Abstract
Background: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field.
Objective: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life.
Methods: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into characteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network.
Results: The accuracy of the final model for slice classification reached 100%.
Conclusion: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.
Keywords: Transformer winding, fault diagnosis, gramian angular difference field, convolutional neural network, moving windows, frequency response.
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