| Summary: | Partial discharge is a common fault type in the operation of power equipment. Recently, deep learning methods have shown great potential in partial discharge (PD) diagnosis. These methods construct a fitting relationship between input and output with mass training samples. Due to the scarcity of PD samples, it is hard to train a classification model. Thus, it is challenging to apply traditional deep learning methods for diagnosing PD. To overcome this issue, this paper introduces a siamese fusion network to diagnose PD. This method comprises three main steps. First, an ultra-high-frequency (UHF) sensor generates two spectrums from power equipment, including phase resolved partial discharge (PRPD) and phase resolved pulse sequence (PRPS). Based on a few-shot learning strategy, a support set is constructed. There are four different types of PD samples and a normal sample. Then, two siamese networks are employed to estimate similarity scores between a test sample and support set samples. One network measures similarity scores in PRPS, and the other measures similarity scores in PRPD. Based on similarity scores, initial diagnosis results are generated. Last, a simple and effective decision fusion technique fuses initial diagnosis results. The final diagnosis result can be generated by jointly exploiting complementary information in two spectrums. With limited training samples, experimental results show that the proposed SFN method can achieve an outstanding diagnosis performance, compared with several classical classification methods.
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