arc
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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To address the problem that the arc fault characteristics of certain loads are not significant and the line current characteristics cannot be detected effectively. In view of the wide application of a large number of nonlinear loads, it is difficult to distinguish the traditional current features from the load current features of nonlinear loads. Current features compared to those extracted from load end voltages, the latter are less affected by the load type, making the detection of faulty arcs more reliable.
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