关键词: 故障识别, 图像处理, 深度学习, 故障判据, 归一化, 隐含层

Abstract: The currently trip fault identification method for distribution networks is mainly based on manual discrimination, which causes the problem of larger workload and lower accuracy. By combining image processing and deep learning technology, automatic identification of distribution network fault types can be realized. First, the telemetry current waveform is converted to a current value group with time stamps by image processing technology. Then feature vectors are constructed with telecommunicating signal, telecontrol signal and normalized current value. Based on the fault type criterion, the deep neural network model is built and trained to realize fault type identification. Finally, the model is optimized by adjusting the number of hidden layers and neurons in the deep neural network. The experimental results show that the types of trip fault can be identified quickly with the proposed method, that and the accuracy is better than existing methods, which means the new method is practical and effective.

Key words: fault identification, image processing, deep learning, fault type criterion, normalized processing, hidden layer