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Fitness distance balance-based Runge–Kutta algorithm for indirect rotor field-oriented vector control of three-phase induction motor

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Abstract

In this article, a study has been carried out to further develop the Runge–Kutta (RK) algorithm, which has a current and robust mathematical structure, using the fitness distance balance (FDB) method, and to test it for induction machine control. The RK algorithm was developed to avoid local optimum solutions, speed up convergence, and seek out the best possible solutions globally. Despite offering promising solutions, it is clear that this algorithm has its shortcomings, especially in solving high-dimensional problems like asynchronous motor control. In this study, the FDB method was used to build the guide selection process in the RK algorithm to reach the optimal solution. The developed FDB-based RK algorithm has been tested and verified on the CEC17 benchmark problems for 30-dimensional search spaces. The results of the proposed algorithm have been compared to the performance of the classical RK algorithm, and it shows that the changes in the design of the RK algorithm are successful. The proportional–integral–derivative (PID) parameters employed as a controller in the indirect rotor field-oriented control approach of a three-phase induction motor have then been optimized using the accuracy-proven algorithm. The FDB-RK, RK, genetic algorithm, particle swarm (PSO), differential evolution, artificial bee colony, and weighted average of vectors (INFO) algorithms have been used in this study with three different fitness functions and Wilcoxon and Freidman statistical analyses to find the best values for PID parameters. According to the data, FDB-RK-based PID controller has the best performance among the techniques.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Bose BK (2008) Power electronics and motor drives recent progress and perspective. IEEE Trans Ind Electron 56(2):581–588

    Article  Google Scholar 

  2. Smith AC, Healey RC, Williamson S (1996) A transient induction motor model including saturation and deep bar effect. IEEE Trans Energy Convers 11(1):8–15

    Article  Google Scholar 

  3. Healey RC, Williamson S, Smith AC (1995) Improved cage rotor models for vector controlled induction motors. IEEE Trans Ind Appl 31(4):812–822

    Article  Google Scholar 

  4. Levi E (1997) Impact of cross-saturation on accuracy of saturated induction machine models. IEEE Trans Energy Convers 12(3):211–216

    Article  Google Scholar 

  5. Boldea I, Nasar A (2001) The induction machine handbook. CRC Press, London

    Google Scholar 

  6. Yadav M, Tayal VK (2019) Performance enhancement of induction motor using PID controller with PID tuner. In: Advances in interdisciplinary Engineering. pp 783–793. Springer, Singapore

  7. Yao L, Lin CC (2009) On a genetic algorithm based scheduled fuzzy PID controller. Int J Innov Comput Inf Control 5(10):3593–3602

    Google Scholar 

  8. Gawthrop PJ, Nomikos PE (1990) Automatic tuning of commercial PID controllers for single-loop and multiloop applications. IEEE Control Syst Mag 10(1):34–42

    Article  Google Scholar 

  9. Lequin O, Gevers M, Mossberg M, Bosmans E, Triest L (2003) Iterative feedback tuning of PID parameters: comparison with classical tuning rules. Control Eng Pract 11(9):1023–1033

    Article  Google Scholar 

  10. Xie F, Wang Q-j, Li G-l (2012) Optimization research of FOC based on PSO of induction motors. 2012 15th international conference on electrical machines and systems (ICEMS). pp 1–4

  11. Beheshti Z, Shamsuddin SMH (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5(1):1–35

    Google Scholar 

  12. Fadhil G, Abed I, Jasim R (2021) Genetic algorithm utilization to fine tune the parameters of PID controller. Kufa J Eng 12(2):1–12

    Article  Google Scholar 

  13. Guedes JJ, Castoldi MF, Goedtel A, Agulhari CM, Sanches DS (2018) Parameters estimation of three-phase induction motors using differential evolution. Electr Power Syst Res 154:204–212

    Article  Google Scholar 

  14. Arslan M, Çunkaş M, Sağ T (2012) Determination of induction motor parameters with differential evolution algorithm. Neural Comput Appl 21(8):1995–2004

    Article  Google Scholar 

  15. Guedes JJ, Castoldi MF, Goedtel A, Agulhari CM, Sanches DS (2019) Differential evolution applied to line-connected induction motors stator fault identification. Soft Comput 23(21):11217–11226

    Article  Google Scholar 

  16. Saraçoğlu B, Güvenç U, Dursun M, Poyraz G, Duman S (2013) Biyocağrafya tabanli optimizasyon metodu kullanarak asenkron motor parametre tahmini. İleri Teknol Bilim Derg 2(1):46–54

    Google Scholar 

  17. Rarick R, Simon D, Villaseca FE, Vyakaranam B (2009) Biogeography-based optimization and the solution of the power flow problem. In: 2009 IEEE international conference on systems, man and cybernetics. pp 1003–1008. IEEE

  18. Ebrahim EA (2014) Artificial bee colony-based design of optimal on-line self-tuning PID-controller fed AC drives. Int J Eng Res 3(12):807–811

    Google Scholar 

  19. Aminu M (2019) A parameter estimation algorithm for induction machines using artificial bee colony (ABC) optimization. Niger J Technol 38(1):193–201

