Series Arc Fault Detection Based on Multimodal Feature Fusion

Abstract

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.

Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

“…The occurrence of an arc is usually accompanied by phenomena such as arc light, arc sound, and electromagnetic radiation. Therefore, temperature, photosensitive, antenna, and other sensors were often used to monitor arc faults in the early stages [6][7][8][9][10]. However, the effectiveness of these non-contact methods is limited by the installation location of the 2 of 14 sensors and is easily affected by external environmental factors.…”