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An advanced intelligent MPPT control strategy based on the imperialist competitive algorithm and artificial neural networks

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Abstract

The development of a robust optimization control technique, which can handle numerous nonlinear system problems, is one of the most challenging aspects of science production. In this study, hybrid intelligent Maximum Power Point Tracking (MPPT) control approach based on the Imperialist Competitive Algorithm (ICA), and an adaptive Artificial Neural Network (ANN) model is proposed to solve the efficiency optimization problem of Photovoltaic (PV) systems, which are considered as one of the most demanded source energy in the world. Consequently, a comparison between various metaheuristic algorithms based ANN including, Particle Swarm Optimization, Grey Wolf Optimization, and Whale Optimization Algorithm, is made for four distinct PV panel architectures to prove the effectiveness of the suggested approach in the optimization process based ICA technique, and in the training phase based three training algorithms namely, Bayesian Regularization (BR), Levenberg Marquardt (LM), and Scaled Conjugate Gradient (SCG). Accordingly, the obtained outcomes have proven that the ICA–ANN approach-based BR algorithm outperformed in three of four cases the other techniques by reaching an accuracy that can go up to 99.9994%. In the second part of this study, the evaluation of the obtained findings confirmed that our proposed model was able to track the Maximum Power Point (MPP) faster with a response time between 1.9 and 9.6 ms, and efficiency higher than 99.9652%, which is can up to 99.9984%, and it has shown excellent remarkable stability compared to the Perturb & Observe, Incremental Conductance, and the most applied metaheuristic-based MPPT techniques that we have used to conduct the optimization performance comparison.

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References

  1. Abdollahi M, Isazadeh A, Abdollahi D (2013) Imperialist competitive algorithm for solving systems of nonlinear equations. Comput Math Appl 65(12):1894–1908

    MathSciNet  Google Scholar 

  2. Al-Shahri OA, Ismail FB, Hannan M et al (2021) Solar photovoltaic energy optimization methods, challenges and issues: a comprehensive review. J Clean Prod 284(125):465

    Google Scholar 

  3. Al-Showany EFA (2016) The impact of the environmental condition on the performance of the photovoltaic cell. Am J Energy Eng 4(1):1–7

    Google Scholar 

  4. Alonso-Montesinos J, Ballestrín J, López G et al (2021) The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction. J Clean Prod 285(125):395

    Google Scholar 

  5. AlZubaidi AA, Khaliq LA, Hamad HS et al (2022) MPPT implementation and simulation using developed P&O algorithm for photovoltaic system concerning efficiency. Bull Electr Eng Inform 11(5):2460–2470

    Google Scholar 

  6. Anzalchi A, Sarwat A (2015) Artificial neural network based duty cycle estimation for maximum power point tracking in photovoltaic systems. In: SoutheastCon 2015, IEEE, pp 1–5

  7. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, IEEE, pp 4661–4667

  8. Baimel D, Tapuchi S, Levron Y et al (2019) Improved fractional open circuit voltage MPPT methods for PV systems. Electronics 8(3):321

    Google Scholar 

  9. Bharath K et al (2019) A novel sensorless hybrid MPPT method based on FOCV measurement and P&O MPPT technique for solar PV applications. In: 2019 international conference on advances in computing and communication engineering (ICACCE), IEEE, pp 1–5

  10. Burden F, Winkler D (2008) Bayesian regularization of neural networks. Methods Mol Biol 458:25–44

    Google Scholar 

  11. Chander S, Purohit A, Sharma A et al (2015) Impact of temperature on performance of series and parallel connected mono-crystalline silicon solar cells. Energy Rep 1:175–180

    Google Scholar 

  12. Chellaswamy C, Shaji M, Jawwad M et al (2019) A novel optimization method for parameter extraction of industrial solar cells. In: 2019 innovations in power and advanced computing technologies (i-PACT), IEEE, pp 1–6

  13. Chen H, Cui Y, Zhao Y et al (2018) Comparison of P&O and INC methods in maximum power point tracker for PV systems. In: IOP conference series: materials science and engineering, IOP Publishing, pp 072029

