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An intelligent tuning scheme with a master/slave approach for efficient control of the automatic voltage regulator

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Abstract

A new master/slave model driven, and an optimization algorithm-based proportional–integral–derivative (PID) plus second-order derivative (PIDD2) controller is proposed in this work for a stable and efficient operation of an automatic voltage regulator (AVR) system. In this context, an ideal reference model of Bode is used as a master model. The new improved optimization algorithm is constructed via integrating the Lévy flight mechanism into Runge–Kutta optimizing algorithm. This algorithm optimally tunes the PIDD2 controller with the aid of a cost function known as integral of squared error. The latter control mechanism forms the slave model. As the PIDD2 controller and the intelligent tuning algorithm attempt to follow the response dictated by the ideal reference model of the master model, a significant improvement is achieved for the efficiency and the stability of the AVR system. The proposed master/slave driven, and intelligent optimization algorithm-based PIDD2 control approach presents more excellent transient response (steady state error, rise time, settling time, peak time, percent overshoot), frequency response (gain margin, phase margin and bandwidth), robustness and stability. Nonideal conditions such as measurement noise and the saturation at the input of the generator in the AVR are also considered to demonstrate the efficacy of the proposed method. Furthermore, the existing fifty-eight techniques in the literature are also used for performance comparison in order to present the more excellent efficiency of the proposed method from a wider perspective.

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References

  1. Micev M, Ćalasan M, Oliva D (2021) Design and robustness analysis of an automatic voltage regulator system controller by using equilibrium optimizer algorithm. Comput Electr Eng 89:106930. https://doi.org/10.1016/j.compeleceng.2020.106930

    Article  Google Scholar 

  2. Boldea I (2005) Synchronous Generators. CRC Press

    Book  Google Scholar 

  3. Micev M, Ćalasan M, Ali ZM et al (2021) Optimal design of automatic voltage regulation controller using hybrid simulated annealing—Manta ray foraging optimization algorithm. Ain Shams Eng J 12:641–657. https://doi.org/10.1016/j.asej.2020.07.010

    Article  Google Scholar 

  4. Suid MH, Ahmad MA (2022) Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system. ISA Trans 128:265–286. https://doi.org/10.1016/j.isatra.2021.11.037

    Article  Google Scholar 

  5. Bingul Z, Karahan O (2018) A novel performance criterion approach to optimum design of PID controller using cuckoo search algorithm for AVR system. J Franklin Inst 355:5534–5559. https://doi.org/10.1016/j.jfranklin.2018.05.056

    Article  MathSciNet  MATH  Google Scholar 

  6. Dogruer T, Can MS (2022) Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm. Trans Inst Meas Control 44:1862–1873. https://doi.org/10.1177/01423312211066758

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Jumani TA, Mustafa MW, Hussain Z et al (2020) Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system. Alex Eng J 59:2429–2440. https://doi.org/10.1016/j.aej.2020.03.005

    Article  Google Scholar 

  9. Mosaad AM, Attia MA, Abdelaziz AY (2019) Whale optimization algorithm to tune PID and PIDA controllers on AVR system. Ain Shams Eng J 10:755–767. https://doi.org/10.1016/j.asej.2019.07.004

    Article  Google Scholar 

  10. Ozgenc B, Ayas MS, Altas IH (2022) Performance improvement of an AVR system by symbiotic organism search algorithm-based PID-F controller. Neural Comput Appl 34:7899–7908. https://doi.org/10.1007/s00521-022-06892-4

    Article  Google Scholar 

  11. Sahib MA (2015) A novel optimal PID plus second order derivative controller for AVR system. Eng Sci Technol Int J 18:194–206. https://doi.org/10.1016/j.jestch.2014.11.006

    Article  Google Scholar 

  12. Habib S, Abbas G, Jumani TA et al (2022) Improved whale optimization algorithm for transient response, robustness, and stability enhancement of an automatic voltage regulator system. Energies (Basel) 15:5037. https://doi.org/10.3390/en15145037

    Article  Google Scholar 

  13. Izci D, Ekinci S, Zeynelgil HL, Hedley J (2021) Fractional order PID design based on novel improved slime mould algorithm. Electr Power Comp Syst 49:901–918. https://doi.org/10.1080/15325008.2022.2049650

    Article  Google Scholar 

  14. Ekinci S, Izci D, Eker E, Abualigah L (2023) An effective control design approach based on novel enhanced aquila optimizer for automatic voltage regulator. Artif Intell Rev 56:1731–1762. https://doi.org/10.1007/s10462-022-10216-2

    Article  Google Scholar 

  15. Kumar M, Hote YV (2021) Maximum sensitivity-constrained coefficient diagram method-based PIDA controller design: application for load frequency control of an isolated microgrid. Electr Eng. https://doi.org/10.1007/s00202-021-01226-4

