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A Single-Stack Output Power Prediction Method for High-Power, Multi-Stack SOFC System Requirements.

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Název: A Single-Stack Output Power Prediction Method for High-Power, Multi-Stack SOFC System Requirements.
Autoři: Zhang, Daihui, Hu, Jiangong, Zhao, Wei, Lai, Meilin, Gao, Zilin, Wu, Xiaolong
Zdroj: Inorganics; Dec2023, Vol. 11 Issue 12, p474, 16p
Témata: SOLID oxide fuel cells, BACK propagation
Abstrakt: The prediction of stack output power in solid oxide fuel cell (SOFC) systems is a key technology that urgently needs improvement, which will promote SOFC systems towards high-power multi-stack applications. The accuracy of power prediction directly determines the control effect and working condition recognition accuracy of the SOFC system controller. In order to achieve this goal, a genetic algorithm back propagation (GA-BP) neural network is constructed to predict output power in the SOFC system. By testing 40 sets of sample data collected from the experimental platform, it is found that the GA-BP method overcomes the limitation of the traditional back propagation (BP) method—falling into local optima. Further analysis shows that the average relative error of GA-BP has decreased to 1%. The reduction of the relative error improves the accuracy of the prediction results and the average prediction accuracy. Compared with the long short-term memory (LSTM) and BP algorithm, the GA-BP prediction model significantly reduces the relative error of power output prediction, which provides a solid foundation for multi-stack SOFC systems. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:The prediction of stack output power in solid oxide fuel cell (SOFC) systems is a key technology that urgently needs improvement, which will promote SOFC systems towards high-power multi-stack applications. The accuracy of power prediction directly determines the control effect and working condition recognition accuracy of the SOFC system controller. In order to achieve this goal, a genetic algorithm back propagation (GA-BP) neural network is constructed to predict output power in the SOFC system. By testing 40 sets of sample data collected from the experimental platform, it is found that the GA-BP method overcomes the limitation of the traditional back propagation (BP) method—falling into local optima. Further analysis shows that the average relative error of GA-BP has decreased to 1%. The reduction of the relative error improves the accuracy of the prediction results and the average prediction accuracy. Compared with the long short-term memory (LSTM) and BP algorithm, the GA-BP prediction model significantly reduces the relative error of power output prediction, which provides a solid foundation for multi-stack SOFC systems. [ABSTRACT FROM AUTHOR]
ISSN:23046740
DOI:10.3390/inorganics11120474