  14. Collins JW, Marcus HJ, Ghazi A et al (2022) Ethical implications of AI in robotic surgical training: A Delphi consensus statement. Eur Urol Focus 8(2):613–622

    Google Scholar 

  15. Cortés B, Sánchez RT, Flores JJ (2020) Characterization of a polycrystalline photovoltaic cell using artificial neural networks. Sol Energy 196:157–167

    Google Scholar 

  16. Day J, Senthilarasu S, Mallick TK (2019) Improving spectral modification for applications in solar cells: a review. Renew Energy 132:186–205

    Google Scholar 

  17. Deotti L, Silva Júnior I, Honório L et al (2021) Empirical models applied to distributed energy resources—an analysis in the light of regulatory aspects. Energies 14(2):326

    Google Scholar 

  18. Dhass AD, Kumar RS, Lakshmi P et al (2020) An investigation on performance analysis of different PV materials. Mater Today: Proc 22:330–334

    Google Scholar 

  19. Dhass AD, Kumar RS, Lakshmi P et al (2020) An investigation on performance analysis of different PV materials. Mater Today: Proc 22:330–334

    Google Scholar 

  20. Elmelegi A, Aly M, Ahmed EM et al (2019) A simplified phase-shift PWM-based feedforward distributed MPPT method for grid-connected cascaded PV inverters. Sol Energy 187:1–12

    Google Scholar 

  21. Farh HM, Eltamaly AM, Othman MF (2018) Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system. PLoS ONE 13(11):e0206,171

    Google Scholar 

  22. Forouzanfar M, Dajani H, Groza V et al (2010) Comparison of feed-forward neural network training algorithms for oscillometric blood pressure estimation. In: 4th international workshop on soft computing applications, IEEE, pp 119–123

  23. Furkan D, Mehmet Emin M (2010) Critical factors that affecting efficiency of solar cells. Smart grid and renewable energy 1(1):47–50

  24. Fürnkranz J, Chan P, Craw S et al (2010) Mean squared error. Springer Science & Business Media, Encyclopedia of machine learning Sammut, p 653

  25. Gouabi H, Hazzab A, Habbab M et al (2021) Experimental implementation of a novel scheduling algorithm for adaptive and modified P&O MPPT controller using fuzzy logic for WECS. Int J Adapt Control Signal Process 35(9):1732–1753

    Google Scholar 

  26. Huang XL, Ma X, Hu F (2018) Machine learning and intelligent communications. Mobile Netw Appl 23(1):68–70

    Google Scholar 

  27. Işcan B (2020) Ann modeling for justification of thermodynamic analysis of experimental applications on combustion parameters of a diesel engine using diesel and safflower biodiesel fuels. Fuel 279(118):391

    Google Scholar 

  28. Jain A, Sharma S, Kapoor A (2006) Solar cell array parameters using lambert w-function. Sol Energy Mater Sol Cells 90(1):25–31

    Google Scholar 

  29. Jiang LL, Srivatsan R, Maskell DL (2018) Computational intelligence techniques for maximum power point tracking in PV systems: a review. Renew Sustain Energy Rev 85:14–45

    Google Scholar 

  30. Kayri M (2016) Predictive abilities of Bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl 21(2):20

    MathSciNet  Google Scholar 

  31. Kollimalla SK, Mishra MK (2013) A new adaptive P&O MPPT algorithm based on FSCC method for photovoltaic system. In: 2013 international conference on circuits, power and computing technologies (ICCPCT), IEEE, pp 406–411

  32. Kouro S, Leon JI, Vinnikov D et al (2015) Grid-connected photovoltaic systems: an overview of recent research and emerging PV converter technology. IEEE Ind Electron Mag 9(1):47–61

    Google Scholar 

  33. Kumar C, Rao RS (2016) A novel global MPP tracking of photovoltaic system based on whale optimization algorithm. Int J Renew Energy Dev 5(3)

  34. Kumar KK, Bhaskar R, Koti H (2014) Implementation of MPPT algorithm for solar photovoltaic cell by comparing short-circuit method and incremental conductance method. Procedia Technol 12:705–715