    Article  Google Scholar 

  16. Kumar M, Hote YV (2021) Robust PIDD2 controller design for perturbed load frequency control of an interconnected time-delayed power systems. IEEE Trans Control Syst Technol 29:2662–2669. https://doi.org/10.1109/TCST.2020.3043447

    Article  Google Scholar 

  17. Raju M, Saikia LC, Sinha N (2016) Automatic generation control of a multi-area system using ant lion optimizer algorithm based PID plus second order derivative controller. Int J Electr Power Energy Syst 80:52–63. https://doi.org/10.1016/j.ijepes.2016.01.037

    Article  Google Scholar 

  18. Mosaad AM, Attia MA, Abdelaziz AY (2018) Comparative performance analysis of AVR controllers using modern optimization techniques. Electr Power Comp Syst 46:2117–2130. https://doi.org/10.1080/15325008.2018.1532471

    Article  Google Scholar 

  19. Chatterjee S, Mukherjee V (2016) PID controller for automatic voltage regulator using teaching–learning based optimization technique. Int J Electr Power Energy Syst 77:418–429. https://doi.org/10.1016/j.ijepes.2015.11.010

    Article  Google Scholar 

  20. Duman S, Yörükeren N, Altaş İH (2016) Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system. Turk J Electr Eng Comput Sci 24:2387–2400. https://doi.org/10.3906/elk-1404-14

    Article  Google Scholar 

  21. Altbawi SMA, Bin MAS, Jumani TA et al (2021) Optimal design of Fractional order PID controller based automatic voltage regulator system using gradient-based optimization algorithm. J King Saud Univ—Eng Sci. https://doi.org/10.1016/j.jksues.2021.07.009

    Article  Google Scholar 

  22. Khan IA, Alghamdi AS, Jumani TA et al (2019) Salp swarm optimization algorithm-based fractional order PID controller for dynamic response and stability enhancement of an automatic voltage regulator system. Electronics (Basel) 8:1472. https://doi.org/10.3390/electronics8121472

    Article  Google Scholar 

  23. Bourouba B, Ladaci S, Schulte H (2019) Optimal design of fractional order PIλDμ controller for an AVR system using Ant Lion Optimizer. IFAC-PapersOnLine 52:200–205. https://doi.org/10.1016/j.ifacol.2019.11.304

    Article  Google Scholar 

  24. Gozde H, Taplamacioglu MC (2011) Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system. J Franklin Inst 348:1927–1946. https://doi.org/10.1016/j.jfranklin.2011.05.012

    Article  MATH  Google Scholar 

  25. Micev M, Ćalasan M, Oliva D (2020) Fractional order PID controller design for an AVR system using chaotic yellow saddle goatfish algorithm. Mathematics 8:1182. https://doi.org/10.3390/math8071182

    Article  Google Scholar 

  26. Ekinci S, Izci D, Hekimoglu B (2020) Henry gas solubility optimization algorithm based FOPID controller design for automatic voltage regulator. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE, pp 1–6

  27. Mohanty PK, Sahu BK, Panda S (2014) Tuning and assessment of proportional-integral-derivative controller for an automatic voltage regulator system employing local unimodal sampling algorithm. Electr Power Compon Syst 42:959–969. https://doi.org/10.1080/15325008.2014.903546

    Article  Google Scholar 

  28. Panda S, Sahu BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Franklin Inst 349:2609–2625. https://doi.org/10.1016/j.jfranklin.2012.06.008

    Article  MathSciNet  MATH  Google Scholar 

  29. Demirören A, Hekimoğlu B, Ekinci S, Kaya S (2019) Artificial electric field algorithm for determining controller parameters in AVR system. In: 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). pp 1–7

  30. Agwa A, Elsayed S, Ahmed M (2022) Design of optimal controllers for automatic voltage regulation using archimedes optimizer. Intell Autom Soft Comput 31:799–815. https://doi.org/10.32604/iasc.2022.019887

    Article  Google Scholar 

  31. Celik E (2018) Incorporation of stochastic fractal search algorithm into efficient design of PID controller for an automatic voltage regulator system. Neural Comput Appl 30:1991–2002. https://doi.org/10.1007/s00521-017-3335-7

    Article  Google Scholar 

  32. Munagala VK, Jatoth RK (2022) Improved fractional PIλDμ controller for AVR system using Chaotic black widow algorithm. Comput Electr Eng 97:107600. https://doi.org/10.1016/j.compeleceng.2021.107600

    Article  Google Scholar 

  33. Kose E (2020) Optimal control of AVR system with tree seed algorithm-based PID controller. IEEE Access 8:89457–89467. https://doi.org/10.1109/ACCESS.2020.2993628

