Compressive Sensing of Vibration Signals of Power Transformer

Abstract

Vibrations signals of an operated transformer are mainly originated from winding vibration and core vibration. By monitoring the vibration signals in transformer tank via on-line vibration monitoring system, it is possible to capture the incipient mechanical failures at all time while transformer is working. However, a large amount of vibration signals of transformer would produce in the on-line vibration monitoring system, which is a heavy burden for data storage and transmission in the construction of Ubiquitous Electric Internet of Things (UEIOF). To solve this problem, this paper presents a method to compress the vibration signals of transformer according to the compressive sensing theory when considered the spare features of vibration signals. The sparse random matrix, the dictionary and the reconstruction algorithm are built and trained to compress and restore the vibration signals of power transformer. The calculated results of a 35kV oil-immersed transformer in load experiment have shown that the original vibration signals are compressed to 1/8 with the average reconstruction error of 0.06% by the proposed method.