Power transformer fault diagnosis method based on multi source signal fusion and fast spectral correlation

Addressing the issues that signal measured by a single sensor can not provide a complete description of power transformer fault states and the problems that selection of signal features relies on manual experience, a method based on multi source signal fusion and Fast Spectral Correlation is produced for power transformer fault diagnosis. At first, the vibration signals from different locations on the surface of the transformer case are collected by a sensor array synchronously, and Correlation Function Weighting is proposed to fuse multi-source signals from multiple sensors in order to obtain the fused signal; then, the fused signals are subjected to Fast Spectral Correlation belonging to cyclic smooth theory in order to construct a sample set of images; finally, the Fast Spectral Correlation image samples are fed into MobileNetV3 model for training of transfer learning to obtain the fine-tuned neural network model, which completes power transformer fault diagnosis. Experimental results showed that the overall recognition accuracy of the method proposed reached 98.75%, which was 10.52% higher than the diagnosis of single sensor signal, and 10.86% higher than the diagnosis of other classical images, providing a new tool for transformer fault diagnosis based on vibration signals.