The application provides a power distribution network cable fault detection and positioning method with anti-
noise enhancement. By injecting a multi-cycle test
signal and collecting the reflected
signal, combining the m sequence denoising characteristics and the
adaptive wavelet transform, the multi-cycle reflected
signal is denoised and normalized to improve the
signal quality. The multi-cycle secondary cross-correlation weighted power
spectrum function is averaged, and the anti-
noise enhanced fault positioning is realized through IFFT
processing. At the same time, the
time domain features (such as amplitude, phase, envelope) of the filtered signal of the first cycle are extracted, and the final secondary cross-correlation weighted power
spectrum function IFFT value is fused into a high-dimensional
feature vector, which is input into a
deep learning model based on
convolutional neural network, LSTM and attention mechanism. The attention mechanism weights the secondary cross-correlation weighted power
spectrum function IFFT value, extracts the
peak value feature, and improves the positioning accuracy. The method realizes accurate classification and positioning of cable faults, has high precision and anti-interference ability, and is suitable for cable fault detection in complex environments, providing
technical support for stable operation of the power
system.