INFO algorithm based MPPT optimization of a photovoltaic system under partial shading conditions

Öz

Obtaining maximum efficiency from photovoltaic (PV) systems through maximum power point tracking (MPPT) remains an ongoing challenge. In this study, the weighted mean of vector (INFO) algorithm is employed to address and solve the MPPT problem for a photovoltaic system operating under partial shading. Besides INFO algorithm, electric eel optimization (EEFO), red-tailed hawk algorithm (RTHA), and student psychology-based optimization (SPBO) algorithms were also employed, and this study is the first to employ these optimization algorithms for MPPT purposes. The particle swarm optimization (PSO) algorithm, which is frequently employed in MPPT studies, is employed to compare the performance of new metaheuristic algorithms. These algorithms are tested with challenging shading scenarios where the local maximum points (LMPPs) and global maximum power point (GMPP) varied. The performance of these algorithms is evaluated using the Friedman test, which is a statistical test, and performance metrics. According to the findings of the comparison, the INFO algorithm is the most effective among the five algorithms for MPPT optimization under partial shading conditions, and this conclusion is confirmed statistically. Additionally, experimental tests were conducted to evaluate the performance of the INFO algorithm on real hardware. A programmable PV simulator, boost converter, and STM32 board were used. The experiments demonstrated that the algorithm could quickly and stably track the maximum power point.

Anahtar Kelimeler

• Friedman Test
• INFO
• Maximum Power Point
• MPPT
• Partial Shading
• Photovoltaic.

Kısmı gölgelenme koşulları altında fotovoltaik sistemlerin INFO algoritması tabanlı MPPT optimizasyonu

Öz

Fotovoltaik (FV) sistemlerden maksimum verim elde etmek için maksimum güç noktası izleme (MPPT) yöntemi önemli bir araştırma konusu olmaya devam etmektedir. Bu çalışmada, kısmi gölgeleme koşulları altında çalışan bir FV sistem için MPPT problemini çözmek amacıyla vektörlerin ağırlıklı ortalaması (INFO) algoritması kullanılmıştır. INFO’ya ek, elektrikli yılanbalığı optimizasyonu, kızıl kuyruklu şahin algoritması ve öğrenci psikolojisine dayalı optimizasyon algoritması da uygulanmış olup, söz konusu optimizasyon algoritmaları MPPT amacıyla ilk kez bu çalışmada kullanılmıştır. Yeni meta sezgisel algoritmaların performansını karşılaştırmak amacıyla, MPPT çalışmalarında yaygın olarak kullanılan parçacık sürü optimizasyonu algoritması da kullanılmıştır. Algoritmalar, yerel maksimum güç noktalarının ve küresel maksimum güç noktasının değişkenlik gösterdiği zorlu gölgeleme senaryolarında test edilmiştir. Algoritmaların performansları, istatistiksel bir test olan Friedman testi ve performans metrikleri kullanılarak değerlendirilmiştir. Karşılaştırma sonuçlarına göre, kısmi gölgeleme koşulları altında MPPT optimizasyonu için en etkili algoritmanın INFO algoritması olduğu belirlenmiş ve bu sonuç istatistiksel olarak doğrulanmıştır. Ayrıca, INFO algoritmasının gerçek donanımda performansını test etmek için deneysel çalışmalar yapılmıştır. Programlanabilir FV simülatörü, yükseltici tip dönüştürücü ve STM32 kartı kullanılmıştır. Deneyler, algoritmanın hızlı ve kararlı şekilde maksimum güç noktasını izlediğini göstermiştir.

Anahtar Kelimeler

• Friedman Testi
• INFO
• Maksimum Güç Noktası
• MPPT
• Kısmi Gölgeleme
• Fotovoltaik

