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Vol. 3 No. 3 (2024): August 2024 Issue

Improved photovoltaic energy production under partial shading using an innovative MPPT controller based on Flying Squirrel Search Optimization algorithm

Diatta Sene
Laboratoire Eau, Energie, Environnement et Procédés Industriels (LE3PI), Ecole Supérieure Polytechnique, Université Cheikh Anta Diop Dakar, Senegal
Adama Sarr
Laboratoire Eau, Energie, Environnement et Procédés Industriels (LE3PI), Ecole Supérieure Polytechnique, Université Cheikh Anta Diop Dakar, Senegal
Mohamed Koïta Sako
Laboratoire Mécanique et Sciences des Materiaux, Institut Nationale Polytechnique, Félix HOUPHOUËT-BOIGNY Yamoussokro, Cote d’Ivoire
Adama Ouattara
Laboratoire des procédés industriels, des synthèses et des énergies renouvelables Institut Nationale Polytechnique, Félix HOUPHOUËT-BOIGNY Yamoussokro, Cote d’Ivoire
Mouhamadou Falilou Ndiaye
Laboratoire Eau, Energie, Environnement et Procédés Industriels (LE3PI), Ecole Supérieure Polytechnique, Université Cheikh Anta Diop Dakar, Senegal
Vincent Sambou
Laboratoire Eau, Energie, Environnement et Procédés Industriels (LE3PI), Ecole Supérieure Polytechnique, Université Cheikh Anta Diop Dakar, Senegal

Corresponding Author(s) : Diatta Sene

diattasene3s@gmail.com

Future Energy, Vol. 3 No. 3 (2024): August 2024 Issue

Abstract

This paper explores the issue of partial shading (PS) in photovoltaic systems and proposes a solution using the Flying Squirrel Search Optimization (FSSO) algorithm. PS results in power losses, and our study aims to enhance photovoltaic energy production by introducing a new Maximum Power Point Tracking (MPPT) controller based on the FSSO algorithm. This paper presents a controller designed to efficiently track the maximum power in the presence of partial shading. The controller uses the Modified ODD-EVEN (MOE) configuration, the Modified Symmetrical Array (MSA), and the Total-Cross-Tied (TCT). The performance of the FSSO algorithm was compared with that of GWO and PSO. A bidirectional DC-DC converter was integrated to connect the PV system to a battery. The proposed methods were also applied to an electric vehicle powered by a PV battery. The FSSO proposed in this study demonstrates an efficiency of over 99%, outperforming other methods in tracking the maximum power point and converging faster to the overall maximum power point. The results indicate that convergence time can be improved from 16% to over 60%. Furthermore, the proposed MPPT technique effectively minimizes over 80% of random oscillations. The proposed method reduces losses and maximizes fill factor compared to alternative algorithms, based on two observed shading cases. When connecting the system to a battery, the FSSO algorithm outperforms the PSO by more than 34.78% and the GWO by 49.02%. Although slightly inferior to the PSO, the FSSO still performs significantly in the context of an electric vehicle powered by a photovoltaic battery.

Keywords

PV systems MPPT Optimization PV power Partial shading algorithms
Sene, Diatta, Adama Sarr, Mohamed Koïta Sako, Adama Ouattara, Mouhamadou Falilou Ndiaye, and Vincent Sambou. 2024. “Improved Photovoltaic Energy Production under Partial Shading Using an Innovative MPPT Controller Based on Flying Squirrel Search Optimization Algorithm”. Future Energy 3 (3):33-48. https://fupubco.com/fuen/article/view/170.
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References
  1. N. Rakesh and T. V. Madhavaram, “Performance enhancement of partially shaded solar PV array using novel shade dispersion technique,” Front. Energy, vol. 10, no. 2, pp. 227–239, 2016.
  2. S. Pareek, N. Chaturvedi, and R. Dahiya, “Optimal interconnections to address partial shading losses in solar photovoltaic arrays,” Sol. Energy, vol. 155, pp. 537–551, 2017.
  3. V. M. R. Tatabhatla, A. Agarwal, and T. Kanumuri, “Enhanced performance metrics under shading conditions through experimental investigations,” IET Renew. Power Gener, vol. 14, no. 14, pp. 2592–2603, 2020.
  4. M. Etarhouni, B. Chong, and L. Zhang, “A combined scheme for maximising the output power of a Photovoltaic array under partial shading conditions,” Sustain. Energy Technol. Assessments, vol. 50, no. March, 2022.
  5. A. M. Ajmal, T. Sudhakar Babu, V. K. Ramachandaramurthy, D. Yousri, and J. B. Ekanayake, “Static and dynamic reconfiguration approaches for mitigation of partial shading influence in photovoltaic arrays,” Sustain. Energy Technol. Assessments, vol. 40, 2020.
  6. S. Sugumar, D. Prince Winston, and M. Pravin, “A novel on-time partial shading detection technique for electrical reconfiguration in solar PV system,” Sol. Energy, vol. 225, no. September, pp. 1009–1025, 2021.
  7. S. Rezazadeh, A. Moradzadeh, S. M. Hashemzadeh, K. Pourhossein, B. Mohammadi-Ivatloo, and S. H. Hosseini, “A novel prime numbers-based PV array reconfiguration solution to produce maximum energy under partial shade conditions,” Sustain. Energy Technol. Assessments, vol. 47, no. July, p. 101498, 2021.
  8. M. Zeeshan, N. U. Islam, F. Faizullah, I. U. Khalil, and J. Park, “A Novel Row Index Mathematical Procedure for the Mitigation of PV Output Power Losses during Partial Shading Conditions,” Symmetry (Basel), vol. 15, no. 3, 2023.
  9. N. Djilali and N. Djilali, “PV array power output maximization under partial shading using new shifted PV array arrangements,” Appl. Energy, vol. 187, pp. 326–337, 2017.
  10. K. S. Faldu and P. S. Kulkarni, “Maximization of the Output Power from Photovoltaic Array under Partial Shading Conditions,” 2020 IEEE Int. Students’ Conf. Electr. Electron. Comput. Sci. SCEECS 2020, 2020.
  11. A. S. Yadav, R. K. Pachauri, Y. K. Chauhan, S. Choudhury, and R. Singh, “Performance enhancement of partially shaded PV array using novel shade dispersion effect on magic-square puzzle configuration,” Sol. Energy, vol. 144, pp. 780–797, 2017.
  12. B. Dhanalakshmi and N. Rajasekar, “A novel Competence Square based PV array reconfiguration technique for solar PV maximum power extraction,” Energy Convers. Manag, vol. 174, no. August, pp. 897–912, 2018.
  13. P. R. Satpathy, A. Sarangi, S. Jena, B. Jena, and R. Sharma, “Topology alteration for output power maximization in PV arrays under partial shading,” Int. Conf. Technol. Smart City Energy Secur. Power Smart Solut. Smart Cities, ICSESP 2018 - Proc., vol. 2018-Janua, no. March, pp. 1–6, 2018.
  14. S. N. Deshkar, S. B. Dhale, J. S. Mukherjee, T. S. Babu, and N. Rajasekar, “Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm,” Renew. Sustain. Energy Rev., vol. 43, no. 2015, pp. 102–110, 2015.
  15. A. Harrag and S. Messalti, “Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions,” Neural Comput. Appl., vol. 30, no. 4, pp. 1145–1170, 2018.
  16. A. N. M. Mohammad, M. A. M. Radzi, N. Azis, S. Shafie, and M. A. A. M. Zainuri, “A novel hybrid approach for maximizing the extracted photovoltaic power under complex partial shading conditions,” Sustain., vol. 12, no. 14, pp. 1–24, 2020.
  17. D. Prince Winston et al., “Parallel power extraction technique for maximizing the output of solar PV array,” Sol. Energy, vol. 213, no. January, pp. 102–117, 2021.
  18. R. K. Pachauri et al., “Impact of partial shading on various PV array configurations and different modeling approaches: A comprehensive review,” IEEE Access, vol. 8, pp. 181375–181403, 2020.
  19. N. Kacimi, A. Idir, S. Grouni, and M. S. Boucherit, “a New Combined Method for Tracking the Global Maximum Power Point of Photovoltaic Systems,” Rev. Roum. des Sci. Tech. Ser. Electrotech. Energ., vol. 67, no. 3, pp. 349–354, 2022.
  20. Y. Zhan, C. Wei, J. Zhao, Y. Qiang, W. Wu, and Z. Hao, “Adaptive mutation quantum-inspired squirrel search algorithm for global optimization problems,” Alexandria Eng. J., vol. 61, no. 9, pp. 7441–7476, 2022.
  21. Y. Wang and T. Du, “An improved squirrel search algorithm for global function optimization,” Algorithms, vol. 12, no. 4, 2019.
