DC Arc Fault Risk Degree Evaluation Based on Back Propagation Neural Network

The low-voltage DC power system is widely applied in solar systems, electric vehicles, large data centers, etc. DC arc fault becomes threaten to the safe operation of the DC systems. When the arc fault is in different states, the control and isolation of the fault lines or devices should be different. In view of the above problems, this paper proposes an approach to evaluate the DC arc fault risk degree under different test conditions including electrode material, air pressure, current, etc. based on back propagation (BP) neural network algorithm. Firstly, the Otsu method is used to automatically select the threshold value to binarize the arc images. The connected region is traversed to denoise the binarized images to improve the clarity of the images. Secondly, the images are transformed into Hue, Saturation and Value (HSV) space to extract the color features of the images, and the color features are sent to the classifier constructed by the BP neural network to train the features to realize the recognition of different electrode materials. The morphological features and texture features of the images are extracted. According to the effective features, the risk degree of the arc fault is divided into three levels. The effective features are sent into the classifier to train the features to evaluate the arc fault risk degree levels. The results show that the accuracy of the algorithm is more than 95% for the evaluation of arc fault risk degrees and electrode material identification. This method has a significance to evaluate arc fault risk.