Fault tidentification method of outage transmission line based on convolutional neural network and wavelet packet decomposition

When the transmission line is in a hot standby state, a short circuit fault may still occur on the shutdown circuit. Accurate judgment of the fault state of the shutdown line can effectively avoid impact on power system and facilitate the failure troubleshooting. Therefore, it is necessary to identify the fault state of outage transmission line. For double-circuit transmission lines on the same tower, an outage line fault identification method using spectrum graph generated by wavelet packet decomposition as a convolutional neural network (CNN) input is proposed. To reduce the error of human extraction features, the faulty three-phase voltage waveform of the outage transmission line are measured, and wavelet packet decomposition is used to obtain three-phase voltage waveform spectrum maps, the features are extracted by CNN, meanwhile, CNN also undertakes the task of fault classification. The fault identification effect of this method is verified by simulation experiments. Based on the actual data of 3 lines in Hebei Province, 500 kV double-circuit transmission lines on the same tower models was established in ATP-EMTP to obtain the waveform of voltage, and 10 dB Gaussian white noise was added to the measured voltage waveform for simulating the error generated by various factors in the field. The results show that the identification accuracy of fault status on the hot standby line is 99.98%, which provides reference for fault diagnosis and troubleshooting of outage lines to a certain extent.