基于多特征融合神经网络的串联电弧故障识别技术

Traditional low-voltage protection devices such as low-voltage circuit breakers, fuses and other protection devices cannot effectively detect series arc faults which are caused by poor contact or insulation failure, therefore, how to accurately detect series arc faults has become a hot issue in current research. In this paper, the detection method based on current waveform is used for in-depth research. By building an arc fault platform to simulate series arc faults, the data of normal and arc faults under different loads are obtained. On this basis, a neural network algorithm for multi-feature fusion is established. The model is optimized by using mini-batch gradient descent, exponential decay learning rate, and Adam's optimization algorithm. The research results show that the accuracy and recall rate of the algorithm in this paper can reach 98% and 99%, respectively, which has a higher recognition rate than the SVM and BP neural network algorithms. The research provides a new algorithm for series arc fault identification, and expands new ideas for the research in this direction.