Kaynakça

1. [1] Khan K, Su CW, Rehman AU, Ullah R. “Is technological innovation a driver of renewable energy?”. Technology in Society, 70, 102044, 2022.
2. [2] Kishore DJK, Mohamed MR, Sudhakar K, Peddakapu K. “Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions”. Energy, 265, 126366, 2023.
3. [3] Olabi AG, Abdelkareem MA. “Renewable energy and climate change”. Renewable and Sustainable Energy Reviews, 158, 112111, 2022.
4. [4] Gulzar MM, Iqbal A, Sibtain D, Khalid M. “An innovative converterless solar PV control strategy for a grid connected hybrid PV/wind/fuel-cell system coupled with battery energy storage”. IEEE Access, 11, 23245–23259, 2023.
5. [5] P Bojek. “Tracking Clean Energy Progress 2023. Available” https://www.iea.org/reports/tracking-clean-energy-progress (01.01.2025).
6. [6] Sahu A, Yadav N, Sudhakar K. “Floating photovoltaic power plant: A review”. Renewable and Sustainable Energy Reviews, 66, 815–824, 2016.
7. [7] Refaat A, Khalifa AE, Elsakka MM, Elhenawy Y, Kalas A, Elfar MH. “A novel metaheuristic MPPT technique based on enhanced autonomous group particle swarm optimization algorithm to track the GMPP under partial shading conditions-Experimental validation”. Energy Conversion and Management, 287, 117124, 2023.
8. [8] Mirza AF, Ling Q, Javed MY, Mansoor M. “Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading”. Solar Energy, 184, 628–648, 2019.