    Google Scholar 

  35. Kumar MV, Mogili AR, Anusha S et al (2021) A new fuzzy based INC-MPPT algorithm for constant power generation in PV systems. Int Res J Eng Technol 8:212–217

    Google Scholar 

  36. Kumar V, Kumar A, Dhasmana H et al (2018) Efficiency enhancement of silicon solar cells using highly porous thermal cooling layer. Energy Environ 29(8):1495–1511

    Google Scholar 

  37. LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Google Scholar 

  38. Majid Z, Ruslan M, Sopian K et al (2014) Study on performance of 80 watt floating photovoltaic panel. J Mech Eng Sci 7(1):1150–1156

    Google Scholar 

  39. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  40. Modest MF (2003) Fundamentals of thermal radiation. Radiative heat transfer, pp 1–29

  41. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Google Scholar 

  42. Motahhir S, El Hammoumi A, El Ghzizal A (2020) The most used MPPT algorithms: review and the suitable low-cost embedded board for each algorithm. J Clean Prod 246(118):983

    Google Scholar 

  43. Nayak S, Kumar N, Choudhury B (2017) Scaled conjugate gradient backpropagation algorithm for selection of industrial robots. Int J Comput Appl (2250-1797) 7(6)

  44. Ncir N, El Akchioui N (2022) An intelligent improvement based on a novel configuration of artificial neural network model to track the maximum power point of a photovoltaic panel. J Control Autom Electr Syst

  45. Ncir N, Sebbane S, El Akchioui N (2022) A novel intelligent technique based on metaheuristic algorithms and artificial neural networks: application on a photovoltaic panel. In: 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), IEEE, pp 1–8

  46. Nishioka K, Takamoto T, Agui T et al (2006) Evaluation of INGAP/INGAAS/GE triple-junction solar cell and optimization of solar cell’s structure focusing on series resistance for high-efficiency concentrator photovoltaic systems. Sol Energy Mater Sol Cells 90(9):1308–1321

    Google Scholar 

  47. Noamane N, Saliha S, El Akchioui N (2022) Comparison of the efficiency of ANN training algorithms for tracking the maximum power point of photovoltaic field. In: International conference on electrical systems and automation. Springer, Berlin, pp 21–31

  48. Pan H, Niu X, Li R et al (2020) Annealed gradient descent for deep learning. Neurocomputing 380:201–211

    Google Scholar 

  49. Pranava G, Prasad P (2013) Constriction coefficient particle swarm optimization for economic load dispatch with valve point loading effects. In: 2013 international conference on power, energy and control (ICPEC), IEEE, pp 350–354

  50. Puig-Arnavat M, Bruno JC (2015) Artificial neural networks for thermochemical conversion of biomass. In: Recent advances in thermo-chemical conversion of biomass. Elsevier, pp 133–156

  51. Rahman I, Vasant PM, Singh BSM et al (2016) On the performance of accelerated particle swarm optimization for charging plug-in hybrid electric vehicles. Alex Eng J 55(1):419–426

    Google Scholar 

  52. Ramezani M, Bahmanyar D, Razmjooy N (2021) A new improved model of marine predator algorithm for optimization problems. Arab J Sci Eng 46(9):8803–8826

    Google Scholar 

  53. Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J Control Autom Electr Syst 27(4):419–440

    Google Scholar 

  54. Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network-world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16

    Google Scholar 

  55. Razmjooy N, Estrela VV, Loschi HJ (2019) A study on metaheuristic-based neural networks for image segmentation purposes. In: Data science. CRC Press, Boca Raton, pp 25–49

  56. Reichsthaler L, Madreiter T, Giner J et al (2022) An AI-enhanced approach for optimizing life cycle costing of military logistic vehicles. In: Procedia CIRP 105:296–301. The 29th CIRP conference on life cycle engineering, April 4–6, 2022, Leuven, Belgium

  57. Salam Z, Ahmed J, Merugu BS (2013) The application of soft computing methods for MPPT of PV system: a technological and status review. Appl Energy 107:135–148

    Google Scholar 

  58. Sariev E, Germano G (2020) Bayesian regularized artificial neural networks for the estimation of the probability of default. Quant Finance 20(2):311–328