    Article  Google Scholar 

  34. Pachauri N (2020) Water cycle algorithm-based PID controller for AVR. COMPEL—Int J Comput Mathe Electr Electron Eng 39:551–567. https://doi.org/10.1108/COMPEL-01-2020-0057

    Article  Google Scholar 

  35. Guvenc U, Yigit T, Isik AH, Akkaya I (2016) Performance analysis of biogeography-based optimization for automatic voltage regulator system. Turk J Electr Eng Comput Sci 24:1150–1162

    Article  Google Scholar 

  36. Mohamadwasel NB (2020) Rider optimization algorithm implemented on the AVR control system using MATLAB with FOPID. IOP Conf Ser Mater Sci Eng 928:032017. https://doi.org/10.1088/1757-899X/928/3/032017

    Article  Google Scholar 

  37. Ahmadianfar I, Heidari AA, Gandomi AH et al (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079. https://doi.org/10.1016/j.eswa.2021.115079

    Article  Google Scholar 

  38. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 World Congress on Nature and Biologically Inspired Computing, NABIC 2009-Proceedings. pp 210–214

  39. Barbosa RS, Machado JAT, Ferreira IM (2004) Tuning of PID controllers based on Bode’s ideal transfer function. Nonlin Dyn 38:305–321. https://doi.org/10.1007/s11071-004-3763-7

    Article  MATH  Google Scholar 

  40. Ekinci S, Izci D, Abu Zitar R et al (2022) Development of Lévy flight-based reptile search algorithm with local search ability for power systems engineering design problems. Neural Comput Appl 34:20263–20283. https://doi.org/10.1007/s00521-022-07575-w

    Article  Google Scholar 

  41. Mokeddem D, Mirjalili S (2020) Improved whale optimization algorithm applied to design PID plus second-order derivative controller for automatic voltage regulator system. J Chin Inst Eng 43:541–552. https://doi.org/10.1080/02533839.2020.1771205

    Article  Google Scholar 

  42. Sikander A, Thakur P, Bansal RC, Rajasekar S (2018) A novel technique to design cuckoo search based FOPID controller for AVR in power systems. Comput Electr Eng 70:261–274. https://doi.org/10.1016/j.compeleceng.2017.07.005

    Article  Google Scholar 

  43. Ekinci S, Izci D (2022) Enhanced reptile search algorithm with Lévy flight for vehicle cruise control system design. Evol Intell. https://doi.org/10.1007/s12065-022-00745-8

    Article  Google Scholar 

  44. Bhullar AK, Kaur R, Sondhi S (2020) Enhanced crow search algorithm for AVR optimization. Soft comput 24:11957–11987. https://doi.org/10.1007/s00500-019-04640-w

    Article  Google Scholar 

  45. Ekinci S, Demiroren A, Zeynelgil H, Hekimoğlu B (2020) An opposition-based atom search optimization algorithm for automatic voltage regulator system. J Faculty Eng Arch Gazi Univ 35:1141–1158. https://doi.org/10.17341/gazimmfd.598576

    Article  Google Scholar 

  46. Izci D, Ekinci S, Zeynelgil HL, Hedley J (2022) Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems. Trans Inst Meas Control 44:435–456. https://doi.org/10.1177/01423312211037967

    Article  Google Scholar 

  47. Bode HW (1945) Network analysis and feedback amplifier design. Van Nostrand

    Google Scholar 

  48. Azarmi R, Tavakoli-Kakhki M, Sedigh AK, Fatehi A (2016) Robust fractional order PI controller tuning based on Bode’s ideal transfer function. IFAC-PapersOnLine 49:158–163. https://doi.org/10.1016/j.ifacol.2016.07.519

    Article  Google Scholar 

  49. Li X, Wang Y, Li N et al (2017) Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization. Int J Mach Learn Cybern 8:1595–1605. https://doi.org/10.1007/s13042-016-0530-2

    Article  Google Scholar 

  50. Pradhan R, Majhi SK, Pradhan JK, Pati BB (2018) Antlion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system. J Ind Inf Integr 9:45–52. https://doi.org/10.1016/j.jii.2018.01.002

    Article  Google Scholar 

  51. Boussalem C, Mansouri R, Bettayeb M, Hamerlain M (2021) Fractional order integral controller design based on a Bode’s ideal transfer function: application to the control of a single tank process. pp 155–169

  52. Zhang L, Zhang Q, Wang W (2020) Application of ideal bode transfer function tuning fractional order PID in pressure difference of vertical mill. In: 2020 Chinese Control And Decision Conference (CCDC). IEEE, pp 3501–3505

  53. Frikh ML, Soltani F, Bensiali N et al (2021) Fractional order PID controller design for wind turbine systems using analytical and computational tuning approaches. Comput Electr Eng 95:107410. https://doi.org/10.1016/j.compeleceng.2021.107410

