Multifeature Extraction and Semi-Supervised Deep Learning Scheme for State Diagnosis of Converter Transformer

The research on intelligent diagnostic models for power transmission and transformation equipment has attracted increasing interest. In the design of practical diagnostic models, the supervised learning strategy is often used. This put forward higher requirements for the number of labeled data. However, in industrial applications, the cost of collecting a large number of relevant data and effectively labeling is high. At the same time, in the case of unknown faults, the lack of prior knowledge makes accurate classification difficult to achieve. This work designs a semi-supervised hybrid framework to diagnose the state of the converter transformer in terms of the labeled data which is limited. The framework consists of three main modules: multifeature graph generation, semi-supervised training, and soft voting decision. The graph generation module encodes the vibration signal into time, frequency, and energy graphs by calculating the Gramian angular summation fields (GASF). The semi-supervised learning module integrates unsupervised learning and supervised learning strategies trained by partial label information and data reconstruction errors. The decision-making module makes multifeature decisions through soft voting. The effectiveness of the hybrid framework is verified by comparative experiments. The experimental results show that the scheme can learn and diagnose intelligently under harsh datasets.