کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5145410 | 1497349 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
الکتروشیمی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator](/preview/png/5145410.png)
چکیده انگلیسی
An estimation study on the output power and the efficiency of a new-designed axial flux permanent magnet synchronous generator (AFPMSG) is performed. For the estimation algorithm, a multi-layer feedforward artificial neural network (ANN) is developed. Various experimental results from the generator have been used for the training purpose in the cases of different electrical loads and rotational speeds. Some experimental data is kept out of the training process for testing the network and the errors have been evaluated after the formation of the network. According to the findings, a network with three layers has been adequate to achieve very good error percentage between the ANN and laboratory studies. The maximal testing error percentages are found to be nearly 3% and 4% for the output power and efficiency estimations, respectively. According to that finding, the developed ANN has a good property that it can be used in place of the designed generator, especially when the generator mathematical model is required. In addition, since power and efficiency are important for present applications, the present tool can be used to estimate the data for those characteristics of the machines and even it can be beneficial for the applications, where a nonlinear relationship among the power generation, generator efficiency, speed and load is required.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Hydrogen Energy - Volume 42, Issue 28, 13 July 2017, Pages 17692-17699
Journal: International Journal of Hydrogen Energy - Volume 42, Issue 28, 13 July 2017, Pages 17692-17699
نویسندگان
Emre Ãelik, Halil Gör, Nihat Ãztürk, Erol Kurt,