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Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

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Abstract

Automatic voltage regulator (AVR) is an equipment maintaining the terminal voltage of generators to a specific level all the time and under any load conditions. Many controllers for AVR system are designed based on a linearized normal model of AVR system and they are not robust enough against uncertainties such as parameter variation and load change in the system. In this paper, a gray PID (GPID) controller is designed for AVR system. The GPID controller consists of two parts, i.e. a conventional PID controller together with a gray compensation controller. In GPID controller, gray GM (0, N) model is used to estimate the uncertainties online, and a gray compensation controller is constructed according the estimation results to eliminate the effect of uncertainties. To further improve its performance, the GPID controller’s parameters are optimized through a new evolutionary algorithm, i.e., imperialist competitive algorithm (ICA). The proposed GPID controller can effectively deal with the uncertainties in AVR system. Simulation results illustrate its effectiveness of the proposed control scheme.

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References

  1. Aguila-Camacho N, Duarte-Mermoud MA (2013) Fractional adaptive control for an automatic voltage regulator. ISA Trans 52(6):807–815

    Article  Google Scholar 

  2. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congr Evol Comput 2007:4661–4667

    Google Scholar 

  3. Chatterjee A, Mukherjee V, Ghoshal S (2009) Velocity relaxed and craziness-based swarm optimized intelligent PID and PSS controlled AVR system. Int J Electr Power Energy Syst 31(7):323–333

    Article  Google Scholar 

  4. Chiang HK, Tseng CH (2004) Design and implementation of a grey sliding mode controller for synchronous reluctance motor drive. Control Eng Pract 12:155–163

    Article  Google Scholar 

  5. Chiang HK, Tseng CH (2004) Design and implementation of a grey sliding mode controller for synchronous reluctance motor drive. Control Eng Pract 12(2):155–163

    Article  Google Scholar 

  6. Chou CH (2003) Synthesized variable structure control and gray prediction for a class of perturbed systems. ISA Trans 42(2):241–250

    Article  Google Scholar 

  7. Chung CC, Chen HH, Ting CH (2010) Grey prediction fuzzy control for pH processes in the food industry. J Food Eng 96(4):575–582

    Article  Google Scholar 

  8. Demello FP, Concordia C (1969) Concepts of synchronous machine stability as affected by excitation control. IEEE Trans Power App Syst 88(4):316–328

    Article  Google Scholar 

  9. Deng JL (1982) Control problems of grey systems. Syst Control Lett 5:288–294

    MATH  Google Scholar 

  10. Devaraj D, Selvabala B (2009) Real-coded genetic algorithm and fuzzy logic approach for real-time tuning of proportional-integral-derivative controller in automatic voltage regulator system. IET Gener Transm Distrib 3(7):641–649

    Article  Google Scholar 

  11. Erenturk K (2010) Gray-fuzzy control of a nonlinear two-mass system. J Frankl Inst 374:1171–1185

    Article  MathSciNet  MATH  Google Scholar 

  12. Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19:384–391

    Article  Google Scholar 

  13. Gizi AJA, Mustafa M, Al-geelani NA, Alsaedi MA (2015) Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation. Appl Soft Comput 28:226–236

    Article  Google Scholar 

  14. Gozde H, Taplamacioglu MC (2011) Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. J Frankl Inst 348:1927–1946

    Article  MATH  Google Scholar 

  15. Khezri R, Bevrani H (2015) Voltage performance enhancement of DFIG-based wind farms integrated in large-scale power systems: Coordinated AVR and PSS. Int J Electr Power Energy Syst 73:400–410

    Article  Google Scholar 

  16. Krohling, R., Jaschek, H., Rey, J.: Designing PI/PID controllers for a motion control system based on genetic algorithms. In: Proceedings of the 1997 IEEE International Symposium on Intelligent Control 1997, pp 125–130 (1997)

  17. Krohling RA, Rey JP (2001) Design of optimal disturbance rejection PID controllers using a genetic algorithm. IEEE Trans Evol Comput 5:78–82

    Article  Google Scholar 

  18. Lu HC (2004) Grey prediction approach for designing grey sliding mode controller. IEEE Int Conf Syst Man Cybern 1:403–408

    Google Scholar 

  19. Mukherjee V, Ghoshal SP (2007) Intelligent particle swarm optimized fuzzy PID controller for AVR system. Int J Electr Power Energy Syst 77:1689–1698

    Article  Google Scholar 

  20. Panda S, Sahub BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Frankl Inst 349:2609–2625

    Article  MathSciNet  MATH  Google Scholar 

  21. Shayeghi H, Younesi A, Hashemi Y (2015) Optimal design of a robust discrete parallel FP + FI + FD controller for the automatic voltage regulator system. Int J Electr Power Energy Syst 67:66–75

    Article  Google Scholar 

  22. Wang X, He Q, Chen D, Yeung D (2005) A genetic algorithm for solving the inverse problem of support vector machines. Neurocomputing 68:225–238

    Article  Google Scholar 

  23. Wang X, He Y, Dong L, Zhao H (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  24. Wu Q, Hogg B (1988) Robust self-tuning regulator for a synchronous generator. IEE P-Contr Theor Ap 135(6):463–473

    Article  MathSciNet  MATH  Google Scholar 

  25. Yoshida H, Kawata K, Fukuyama Y (2000) A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans Power Syst 15:1232–1239

    Article  Google Scholar 

  26. Zhang, Y., Jiao, Z.: Study of grey PID control for discrete system. In: International Workshop on Intelligent Systems and Applications 2009 pp 1–4. IEEE (2009)

Download references

Acknowledgments

This manuscript is supported partly by the National Natural Science Foundation of China (No. 61273260), Natural Science Foundation of Hebei Province (No. F2014203208), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20121333120010), China Postdoctoral Science Foundation (Nos. 2013M530888, 2014T70229) and Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China (No. SCIP2012008).

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Authors and Affiliations

  1. Institute of Electrical Engineering, Yanshan University, 066004, Qinhuangdao, Hebei, China

    Yinggan Tang, Liheng Zhao, Zhenzhen Han & Xiangwei Bi

  2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, 066004, Qinhuangdao, Hebei, China

    Yinggan Tang, Liheng Zhao, Zhenzhen Han & Xiangwei Bi

  3. Key Laboratory of System Control and Information Processing, Ministry of Education, 200240, Shanghai, China

    Xinping Guan

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  1. Yinggan Tang
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Correspondence to Yinggan Tang.

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Tang, Y., Zhao, L., Han, Z. et al. Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm. Int. J. Mach. Learn. & Cyber. 7, 229–240 (2016). https://doi.org/10.1007/s13042-015-0431-9

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  • DOI: https://doi.org/10.1007/s13042-015-0431-9

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