This study addresses fault identification in differential protection of a V/x-type traction transformer used in a highspeed railway. To quickly and accurately identify an internal short circuit in a traction transform...
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This study addresses fault identification in differential protection of a V/x-type traction transformer used in a highspeed railway. To quickly and accurately identify an internal short circuit in a traction transformer, a hybrid algorithm is developed that combines intrinsicmodefunction (IMF) energyentropy with the correlation dimension from chaos theory. IMF energyentropy and correlation dimension are sufficiently fast and sensitive to reflect the dynamic changes in the differential-current signal from the traction transformer using different metrics;thus, this hybrid method can effectively identify an internal short circuit and magnetising inrush. Real-time simulations and actual measurements of faults illustrate the validity of the proposed method.
The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical mode Decomposition ...
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ISBN:
(纸本)9789881563903
The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical mode Decomposition (EMD) threshold denoising and Support Vector Machine (SVM) is proposed to classify these characteristics. First, the EMD threshold denoising method is introduced to denoise the raw signals of the drill string vibration. Second, the features of these characteristics are selected by the intrinsicmodefunction (IMF) energyentropy and marginal spectral energy. Last, the drilling conditions are classified and identified by the Support Vector Machine (SVM). The simulation results show that the identification accuracy of the proposed method is higher than the conventional methods.
The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical mode Decomposition(...
详细信息
The characteristics of the normal and stick-slip vibration signals reflect the drilling conditions, which is significant in recognizing them. In this paper, a new method that combines the Empirical mode Decomposition(EMD) threshold denoising and Support Vector Machine(SVM) is proposed to classify these characteristics. First, the EMD threshold denoising method is introduced to denoise the raw signals of the drill string vibration. Second, the features of these characteristics are selected by the intrinsicmodefunction(IMF) energyentropy and marginal spectral energy. Last, the drilling conditions are classified and identified by the Support Vector Machine(SVM). The simulation results show that the identification accuracy of the proposed method is higher than the conventional methods.
To quickly and accurately detect MMC-HVDC transmission line faults and identify the fault types, a new transient protection scheme based on one-terminal transient current signal is presented. MMC has the attenuation c...
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ISBN:
(纸本)9781538622124
To quickly and accurately detect MMC-HVDC transmission line faults and identify the fault types, a new transient protection scheme based on one-terminal transient current signal is presented. MMC has the attenuation characteristic to the high frequency transient current components. By using empirical mode decomposition (EMD) and intrinsicmodefunction (IMF) energyentropy, the energy distribution of transient current in frequency domain is quantified. The IMF energyentropy is employed as a fault detection criterion to distinguish DC line faults from AC faults. Meanwhile, the trend of transient current, which can be extracted by using the moving average filter (MAF), is obviously dissimilar for different types of DC line faults. DC line faults can be classified into various categories based on this feature. RTDS test results demonstrate that the proposed scheme is able to detect and classify' MMC-HVDC transmission line faults under different fault conditions.
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