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2016 | OriginalPaper | Buchkapitel

Swarm Intelligence PID Controller Tuning for AVR System

verfasst von : Naglaa K. Bahgaat, M. A. Moustafa Hassan

Erschienen in: Advances in Chaos Theory and Intelligent Control

Verlag: Springer International Publishing

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Abstract

The voltage regulator is designed to automatically maintain a constant voltage level in the power system. It may be used to regulate one or more AC or DC voltages in power systems. Voltage regulator may be designed as a simple “feed-forward” or may include “negative feedback” control loops. Depending on the design, it may use an electromechanical mechanism, or electronic components. The role of an AVR is to keep constant the output voltage of the generator in a specified range. The PID controller can used to provide the control requirements.The chapter discusses the methods to get the best possible tuning controller parameters for an automatic voltage regulator (AVR) system of a synchronous generator. It was necessary to use PID controller to increase the stability margin and to improve performance of the system. Some modern techniques were defined. These techniques as Particle Swarm Optimization (PSO), also it illustrates the use of a Adaptive Weight Particle Swarm Optimization (AWPSO), Adaptive Acceleration Coefficients based PSO, (AACPSO), Adaptive Acceleration Coefficients based PSO (AACPSO). Furthermore, it introduces a new modification for AACPSO technique, Modified Adaptive Acceleration Coefficients based PSO (MAAPSO) is the new technique which will be discussed inside the chapter, A comparison between the results of all methods used will be given in this chapter. Simulation for comparison between the proposed methods will be displayed. The obtained results are promising.

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Allaoua B (2008) The efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control. Serbian J Electr Eng 5(2):247–262CrossRef

Azar AT, Serrano FE (2014) Robust IMC-PID tuning for cascade control systems with gain and phase margin specifications’, Neural Computing and Applications, 25(5): 983–995 Springer. doi:10.1007/s00521-014-1560-x

Azar AT (2010) Fuzzy systems. IN-TECH, Austria, Vienna. ISBN: 978-953-7619-92-3

Azar AT (2012) Overview of type-2 fuzzy logic systems. Int J Fuzzy Syst Appl (IJFSA) 2(4):1–28

Azar AT, Serrano FE (2015) Design and modeling of anti wind up PID controllers. In: Zhu Q, Azar AT (eds) Complex system modeling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer, Germany, pp 1–44 doi:10.1007/978-3-319-12883-2_1.1

Azar AT, Vaidyanathan S (2015a) Handbook of research on advanced intelligent control engineering and automation. Advances in computational intelligence and robotics (ACIR). Book Series, IGI Global, USA

Azar AT, Vaidyanathan S (2015c) Computational intelligence applications in modeling and control studies in computational intelligence, vol 575. Springer, Germany. ISBN: 978-3-319-11016-5

Azar AT, Vaidyanathan S (2015b) Chaos modeling and control systems design. Studies in computational intelligence, vol 581. Springer, Germany. ISBN: 978-3-319-13131-3

Bahgaat NK, El-Sayed MI, Moustafa Hassan MA, Bendary FA (2014) Load frequency control in power system via improving PID controller based on particle swarm optimization and ANFIS techniques. Int J Syst Dyn Appl (IJSDA). IGI-Global, USA 3(3):1–24

10.

Bahgaat NK, El-Sayed MI, Moustafa Hassan MA, Bendary FA (2015) Application of some modern techniques in load frequency control in power systems. In: Chaos Modeling and Control Systems Design, vol 581. Springer, Germany, pp 163–211

11.

Bahgaat NK (2013) Artificial intelligent based controller for frequency control in power systems, Ph.D. Thesis, Faculty of Engineering at Al-Azhar University, Cairo, Egypt

12.

Bevrani H (2009) Robust power system frequency control. Springer Science and Business Media LLC, Brisbane, Australia

13.

Bhati S, Nitnawwre D (2012) Genetic optimization tuning of an automatic voltage regulator system. Int J Sci Eng Technol 1:120–124

14.

Bhatt VK, Bhongade S (2013) Design of PID controller in automatic voltage regulator (AVR) system using PSO technique. Int J Eng Res Appl (IJERA) 3(4):1480–1485

15.

Cheng G (1997) Genetic algorithms and engineering design. Wiley, New York

16.

Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRef

17.

Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of IEEE International Conference on Evolution in Computing. Anchorage, AK, May, pp 611–616

18.

Fitch JW, Zachariah KJ, Farsi M (1999) Turbo generator self-tuning automatic voltage regulator. IEEE Trans Energy Convers 14(3):843–848CrossRef

19.

Gaing ZL (2004) A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 19(2):384–391CrossRef

20.

Hamid A, Abdul-Rahman TK (2010) Short term load forecasting using an artificial neural network trained by artificial immune system learning algorithm in computer modeling and simulation (UKSim). In: 12th international conference on IEEE, pp 408–4131

21.

Hassanien AE, Tolba M, Azar AT (2014) Advanced machine learning technologies and applications: second international conference, AMLTA 2014. Cairo, Egypt, 28–30 Nov 2014. In: Proceedings, communications in computer and information science, vol 488. GmbH, Springer, Heidelberg. ISBN: 978-3-319-13460-4

22.

Ingemar EO (1983) Electric energy systems theory. McGrawhill Book Company, London

23.

Ismail A (2006) Improving UAE power systems control performance by using combined LFC and AVR. In: 7th UAE University Research Conference on Engineering, pp 50–60

24.

Kennedy J, Eberhart RC (1995) A new optimizer using particle swarm theory. In: Proceedings of 6th international symposium micro machine human science, Nagoya, Japan, pp 39–43

25.

Khalifa F, Moustafa Hassan M, Abul-Haggag O, Mahmoud H (2015) The application of evolutionary computational techniques in medium term forecasting accepted for presentation at MEPCON’2015, Mansoura University, Mansoura, Egypt

26.

Kim DH, Cho JH (2006) A biologically inspired intelligent PID controller tuning for AVR systems. Int J Control Autom Syst 4:624–636

27.

Kumar A, Gupta R (2013) Compare the results of tuning of PID controller by using PSO and GA technique for AVR system. Int J Adv Res Comput Eng Technol (IJARCET) 2(6)

28.

Kumar DV (1998) Intelligent controllers for automatic generation control. In: IEEE Region, 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control, TENCON’98, vol 2, pp 557–574

29.

Musa BU, Kalli BM, Kalli S (2013) Modeling and simulation of LFC and AVR with PID controller. Int J Eng Sci Invent 2:54–57

30.

Naik RS, ChandraSekhar K, Vaisakh K (2005) Adaptive PSO based optimal fuzzy controller design for AGC equipped with SMES and SPSS. J Theor Appl Inf Technol 7(1):008–017

31.

RamaSudha K, Vakula VS, Shanthi RV (2010) PSO based design of robust controller for two area load frequency control with nonlinearities. Int J Eng Sci 2(5):1311–1324

32.

Rania HM (2012) Development of advanced controllers using adaptive weighted PSO algorithm with applications, M.Sc. Thesis, Faculty of Engineering, Cairo University, Cairo, Egypt

33.

Shabib G, Abdel Gayed M, Rashwan AM (2010) Optimal tuning of PID controller for AVR system using modified particle swarm optimization. In: Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10), Cairo University, Egypt

34.

Skogestad S (2003) Simple analytic rules for model reduction and PID controller tuning. J Process Control 13(4):291–309MathSciNetCrossRef

35.

Soundarrajan A, Sumathi S (2010) Particle swarm optimization based LFC and AVR of autonomous power generating system. IAENG Int J Comput Sci

36.

Swidenbank E, Brown MD, Flynn D (1999) Self-tuning turbine generator control for power plant. Mechatronics 9:513–537CrossRef

37.

Tammam MA (2011) Multi objective genetic algorithm controllers tuning for load frequency control in electric power systems, M.Sc. Thesis, Faculty of Engineering at Cairo University, Cairo, Egypt

38.

Vlachogiannis JG, Lee KY (2009) Economic load dispatch—acomparative study on heuristic optimization techniques with an improved coordinated aggregation based PSO. IEEE Trans Power Syst 24(2):991–1001CrossRef

39.

Wong CC, An Li S, Wang H (2009) Optimal PID controller design for AVR system. Tamkang J Sci Eng 12(3):259–270

40.

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

41.

Zhu Q, Azar AT (2015) Complex system modelling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer, Germany. ISBN: 978-3-319-12882-5

Titel: Swarm Intelligence PID Controller Tuning for AVR System
verfasst von: Naglaa K. Bahgaat
M. A. Moustafa Hassan
Verlag: Springer International Publishing
Buch: Advances in Chaos Theory and Intelligent Control
Print ISBN: 978-3-319-30338-3

Electronic ISBN: 978-3-319-30340-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-30340-6_33

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