Abstract
This paper presents the application of similarity mining technology in transformers state early warning. Depending on the complexity of power transformer characteristics, similarity mining technology provides a transformer fault diagnosis model based on mass data. The analysis of historical data based on a large number of operating states is the foundation of the transformer normal state model which is derived by similarity mining technology. This paper describes the modeling process and the application of early warning in detail. The model also can be improved in diagnostic effect by rich training samples. The example also demonstrates the effectiveness of this method.
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