    Article  Google Scholar 

  20. Sharma AK, Patidar NP, Agnitotri G, Palwalia DK (2013) Performance analysis of self excited induction generator using artificial bee colony algorithm. In: 2013 international conference on electrical, electronics and system engineering (ICEESE). pp. 108–113. IEEE

  21. El-Telbany ME (2013) Tuning PID controller for DC motor: an artificial bees optimization approach. Int J Comput Appl. 77(15)

  22. Goel R, Maini R (2018) A hybrid of ant colony and firefly algorithms (HAFA) for solving vehicle routing problems. J Comput Sci 25:28–37

    Article  MathSciNet  Google Scholar 

  23. Aydilek IB (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Soft Comput 66:232–249

    Article  Google Scholar 

  24. Banerjee T, Chowdhuri S, Sarkar G, Bera J (2012) Performance comparison between GA and PSO for optimization of PI and PID controller of direct FOC induction motor drive. Int J Sci Res Publ 2(7):1–8

    Google Scholar 

  25. Boukhalfa G, Belkacem S, Chikhi A, Benaggoune S (2019) Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor. J Cent South Univ 26(7):1886–1896

    Article  Google Scholar 

  26. Zemmit A, Messalti S, Harrag A (2018) A new improved DTC of doubly fed induction machine using GA-based PI controller. Ain Shams Eng J 9(4):1877–1885

    Article  Google Scholar 

  27. Bekakra Y, Labbi Y, Ben Attous D, Malik OP (2021) Rooted tree optimization algorithm to improve DTC response of DFIM. J Electr Eng Technol 16(5):2463–2483

    Article  Google Scholar 

  28. Çelik E, ÖZTÜRK N (2017) Doğru akım motor sürücüleri için PI parametrelerinin simbiyotik organizmalar arama algoritması ile optimal ayarı. Bilişim Teknol Derg 10(3):311–318

    Article  Google Scholar 

  29. Dhieb Y, Yaich M, Guermazi A, Ghariani M (2019) PID controller tuning using ant colony optimization for induction motor. J Electr Syst 15(1):133–141

    Google Scholar 

  30. Merugumalla MK, Kumar NP (2017) Optimized PID controller for BLDC motor using nature-inspired algorithms. Int J Appl Eng Res 12(1):2017–2415

    Google Scholar 

  31. Cengiz E, Yılmaz C, Kahraman HT, Suiçmez Ç (2021) Improved Runge Kutta optimizer with fitness distance balance-based guiding mechanism for global optimization of high-dimensional problems. Duzce Univ J Sci Technol 9(6):135–149

    Google Scholar 

  32. Kahraman HT, Aras S, Gedikli E (2020) Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl-Based Syst 190:105169

    Article  Google Scholar 

  33. Aras S, Gedikli E, Kahraman HT (2021) A novel stochastic fractal search algorithm with fitness-distance balance for global numerical optimization. Swarm Evol Comput 61:100821

    Article  Google Scholar 

  34. Sharifi MR, Akbarifard S, Qaderi K, Madadi MR (2021) Developing MSA algorithm by new fitness-distance-balance selection method to optimize cascade hydropower reservoirs operation. Water Resour Manag 35(1):385–406

    Article  Google Scholar 

  35. Guvenc U, Duman S, Kahraman HT, Aras S, Katı M (2021) Fitness-distance balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources. Appl Soft Comput 108:107421

    Article  Google Scholar 

  36. Kim JH, Choi JW, Sul SK (2002) Novel rotor-flux observer using observer characteristic function in complex vector space for field-oriented induction motor drives. IEEE Trans Ind Appl 38(5):1334–1343

    Article  Google Scholar 

  37. Wang L, Yang B, Orchard J (2016) Particle swarm optimization using dynamic tournament topology. Appl Soft Comput 48:584–596

    Article  Google Scholar 

  38. Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079

    Article  Google Scholar 

  39. Le DT, Bui DK, Ngo TD, Nguyen QH, Nguyen-Xuan H (2019) A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures. Comput Struct 212:20–42

    Article  Google Scholar 

  40. Qin Q, Cheng S, Zhang Q, Li L, Shi Y (2015) Particle swarm optimization with interswarm interactive learning strategy. IEEE Trans Cybern 46(10):2238–2251

    Article  Google Scholar 

  41. Al-Bahrani LT, Patra JC (2018) A novel orthogonal PSO algorithm based on orthogonal diagonalization. Swarm Evol Comput 40:1–23

    Article  Google Scholar 

  42. Bakir H, Guvenc U, Kahraman HT, Duman S (2022) Improved lévy flight distribution algorithm with FDB-based guiding mechanism for AVR system optimal design. Comput Ind Eng 168:108032

    Article  Google Scholar 

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  1. Department of Electrical and Electronics Engineering, Engineering Faculty, Duzce University, Düzce, Turkey

    Mustafa Dursun

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  1. Mustafa Dursun
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Correspondence to Mustafa Dursun.

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Dursun, M. Fitness distance balance-based Runge–Kutta algorithm for indirect rotor field-oriented vector control of three-phase induction motor. Neural Comput & Applic 35, 13685–13707 (2023). https://doi.org/10.1007/s00521-023-08408-0

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