A fault line detection method for indirectly grounding power system based on quantum neural network and evidence fusion

A new fault diagnosis model based on intergrated quantum neural networks (QNNs) and dempster-shafer (DS) evidence theory to detect the fault line for indirectly grounding power system is proposed. According to fast fourier transform (FFT) and wavelet packet transform (WPT) algorithms, the fault features extracted from zero sequence current are used to train the quantum neural networks, then DS evidence theory is used for global diagnosis to gain a unified line selection result from the outputs of the networks. The simulation results indicate that the model has strong adaptability to the fault line detection for indirectly grounding system, and the process is not sensitive to earth mode, inception angles and transition resistance. The issues are solved, which are low accuracy of the detecting process with single criterion, slow convergence speed and long diagnosis time of the high dimension inputs neural netwok.