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    Wyświetlanie 1-2 z 2
    Tytuł:
    Numerical identification of Preisach distribution function including temperature effects
    Autorzy:
    Chelghoum, Leila
    Powiązania:
    https://bibliotekanauki.pl/articles/2086685.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    magnetic hysteresis
    Preisach distribution function
    Preisach model
    soft ferromagnetic materials
    temperature effect
    Opis:
    Magnetic hysteresis occurs in most electrical engineering devices once soft ferromagnetic materials are exposed to relatively high temperatures. According to several scientific studies, magnetic properties are strongly influenced by temperature. The development of models that can accurately describe the thermal effect on ferromagnetic materials is still an issue that inspires researchers. In this paper, the effect of temperature on magnetic hysteresis for ferromagnetic materials is investigated using a self-developed numerical method based on the Preisach distribution function identification. It employs a parameter depending on both temperature and the Curie temperature. This approach is of the macroscopic phenomenological type, where the variation of the magnetization (in direct connection with the Preisach triangle) is related to the observed macroscopic hysteretic behavior. The isotropic character of the material medium is predominant. The technique relies on a few experimental data extracted from the first magnetization curve provided by metallurgists. The ultimate goal is to provide a simple and robust magnetic behavior modeling tool for designers of electrical devices. Temperature is introduced at the stage of identifying the distribution function of the Preisach model. This method is validated by the agreement between the experimental data and the simulation results. The developed method is very accurate and efficient in modeling the hysteresis of ferromagnetic materials in engineering particularly for systems with ferromagnetic components and electromagnetic-thermal coupling.
    Źródło:
    Archives of Electrical Engineering; 2022, 71, 2; 297--309
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Short-term wind power combined prediction based on EWT-SMMKL methods
    Autorzy:
    Li, Jun
    Ma, Liancai
    Powiązania:
    https://bibliotekanauki.pl/articles/1955182.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    combined model
    empirical wavelet transform
    prediction
    soft margin multiple kernel learning
    wind power
    model łączony
    empiryczna transformacja falkowa
    przewidywanie
    miękki margines uczenia wielokrotnego jądra
    energia wiatrowa
    Opis:
    Since wind power generation has strong randomness and is difficult to predict, a class of combined prediction methods based on empirical wavelet transform (EWT) and soft margin multiple kernel learning (SMMKL) is proposed in this paper. As a new approach to build adaptive wavelets, the main idea is to extract the different modes of signals by designing an appropriate wavelet filter bank. The SMMKL method effectively avoids the disadvantage of the hard margin MKL method of selecting only a few base kernels and discarding other useful basis kernels when solving for the objective function. Firstly, the EWT method is used to decompose the time series data. Secondly, different SMMKL forecasting models are constructed for the sub-sequences formed by each mode component signal. The training processes of the forecasting model are respectively implemented by two different methods, i.e., the hinge loss soft margin MKL and the square hinge loss soft margin MKL. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. In order to verify the effectiveness of the proposed method, it was applied to an actual wind speed data set from National Renewable Energy Laboratory (NREL) for short-term wind power single-step or multi-step time series indirectly forecasting. Compared with a radial basic function (RBF) kernel- based support vector machine (SVM), using SimpleMKL under the same condition, the experimental results show that the proposed EWT-SMMKL methods based on two different algorithms have higher forecasting accuracy, and the combined models show effectiveness.
    Źródło:
    Archives of Electrical Engineering; 2021, 70, 4; 801-817
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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