Modified Interactive Algorithm Based on Runge Kutta Optimizer for Photovoltaic Modeling: Justification Under Partial Shading and Varied Temperature Conditions
| Parent link: | IEEE Access Vol. 10.— 2022.— [P. 20793-20815] |
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| Other Authors: | , , , , , , |
| Summary: | Title screen The accuracy of characteristic the PV cell/module/array under several operating conditions of radiation and temperature mainly relies on their equivalent circuits sequentially; it is based on identified parameters of the circuits. Therefore, this paper proposes a modified interactive variant of the recent optimization algorithm of the rung-kutta method (MRUN) to determine the reliable parameters of single and double diode models parameters for different PV cells/modules. The results of the MRUN optimizer are validated via series of statistical analyses compared with five new meta-heuristic algorithms including aquila optimizer (AO), electric fish optimizer (EFO), barnacles mating optimizer (BMO), capuchin search algorithm (CapSA), and red fox optimization algorithm (RFSO) moreover, twenty-five state-of the art techniques from literature. Furthermore, the identified parameters certainty is evaluated in implementing the characteristics of an entire system consists of series (S), and series-parallel (S-P) PV arrays with numerous dimensions. The considered arrays dimensions are three series (3S), six series (6S), and nine series (9S) PV modules. For the investigated arrays, three-dimensional arrays are recognized. The first array comprises 3S-2P PV modules where two parallel strings (2P) have three series modules in each string (3S). The second array consists of six series-three parallel (6S-3P) PV modules, and the third one has nine series-nine parallel (9S-9P) PV modules. The results prove that the proposed algorithm precisely and reliably defines the parameters of different PV models with root mean square error and standard deviation of $7.7301e^{-4}\pm 4.9299e^{-6}$ , and ${7.4653e^{-4}}\pm {7.2905e^{-5}}$ for 1D, and 2D models, respectively meanwhile the RUN have $7.7438e^{-4}\pm ~3.5798e^{-4}$ , and $7.5861e^{-4}\pm ~4.1096e^{-4}$ , respectively. Furthermore MRUN provided extremely competing results compared to other well-known PV parameters extraction methods statistically as it has. |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.1109/ACCESS.2022.3152160 |
| Format: | Electronic Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=668685 |