  22. G. Azizyan, F. Miarnaeimi, M. Rashki, and N. Shabakhty, “Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems,” Iran. J. Optim., vol. 11, no. 2, pp. 177–205, 2019.
  23. D. Kumar et al., “A Novel Hybrid MPPT Approach for Solar PV Systems Using Particle-Swarm-Optimization-Trained Machine Learning and Flying Squirrel Search Optimization,” Sustain. 2023, Vol. 15, Page 5575, vol. 15, no. 6, p. 5575, Mar. 2023.
  24. I. Grgić, M. Bašić, and D. Vukadinović, “Optimization of electricity production in a grid-tied solar power system with a three-phase quasi-Z-source inverter,” J. Clean. Prod., vol. 221, pp. 656–666, 2019.
  25. D. Yousri, T. S. Babu, E. Beshr, M. B. Eteiba, and D. Allam, “A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System,” IEEE Access, vol. 8, pp. 112407–112426, 2020.
  26. N. Rajeswari and S. Venkatanarayanan, “An Efficient Honey Badger Optimization Based Solar MPPT Under Partial Shading Conditions,” Intell. Autom. Soft Comput., vol. 35, no. 2, pp. 1311–1322, 2023
  27. A. M. Humada, M. Hojabri, S. Mekhilef, and H. M. Hamada, “Solar cell parameters extraction based on single and double-diode models: A review,” Renew. Sustain. Energy Rev., vol. 56, no. January 2020, pp. 494–509, 2016.
  28. K. Ishaque, Z. Salam, H. Taheri, and Syafaruddin, “Modeling and simulation of photovoltaic (PV) system during partial shading based on a two-diode model,” Simul. Model. Pract. Theory, vol. 19, no. 7, pp. 1613–1626, 2011.
  29. M. H. Zafar, N. M. Khan, A. F. Mirza, and M. Mansoor, “Bio-inspired optimization algorithms based maximum power point tracking technique for photovoltaic systems under partial shading and complex partial shading conditions,” J. Clean. Prod., vol. 309, no. May, p. 127279, 2021.
  30. P. Shaw, “Modelling and analysis of an analogue MPPT-based PV battery charging system utilising DC-DC boost converter,” IET Renew. Power Gener., vol. 13, no. 11, pp. 1958–1967, Aug. 2019.
  31. M. Mansoor, A. F. Mirza, and Q. Ling, “Harris hawk optimization-based MPPT control for PV systems under partial shading conditions,” J. Clean. Prod., vol. 274, p. 122857, 2020.
  32. T. G. Ragnagnéwendé, “Maximisation du transfert de l ’ énergie d ’ un champ photovoltaïque tenant compte du phénomène d ’ ombrage partiel : connexion réseau électrique par,” 2019.
  33. O. Bingöl and B. Özkaya, “Analysis and comparison of different PV array configurations under partial shading conditions,” Sol. Energy, vol. 160, no. November 2017, pp. 336–343, 2018.
  34. A. S. Yadav, R. K. Pachauri, and Y. K. Chauhan, “Comprehensive investigation of PV arrays with puzzle shade dispersion for improved performance,” Sol. Energy, vol. 129, pp. 256–285, 2016.
  35. M. S. S. Nihanth, J. P. Ram, D. S. Pillai, A. M. Y. M. Ghias, A. Garg, and N. Rajasekar, “Enhanced power production in PV arrays using a new skyscraper puzzle based one-time reconfiguration procedure under partial shade conditions (PSCs),” Sol. Energy, vol. 194, no. October, pp. 209–224, 2019.
  36. G. Meerimatha and B. L. Rao, “Novel reconfiguration approach to reduce line losses of the photovoltaic array under various shading conditions,” Energy, vol. 196, p. 117120, 2020.
  37. D. Fares, M. Fathi, I. Shams, and S. Mekhilef, “A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions,” Energy Convers. Manag., vol. 230, no. December 2020, p. 113773, 2021.
  38. N. Singh, K. K. Gupta, S. K. Jain, N. K. Dewangan, and P. Bhatnagar, “A Flying Squirrel Search Optimization for MPPT under Partial Shaded Photovoltaic System,” IEEE J. Emerg. Sel. Top. Power Electron, vol. 9, no. 4, pp. 4963–4978, 2021.
  39. P. Verma et al., “Meta-heuristic optimization techniques used for maximum power point tracking in solar pv system,” Electron., vol. 10, no. 19, pp. 1–57, 2021.
  40. J. Ahmed and Z. Salam, “A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability,” Appl. Energy, vol. 119, pp. 118–130, 2014.
  41. Y. Xiaobing, Y. Xianrui, and C. Hong, “An improved gravitational search algorithm for global optimization,” J. Intell. Fuzzy Syst., vol. 37, no. 4, pp. 5039–5047, 2019.
  42. M. Jain, V. Singh, and A. Rani, “A novel nature-inspired algorithm for optimization: Squirrel search algorithm,” Swarm Evol. Comput., vol. 44, no. November 2017, pp. 148–175, 2019.
  43. D. Yousri, T. S. Babu, E. Beshr, M. B. Eteiba, and D. Allam, “A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System,” IEEE Access, vol. 8, pp. 112407–112426, 2020.
  44. B. Qi and J. Wang, “Fill factor in organic solar cells,” Phys. Chem. Chem. Phys., vol. 15, no. 23, pp. 8972–8982, 2013.
  45. BLOM, Youri, VOGT, Malte Ruben, RUIZ TOBON, Carlos M., et al. Energy Loss Analysis of Two‐Terminal Tandem PV Systems under Realistic Operating Conditions—Revealing the Importance of Fill Factor Gains. Solar RRL, 2023, p. 2200579.
  46. M. Dhimish, V. Holmes, B. Mehrdadi, M. Dales, B. Chong, and L. Zhang, “Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions” Renewable Energy, 2017 (pages 438-460) https://doi.org/10.1016/j.renene.2017.06.014
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