Arc Flash
To deal with the issue of insignificant series arc fault characteristics in disturbing loads, this paper proposes a voltage-type series arc fault detection technique that utilizes a convolutional bidirectional long- and short-term memory neural network (CNN-BiLSTM) combined with the Keplerian optimization algorithm (KOA) and Attention Mechanism (AM). Moreover, through experimental verifications, the accuracy of detecting experimental loads during the formation of series arc faults can exceed 99%, which demonstrate the effectiveness of the proposed method.
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This study presents a method for detecting arc faults by combining load identification and MLP-SVM. The method addresses the issue of interfering loads on arc fault detection and the lack of significant arc fault features in some loads. Initially, the eigenvalues of the line currents for single and mixed loads are extracted in the time domain, both during arc fault and normal operation. Subsequently, load identification is performed using a complex matrix calculation method. After identification, an eigenmatrix and history matrix are created for each load.
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The excel spreadsheet files provide access to a functional Arc Flash Calculator that includes various equipment configurations (VCB, VCBB, HCB, VOA and HOA), Incident Energy and voltage ranges.
As equipment’s configuration and input fields are presented, the user will be able to key entry specific data using the proper input fields and/or pull down menus provided. All variables and constants are defined. When all variables inputs have been entered, and proper equipment configuration has been selected, the Calculator will solve the equations and the answer is returned.
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VideoSupplement "SEM-assisted (LVEM-assisted) isopotential mapping of dielectric charging of the nonwoven fabric structures using Sobel–Feldman operator (Sobel filter)" for our article in russian journal (translated in English).
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