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Wyszukujesz frazę "frequency support" wg kryterium: Temat

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    Wyświetlanie 1-3 z 3
    Tytuł:
    Modelling of synchronous generators for the analysis of frequency transients above 1 Hz/s
    Autorzy:
    Assenkamp, Alf
    Powiązania:
    https://bibliotekanauki.pl/articles/2135729.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    control system
    distributed energy systems
    frequency support
    generator model
    RoCoF
    synchronous generators
    synchronous machines
    Opis:
    Large synchronous generators are of high importance for the stability of power systems. They generate the frequency of the system and stabilize it in case of severe grid faults like trips of large in-feeders or loads. In distributed energy systems, in-feed via inverters will replace this generation in large parts. Modern inverters are capable of supporting grid frequency during severe faults by different means on the one hand. On the other hand, higher Rates of Change of Frequency (RoCoF) after incidents need to be accustomed by future systems. To be able to analyse the RoCoF withstand capability of synchronous or induction generators, suitable models need to be developed. Especially the control and excitation system model need enhancements compared to models proposed in standards like IEEE Std 421.5. This paper elaborates on the necessary modelling depth and validates the approach with example results.
    Źródło:
    Archives of Electrical Engineering; 2022, 71, 3; 687--700
    1427-4221
    2300-2506
    Pojawia się w:
    Archives of Electrical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Music Performers Classification by Using Multifractal Features : A Case Study
    Autorzy:
    Reljin, N.
    Pokrajac, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/177266.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    music classification
    multifractal analysis
    support vector machines
    cross-validation
    mel-frequency cepstral coefficients
    Opis:
    In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 223-233
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Specific emitter identification using geometric features of frequency drift curve
    Autorzy:
    Zhao, Y.
    Wui, L.
    Zhang, J.
    Li, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/200575.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    specific emitter identification
    geometric features
    frequency drift
    adaptive fractional spectrogram
    support vector machine
    emiter
    cechy geometryczne
    dryf częstotliwości
    spektrogram
    Opis:
    Specific emitter identification (SEI) is a technique for recognizing different emitters of the same type which have the same modulation parameters. Using only the classic modulation parameters for recognition, one cannot distinguish different emitters of a same type. To solve the problem, new features urgently need to be developed for recognition. This paper focuses on the common phenomenon of frequency drift, defines geometric features of frequency drift curve and, finally, proposes a practical algorithm of specific emitter identification using the geometric features. The proposed algorithm consists of three processes: instantaneous frequency estimation based on the adaptive fractional spectrogram, feature extraction of frequency drift curve based on geometric methods for describing a curve and recognition process based on support vector machine. Simulation results show that the identification rate is generally more than 98% above –5 dB of signal to noise ratio (SNR), and real data experiment verifies the practical performance of the proposed algorithm.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 1; 99-108
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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