9. [9] Yang B, Zhu T, Wang J, Shu H, Yu T, Zhang X, Yao W, Sun L. “Comprehensive overview of maximum power point tracking algorithms of PV systems under partial shading condition”. Journal of Cleaner Production, 268, 121983, 2020.
10. [10] Wasim MS, Amjad M, Habib S, Abbasi MA, Bhatti AR, Muyeen SM. “A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions”. Energy Reports, 8, 4871-4898,2022.
11. [11] Kollimalla SK, Mishra MK. “Variable perturbation size adaptive P&O MPPT algorithm for sudden changes in irradiance”. IEEE Transactions on Sustainable Energy, 5(3), 718–728, 2014.
12. [12] Swaminathan N, Lakshminarasamma N, Cao Y. “A fixed zone perturb and observe MPPT technique for a standalone distributed PV system”. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(1), 361–374,2021.
13. [13] Jately V, Azzopardi B, Joshi J, Venkateswaran BV, Sharma A, Arora S. “Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels”. Renewable and Sustainable Energy Reviews, 150, 111467, 2021.
14. [14] Kumar V, Bindal RK. “A comparative analysis of effective MPPT technology for photovoltaic system with boost converter”. Materials Today: Proceedings, 69, A1–A5, 2022.
15. [15] Loukriz A, Haddadi M, Messalti S. “Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems”. ISA Transactions, 62, 30–38, 2016.
16. [16] Khan MK, Zafar MH, Riaz T, Mansoor M, Akhtar N. “Enhancing efficient solar energy harvesting: A process-in-loop investigation of MPPT control with a novel stochastic algorithm”. Energy Conversion and Management: X, 21, 100509, 2024.
17. [17] Hsu TW, Wu HH, Tsai DL, Wei CL. “Photovoltaic energy harvester with fractional open-circuit voltage based maximum power point tracking circuit”. IEEE Transactions on Circuits and Systems. II, Express Briefs, 66(2), 257–261, 2019.
18. [18] Shebani MM, Iqbal T, Quaicoe JE. “Comparing bisection numerical algorithm with fractional short circuit current and open circuit voltage methods for MPPT photovoltaic systems”. 2016 IEEE Electrical Power and Energy Conference, Ottawa, Canada, 12–14 October 2016.
19. [19] Luo G, Liu J, Yang T, Dou Y, Chen N. “A constant current and constant voltage charging circuit through MPPT method and its stability analysis”. 3rd China International SAR Symposium, Shanghai, China, 2–4 November 2022.
20. [20] Motahhir S, El Hammoumi A, El Ghzizal A. “The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm”. Journal of Cleaner Production, 246, 118983, 2020.
21. [21] Tang S, Sun Y, Chen Y, Zhao Y, Yang Y, Szeto W. “An enhanced MPPT method combining fractional-order and fuzzy logic control”. IEEE Journal of Photovoltaics, 7(2), 640–650, 2017.
22. [22] Roy RB, Rokonuzzaman M, Amin N, Mishu MK, Alahakoon S, Rahman S, Mithulananthan N, Rahman KS, Shakeri M, Pasupuleti J. “A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system”. IEEE Access, 9, 102137–102152, 2021.
23. [23] Nguimfack-Ndongmo JD, Harrison A, Alombah NH, Kuate-Fochie R, Ajesam Asoh D, Kenné G. “Adaptive terminal synergetic-backstepping technique-based machine learning regression algorithm for MPPT control of PV systems under real climatic conditions”. ISA Transactions, 145, 423–442, 2024.
24. [24] Zhou L, Chen Y, Guo K, Jia F. “New approach for MPPT control of photovoltaic system with mutative-scale dual-carrier chaotic search”. IEEE Transactions on Power Electronics, 26(4), 1038–1048, 2011.
25. [25] Samantara S, Roy B, Sharma R, Rout A. “Modeling and simulation of CUK converter with beta (B) MPPT for standalone PV system”. Michael Faraday IET International Summit 2015, West Bengal, India, 12–13 September 2015.
26. [26] Xu S, Gao Y, Zhou G, Mao G. “A global maximum power point tracking algorithm for photovoltaic systems under partially shaded conditions using modified maximum power trapezium method”. IEEE Transactions on Industrial Electronics, 68(1), 370–380, 2021.
27. [27] Osmani K, Haddad A, Lemenand T, Castanier B, Ramadan M. “An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters”. Energy, 224, 120092, 2021.
28. [28] Furtado AMS, Bradaschia F, Cavalcanti MC, Limongi LR. “A reduced voltage range global maximum power point tracking algorithm for photovoltaic systems under partial shading conditions”. IEEE Transactions on Industrial Electronics, 65(4), 3252–3262, 2018.
29. [29] Eltamaly AM, Farh HMH. “Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC”. Solar Energy, 177, 306–316, 2019.
30. [30] Seyedmahmoudian M, Rahmani R, Mekhilef S, Maung Than Oo A, Stojcevski A, Soon TK, Ghandhari AS. “Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method”. IEEE Transactions on Sustainable Energy, 6(3), 850–862, 2015.
31. [31] Sundareswaran K, Vignesh kumar V, Palani S. “Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions”. Renewable Energy, 75, 308–317, 2015.
32. [32] Mohanty S, Subudhi B, Ray PK. “A grey wolf-assisted perturb & observe MPPT algorithm for a PV system”. IEEE Transactions on Energy Conversion, 32(1), 340–347, 2017.
33. [33] Cruz-Duarte JM, Amaya I, Ortiz-Bayliss JC, Conant-Pablos SE, Terashima-Marín H, Shi Y. “Hyper-Heuristics to customise metaheuristics for continuous optimization”. Swarm and Evolutionary Computation, 66, 100935, 2021.
34. [34] Karmouni H, Chouiekh M, Motahhir S, Qjidaa H, Ouazzani Jamil M, Sayyouri M. “A fast and accurate sine-cosine MPPT algorithm under partial shading with implementation using arduino board”. Cleaner Engineering and Technology, 9, 100535, 2022.
