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Wyświetlanie 1-2 z 2
Tytuł:
Fault location in EHV transmission lines using artificial neural networks
Autorzy:
Bouthiba, T.
Powiązania:
https://bibliotekanauki.pl/articles/907284.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
linia przesyłowa
detekcja uszkodzeń
lokalizacja uszkodzeń
sztuczna sieć neuronowa
transmission line
fault detection
fault location
artificial neural networks
Opis:
This paper deals with the application of artificial neural networks (ANNs) to fault detection and location in extra high voltage (EHV) transmission lines for high speed protection using terminal line data. The proposed neural fault detector and locator were trained using various sets of data available from a selected power network model and simulating different fault scenarios (fault types, fault locations, fault resistances and fault inception angles) and different power system data (source capacities, source voltages, source angles, time constants of the sources). Three fault locators are proposed and a comparative study of the proposed fault locators is carried out in order to determine which ANN fault locator structure leads to the best performance. The results show that artificial neural networks offer the possibility to be used for on-line fault detection and location in transmission lines and give satisfactory results.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 1; 69-78
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection and classification of short-circuit faults on a transmission line using current signal
Autorzy:
Coban, Melih
Tezcan, Suleyman S.
Powiązania:
https://bibliotekanauki.pl/articles/2086833.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
transmission line
fault detection
fault classification
support vector machine
SVM
linia przesyłowa
wykrywanie uszkodzeń
klasyfikacja błędów
maszyna wektorów nośnych
Opis:
This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 4; e137630, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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