آمار

تعداد نشریات30
تعداد شماره‌ها424
تعداد مقالات3,741
تعداد مشاهده مقاله5,865,359
تعداد دریافت فایل اصل مقاله4,052,439
Gurgi, Z. K., Ismael, A. I., Mejeed, R. A.. (1403). The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature. سامانه مدیریت نشریات علمی, 12(2), 134-141. doi: 10.22098/joape.2023.10704.1774
Z. K. Gurgi; A. I. Ismael; R. A. Mejeed. "The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature". سامانه مدیریت نشریات علمی, 12, 2, 1403, 134-141. doi: 10.22098/joape.2023.10704.1774
Gurgi, Z. K., Ismael, A. I., Mejeed, R. A.. (1403). 'The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature', سامانه مدیریت نشریات علمی, 12(2), pp. 134-141. doi: 10.22098/joape.2023.10704.1774
Gurgi, Z. K., Ismael, A. I., Mejeed, R. A.. The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature. سامانه مدیریت نشریات علمی, 1403; 12(2): 134-141. doi: 10.22098/joape.2023.10704.1774

The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperature

Journal of Operation and Automation in Power Engineering
مقاله 4، دوره 12، شماره 2، 2024، صفحه 134-141    اصل مقاله (978.3 K)
نوع مقاله: Research paper
شناسه دیجیتال (DOI): 10.22098/joape.2023.10704.1774
نویسندگان
Z. K. Gurgi* ؛ A. I. Ismael؛ R. A. Mejeed
Departement of Electrical power and Machine Engineering, College of Engineering, Diyala University, Iraq
چکیده
Solar cell efficiency considers an important part of the PV system, the parameters (Io, IL, n, Rs, and Rsh) of solar cell is the main part that effected on efficiency. The Matlab simulation program was used to estimate the three parameters' optimization values and evaluated by the Fminsearch method, they calculated for solar cells measured from 0oC to 100oC for seven temperatures, then make comparing for the results between the Genetic Algorithm method with Neural Network Algorithm. This paper establishes the results are frequently in GA was better than NNA, with the Io being 3.0992 e-7 and IL being 3.8059 found by GA. GA is good if they have the same population size and number of iterations. The value of the objective function (fval) in GA is 0.002856 but in NNA is 0.005518. And also second objective function (fvaltemp) in GA is 0.1035 with a 0.1069 value in NNA. From the side, the execution time considers in the Fminsearch method is less than NNA and GA that being 64.9 s, 781 s, and 289 s respectively.
کلیدواژه‌ها
Genetic algorithm؛ Neural network؛ Solar cell
مراجع
  1. R. Pazikadin, et al., "Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend," Sci. Total Environ. , vol. 715, p. 136848, 2020.
  2. A. Vitorino, et al., "Using the model of the solar cell for determining the maximum power point of photovoltaic systems," Europ. Conf. Power Electro. Appli., pp. 1-10, 2007.
  3. -H. Chung, et al., "A novel maximum power point tracking technique for solar panels using a SEPIC or Cuk converter," IEEE trans. power electron., vol. 18, pp. 717-724, 2003.
  4. Hashemzadeh and M. Hejri, "A Fast and Accurate Global Maximum Power Point Tracking Method for Solar Strings under Partial Shading Conditions," J. Oper. Autom. Power Eng., vol. 8, pp. 245-256, 2020.
  5. O. Saetre, et al., "A new analytical solar cell I–V curve model," Renewable Energy, vol. 36, pp. 2171-2176, 2011.
  6. A. Hamdy, "A new model for the current-voltage output characteristics of photovoltaic modules," J. power sources, vol. 50, pp. 11-20, 1994.
  7. Ahmad, et al., "Theoretical analysis and experimental verification of PV modules," Renewable energy, vol. 28, pp. 1159-1168, 2003.
  8. MILENOV, et al., "Modeling of electrical characteristics of various PV panels," 16th Conf. Electri. Machines, Drives Power Syst. (ELMA), pp. 1-5, 2019.
  9. Skoplaki, et al., "A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting," Sol. Energy Mater. Sol. Cells, vol. 92, pp. 1393-1402, 2008.
  10. Li, et al., "Investigating the effect of radiative cooling on the operating temperature of photovoltaic modules," Solar RRL, vol. 5, p. 2000735, 2021.
  11. N. Botsaris and J. A. Tsanakas, "Infrared thermography as an estimator technique of a photovoltaic module performance via operating temperature measurements," Proce. 10th ECNDT Conf., 2010.
  12. Ciulla, et al., "A comparison of different one-diode models for the representation of I–V characteristic of a PV cell," Renewable Sustainable Energy Rev., vol. 32, pp. 684-696, 2014.
  13. Al-Rawi, "Numerical solution of integral equations using taylor series," J. College of Educ., vol. 5, pp. 51-60, 1992.
  14. Duran, et al., "Different methods to obtain the I–V curve of PV modules: A review," in 2008 33rd IEEE Photovoltaic Specialists Conference, 2008, pp. 1-6.
  15. S. Rasheed and S. Shihab, "Analysis of Mathematical Modeling of PV Cell with Numerical Algorithm," Advanced Energy Conver. Materi., pp. 70-79, 2020.
  16. Rasheed, et al., "A comparative Analysis of PV Cell Mathematical Model," J. Phys. Conf. Ser., p. 012042, 2021.
  17. Lupu, et al., "A review of solar photovoltaic systems cooling technologies," IOP Conf. Series: Materi. Scie. Eng., p. 082016, 2018.
  18. A. Khan, et al., "Performance evaluation of photovoltaic solar system with different cooling methods and a bi-reflector PV system (BRPVS): an experimental study and comparative analysis," Energies, vol. 10, p. 826, 2017.
  19. Bella and F. Kebbab, "Application of Fminsearch Optimization to Minimize Total Maintenance Cost with the Aim of Reducing Environmental Degradation," Eng. Techno. Applied Science Research, vol. 12, pp. 8548-8554, 2022.
  20. D. Bastidas-Rodriguez, et al., "A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel," Math. Comput. Simul, vol. 131, pp. 38-54, 2017.
  21. Hamid, et al., "Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover," Int. J. Ambient Energy, vol. 40, pp. 517-524, 2019.
  22. Petrone, et al., "Online identification of photovoltaic source parameters by using a genetic algorithm," Applied Sciences, vol. 8, p. 9, 2018.
  23. Karatepe, et al., "Neural network based solar cell model," Energy Convers. Manage. , vol. 47, pp. 1159-1178, 2006.
  24. Z. Antonopoulos, et al., "Solar radiation estimation methods using ANN and empirical models," Comput. Electron. Agric., vol. 160, pp. 160-167, 2019.
  25. J. Wagner, et al., "Optimizing dispatch for a concentrated solar power tower," Solar Energy, vol. 174, pp. 1198-1211, 2018.
  26. Sadollah, et al., "A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm," Appl. Soft Comput., vol. 71, pp. 747-782, 2018.
  27. K. Mishu, et al., "An adaptive TE-PV hybrid energy harvesting system for self-powered iot sensor applications," Sensors, vol. 21, p. 2604, 2021.
  28. B. Roy, et al., "A comparative performance analysis of ANN algorithms for MPPT energy harvesting in solar PV system," IEEE Access, vol. 9, pp. 102137-102152, 2021.
  29. Dahmardeh, et al., "A novel combined DTC method and SFOC system for three-phase induction machine drives with PWM switching method," J. Oper. Autom. Power Eng., 2022.
آمار
تعداد مشاهده مقاله: 749
تعداد دریافت فایل اصل مقاله: 829


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب