fuzzy inference system and genetic algorithm-a novel approach. AMSE Journals, Series Advances B (Signal Processing and Pattern Recognition) 55(1): 1-21.