Abstract
In all fault detection techniques, fault signal feature extraction is crucial and challenging. Convolutional neural network and continuous wavelet transform (CNN)-the based technique of SLGF detection in distribution power system protected by Petersen coil proposed in this paper. By using the CWT on the zero-sequence current signals of the faulty feeder and healthy feeders, time-frequency RGB scale images acquired. A few RGB scale pictures under different types of faults circumstances, which will extract characteristics of RGB scale image adaptively, trained that which is CNN. A trained CNN could extract features and detect faulty feeder simultaneously. The distribution power system protected by Petersen coil simulated in MATLAB simulated and record the Zero Sequence Current ZSC and analyzed it by Orange big mining tool. The efficacy and the performance the suggested method for detecting faulty feeders are compared and confirmed under various faults scenarios, two methods for identifying faulty feeders on conventional machine learning and artificial feature extraction for comparison, concluded that The CNN best to detected the fault in different condition, for Test 1and Test 2 were classification accuracy 100%, and Test 3 was 99.5%, and Test 4 was 70.9%.
