University of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457612220240401The Comparing Between Genetic Algorithm and Neural Network to Compute of Three-Basic Solar Cell Parameters with Wide Range of Measured Temperatureمقایسه الگوریتم ژنتیک و شبکه عصبی برای محاسبه پارامترهای سلول خورشیدی سه پایه با دامنه وسیعی از دمای اندازه گیری شده134141192410.22098/joape.2023.10704.1774ENZ. K. GurgiDepartement of Electrical power and Machine Engineering, College of Engineering, Diyala University, IraqA. I. IsmaelDepartement of Electrical power and Machine Engineering, College of Engineering, Diyala University, IraqR. A. MejeedDepartement of Electrical power and Machine Engineering, College of Engineering, Diyala University, IraqJournal Article20220424Solar cell efficiency considers an important part of the PV system, the parameters (I<sub>o</sub>, I<sub>L</sub>, n, R<sub>s, </sub>and R<sub>sh</sub>) 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 0<sup>o</sup>C to 100<sup>o</sup>C 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 I<sub>o</sub> being 3.0992 e<sup>-7 </sup>and I<sub>L</sub> 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.https://joape.uma.ac.ir/article_1924_ce25de0461963c19a2558b3ffb1a7c54.pdf