    Article  Google Scholar 

  54. Izci D, Ekinci S, Kayri M, Eker E (2022) A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode’s ideal transfer function based automobile cruise control system. Evol Syst 13:453–468. https://doi.org/10.1007/s12530-021-09402-4

    Article  Google Scholar 

  55. Yumuk E, Güzelkaya M, Eksin İ (2022) A robust fractional-order controller design with gain and phase margin specifications based on delayed Bode’s ideal transfer function. J Franklin Inst 359:5341–5353. https://doi.org/10.1016/j.jfranklin.2022.05.033

    Article  MathSciNet  MATH  Google Scholar 

  56. Izci D, Ekinci S, Hekimoğlu B (2022) A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system. Trans Inst Meas Control 44:393–409. https://doi.org/10.1177/01423312211036591

    Article  Google Scholar 

  57. Izci D, Ekinci S (2022) A novel improved version of hunger games search algorithm for function optimization and efficient controller design of buck converter system e-Prime. Adv Electr Eng Electron Energy 2:100039. https://doi.org/10.1016/j.prime.2022.100039

    Article  Google Scholar 

  58. Izci D, Ekinci S (2021) Comparative performance analysis of slime mould algorithm for efficient design of proportional–integral–derivative controller. Electrica 21:151–159. https://doi.org/10.5152/electrica.2021.20077

    Article  Google Scholar 

  59. Ekinci S, Hekimoglu B, Kaya S (2018) Tuning of PID controller for AVR system using salp swarm algorithm. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, pp 1–6

  60. Anbarasi S, Muralidharan S (2017) Intelligent tuning of proportional integral derivative controller using hybrid bacterial foraging particle swarm optimization for automatic voltage regulator system. Revue Roumaine des Sciences Techniques Serie Electrotechnique et Energetique 62:325–331

    Google Scholar 

  61. Ekinci S, Hekimoglu B (2019) Improved kidney-inspired algorithm approach for tuning of PID controller in AVR system. IEEE Access 7:39935–39947. https://doi.org/10.1109/ACCESS.2019.2906980

    Article  Google Scholar 

  62. Bakir H, Guvenc U, Tolga Kahraman H, Duman S (2022) Improved Lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design. Comput Ind Eng 168:108032. https://doi.org/10.1016/j.cie.2022.108032

    Article  Google Scholar 

  63. Lahcene R, Abdeldjalil S, Aissa K (2017) Optimal tuning of fractional order PID controller for AVR system using simulated annealing optimization algorithm. In: 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B). IEEE, pp 1–6

  64. Çelik E, Öztürk N (2018) A hybrid symbiotic organisms search and simulated annealing technique applied to efficient design of PID controller for automatic voltage regulator. Soft comput 22:8011–8024. https://doi.org/10.1007/s00500-018-3432-2

    Article  Google Scholar 

  65. Hekimoğlu B (2019) Sine-cosine algorithm-based optimization for automatic voltage regulator system. Trans Inst Meas Control 41:1761–1771. https://doi.org/10.1177/0142331218811453

    Article  Google Scholar 

  66. Hekimoğlu B, Ekinci S (2018) Grasshopper optimization algorithm for automatic voltage regulator system. In: 2018 5th International Conference on Electrical and Electronics Engineering, ICEEE 2018. pp 152–156

  67. Ma L, Hu C, Yu J et al (2022) Distributed fixed/preassigned-time optimization based on piecewise power-law design. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2022.3163623

    Article  Google Scholar 

  68. Hu C, Jiang H (2022) Special functions-based fixed-time estimation and stabilization for dynamic systems. IEEE Trans Syst Man Cybern Syst 52:3251–3262. https://doi.org/10.1109/TSMC.2021.3062206

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Batman University, Batman, 72100, Turkey

    Davut Izci & Serdar Ekinci

  2. Centre for Artificial Intelligence Research and Optimization, Torrens University, Adelaide, Australia

    Seyedali Mirjalili

  3. Yonsei Frontier Lab, Yonsei University, Seoul, South Korea

    Seyedali Mirjalili

  4. Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. MEU Research Unit, Middle East University, Amman, Jordan

    Davut Izci & Laith Abualigah

  6. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, 19328, Amman, Jordan

    Laith Abualigah

  7. Applied science research center, Applied science private university, 11931, Amman, Jordan

    Laith Abualigah

  8. School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia

    Laith Abualigah

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  1. Davut Izci
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Izci, D., Ekinci, S., Mirjalili, S. et al. An intelligent tuning scheme with a master/slave approach for efficient control of the automatic voltage regulator. Neural Comput & Applic 35, 19099–19115 (2023). https://doi.org/10.1007/s00521-023-08740-5

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