A Transfer Ensemble Learning Method for Evaluating Power Transformer Health Conditions with Limited Measurement Data

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Health condition evaluation of power transformers is of great importance for safe and reliable operation of power grids. The data-driven methods have been widely studied and applied. However, in practical applications, collecting high-quality data is difficult and expensive. Therefore, it is still challenging to build effective diagnostic models with insufficient labeled training data. This paper proposes a transfer ensemble model to address this data scarcity problem. Firstly, a one-dimensional convolutional neural network is pre-trained using the large source domain dataset. Then, the target model parameters are initialized with those of the source model and fine-tuned using the target dataset. A transfer strategy is proposed to decide what and where the diagnostic knowledge should be transferred from the source domain to the target domain. Finally, an ensemble model is built on the basis of a series of target models with different transfer strategies, which can further alleviate the over-fitting issue and improve the gener-alization ability. The experimental results validate the effectiveness of the proposed method and show the feasibility of practical applications.