35. [35] Chandrasekharan S, Subramaniam S, Veerakgoundar V. “Honey badger optimization algorithm based maximum power point tracking for solar photovoltaic systems”. Electric Power Systems Research, 221, 109393, 2023.
36. [36] Fares D, Fathi M, Shams I, Mekhilef S. “A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions”. Energy Conversion and Management, 230, 113773, 2021.
37. [37] Mansoor M, Mirza AF, Ling Q. “Harris hawk optimization-based MPPT control for PV systems under partial shading conditions”. Journal of Cleaner Production, 274, 122857, 2020.
38. [38] Alshareef MJ. “An effective falcon optimization algorithm based MPPT under partial shaded photovoltaic systems”. IEEE Access, 10, 131345–131360, 2022.
39. [39] Zhao Z, Zhang M, Zhang Z, Wang Y, Cheng R, Guo J, Yang P, Lai CS, Li P, Lai LL. “Hierarchical pigeon-inspired optimization-based MPPT method for photovoltaic systems under complex partial shading conditions”. IEEE Transactions on Industrial Electronics, 69(10), 10129–10143, 2022.
40. [40] Moosavi SKR, Mansoor M, Zafar MH, Khan NM, Mirza AF, Akhtar N. “Highly efficient maximum power point tracking control technique for PV system under dynamic operating conditions”. Energy Reports, 8, 13529–13543, 2022.
41. [41] Sridhar R, Subramani C, Pathy S. “A grasshopper optimization algorithm aided maximum power point tracking for partially shaded photovoltaic systems”. Computers and Electrical Engineering, 92, 107124, 2021.
42. [42] Pervez I, Shams I, Mekhilef S, Sarwar A, Tariq M, Alamri B. “Most valuable player algorithm based maximum power point tracking for a partially shaded PV generation system”. IEEE Transactions on Sustainable Energy, 12(4), 1876–1890, 2021.
43. [43] Xu H, Zhao M, Xue F, Zhang X, Sun L. “An improved mayfly algorithm with shading detection for MPPT of photovoltaic systems”. IEEE Access, 11, 110827–110836, 2023.
44. [44] Koh JS, Tan RHG, Lim WH, Tan NML. “A modified particle swarm optimization for efficient maximum power point tracking under partial shading condition”. IEEE Transactions on Sustainable Energy, 14(3), 1822–1834, 2023.
45. [45] Mohammed KK, Buyamin S, Shams I, Mekhilef S. “Hybrid global maximum power tracking method with partial shading detection technique for PV systems”. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(4), 4821–4831, 2021.
46. [46] Azli H, Titri S, Larbes C, Kaced K, Femmam K. “Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions”. Solar Energy, 247, 295–307, 2022.
47. [47] Fu C, Zhang L. “A novel method based on tuna swarm algorithm under complex partial shading conditions in PV system”. Solar Energy, 248, 28–40, 2022.
48. [48] Refaat A, Ali QA, Elsakka MM, Elhenawy Y, Majozi T, Korovkin NV, Elfar MH. “Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions”. Renewable Energy, 220, 119718, 2024.
49. [49] Mirza AF, Mansoor M, Ling Q. “A novel MPPT technique based on Henry gas solubility optimization”. Energy Conversion and Management, 225, 113409, 2020.
50. [50] Zheng S, Shahzad M, Asif HM, Gao J, Muqeet HA. “Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies”. Renewable Energy, 206, 1326–1335, 2023.
51. [51] Mirza AF, Mansoor M, Ling Q, Yin B, Javed MY. “A Salp-Swarm Optimization based MPPT technique for harvesting maximum energy from PV systems under partial shading conditions”. Energy Conversion and Management, 209, 112625, 2020.
52. [52] Tagayi RK, Baek J, Kim J. “Flower pollination global peak search algorithm for partially shaded solar photovoltaic system”. Journal of Building Engineering, 66, 105818, 2023.
53. [53] Gürkan E, Güner A. “Comparative performance analysis of metaheuristic algorithms for maximum power point tracking under partial shading conditions in PV systems”. Pamukkale University Journal of Engineering Sciences, 30 (7), 891–905, 2024.
54. [54] Wolpert DH, Macready WG. “No free lunch theorems for optimization”. IEEE Transactions on Evolutionary Computation: A Publication of the IEEE Neural Networks Council, 1(1), 67–82, 1997.
55. [55] Demirtas M, Koc K. “Parameter extraction of photovoltaic cells and modules by INFO algorithm”. IEEE Access, 10, 87022–87052, 2022.
56. [56] Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH. “INFO: An efficient optimization algorithm based on weighted mean of vectors”. Expert Systems with Applications, 195, 116516, 2022.
57. [57] Zhao W, Wang L, Zhang Z, Fan H, Zhang J, Mirjalili S, Khodadadi N, Cao Q. “Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications”. Expert Systems with Applications, 238, 122200, 2024.
58. [58] Ferahtia S, Houari A, Rezk H, Djerioui A, Machmoum M, Motahhir S, Ait-Ahmed M. “Red-tailed hawk algorithm for numerical optimization and real-world problems”. Scientific Reports, 13(1), 1–42, 2023.
59. [59] Das B, Mukherjee V, Das D. “Student psychology-based optimization algorithm: A new population-based optimization algorithm for solving optimization problems”. Advances in Engineering Software, 146, 102804, 2020.
60. [60] Eberhart R, Kennedy J. “A new optimizer using particle swarm theory”. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 4–6 October 1995.
61. [61] He K, Zhang Y, Wang YK, Zhou RH, Zhang HZ. “EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems”. Alexandria Engineering Journal, 87, 543–573, 2024.
62. [62] Derrac J, García S, Molina D, Herrera F. “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”. Swarm and Evolutionary Computation, 1(1), 3–18, 2011.