DOI: 10.3390/machines12030185 ISSN: 2075-1702

An Improved Fault Localization Method for Direct Current Filters in HVDC Systems: Development and Application of the DRNCNN Model

Xiaohui Liu, Haofeng Liu, Hui Qiao, Sihan Zhou, Liang Qin
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Control and Optimization
  • Mechanical Engineering
  • Computer Science (miscellaneous)
  • Control and Systems Engineering

This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.

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