An Unsupervised Approach to Power Transformer Early Fault Warning Based on PMCAEN and SVDD

Early fault warning is critical for long-term stable operation of power transformer. However, slight mechanical fault of the power transformer does not lead to obvious changes in the vibration signal, which undoubtedly increases the difficulty of early warning. To solve this problem, this article proposes an early fault warning scheme for power transformer using vibration signal. Specifically, a new unsupervised learning framework, parallel multiscale convolutional autoencoder network (PMCAEN), is first developed to capture multidimensional and multiscale feature information of power transformer. Subsequently, support vector data description is established, and the reconstruction error obtained by PMCAEN under normal conditions is used as new observation information to construct the judgment domain of degraded state. The effectiveness of the proposed method is verified by the data collected from a 220-kV power transformer. The experimental results show that the proposed method achieves higher early warning accuracy with respect to the compared algorithms.