    MathSciNet  Google Scholar 

  59. Sariev E, Germano G (2020) Bayesian regularized artificial neural networks for the estimation of the probability of default. Quant Finance 20(2):311–328

    MathSciNet  Google Scholar 

  60. Sebbane S, Ncir N, El Akchioui N (2022a) Diagnosis and classification of photovoltaic panel defects based on a hybrid intelligent method. In: International conference on electrical systems and automation, Springer, pp 59–69

  61. Sebbane S, Ncir N, El Akchioui N (2022b) Performance study of artificial neural network training algorithms for classifying PV field defects. In: 2022 2nd international conference on innovative research in applied science, engineering and technology (IRASET), IEEE, pp 1–5

  62. Sedaghati F, Nahavandi A, Badamchizadeh MA et al (2012) PV maximum power-point tracking by using artificial neural network. In: Mathematical problems in engineering

  63. Seyedmahmoudian M, Horan B, Soon TK et al (2016) State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems—a review. Renew Sustain Energy Rev 64:435–455

    Google Scholar 

  64. Shukla A, Kant K, Sharma A et al (2017) Cooling methodologies of photovoltaic module for enhancing electrical efficiency: a review. Sol Energy Mater Sol Cells 160:275–286

    Google Scholar 

  65. Sikora A, Zielonka A, Woźniak M (2021) Heuristic optimization of 18-pulse rectifier system. In: 2021 IEEE congress on evolutionary computation (CEC), IEEE, pp 673–680

  66. Sikora A, Zielonka A, Woźniak M (2022) Minimization of energy losses in the BLDC motor for improved control and power supply of the system under static load. Sensors 22(3):1058

    Google Scholar 

  67. Simsek C, Kaan HL, Aihara H (2022) Future directions for robotic endoscopy-artificial intelligence (AI), three-dimensional (3D) imaging, and natural orifice transluminal endoscopic surgery (notes). Techniques and innovations in gastrointestinal endoscopy

  68. Soler-Castillo Y, Rimada JC, Hernández L et al (2021) Modelling of the efficiency of the photovoltaic modules: GRID-connected plants to the Cuban national electrical system. Sol Energy 223:150–157

    Google Scholar 

  69. Sredenšek K, Štumberger B, Hadžiselimović M et al (2021) Experimental validation of a thermo-electric model of the photovoltaic module under outdoor conditions. Appl Sci 11(11):5287

    Google Scholar 

  70. Titri S, Larbes C, Toumi KY et al (2017) A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Appl Soft Comput 58:465–479

    Google Scholar 

  71. Tobnaghi DM, Madatov R, Naderi D (2013) The effect of temperature on electrical parameters of solar cells. Int J Adv Res Electr Electron Instrum Eng 2(12):6404–6407

    Google Scholar 

  72. Woźniak M, Sikora A, Zielonka A et al (2021) Heuristic optimization of multipulse rectifier for reduced energy consumption. IEEE Trans Ind Inf 18(8):5515–5526

    Google Scholar 

  73. Yap KY, Sarimuthu CR, Lim JMY (2020) Artificial intelligence based MPPT techniques for solar power system: a review. J Modern Power Syst Clean Energy 8(6):1043–1059

    Google Scholar 

  74. You T, Hu Y, Li P et al (2019) An improved imperialist competitive algorithm for global optimization. Turk J Electr Eng Comput Sci 27(5):3567–3581

    Google Scholar 

  75. Zhang G, **ao C, Razmjooy N (2020) Optimal parameter extraction of PEM fuel cells by meta-heuristics. Int J Ambient Energy 43:2510–2519

    Google Scholar 

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  1. Faculty of Science and Technology, Abdelmalek Essaadi University, Al Hoceima, Tetouan, Morocco

    Noamane Ncir & Nabil El Akchioui

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Ncir, N., El Akchioui, N. An advanced intelligent MPPT control strategy based on the imperialist competitive algorithm and artificial neural networks. Evol. Intel. 17, 1437–1461 (2024). https://doi.org/10.1007/s12065-023-00838-y

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