A Machine Learning approach to fault detection in transformers by using vibration data

Abstract

Transformer Vibration Technique is considered an effective method to monitor structural elements of transformers, in particular, to detect loose or deformed windings. As it is well known, vibrations vary with the sensor location on the transformer tank, which makes the number and the placement of sensors critical aspects for fault detection. In this paper, we investigate this issue by analyzing vibration spectra collected from various sensors installed on the tank of a typical oil filled power transformer operating under two limit cases, namely absence or presence of clamping looseness on windings. Support Vector Machines (SVM) are employed and an extensive analysis is performed to understand the informativeness of data corresponding to various sensors so as to figure out the appropriate number of sensors and their best location. This way fault detection is eventually achieved with a reduced and optimized number of sensors, resulting in a significant saving of time and costs.