Online abnormality detection method for transformer vibration

Abstract

In view of how to analyze the operating state of a transformer and perform fault diagnosis based on a vibration method, the embodiment of the invention discloses an online abnormality detection methodfor transformer vibration. The method comprises: obtaining new data samples about a vibration signal of the transformer operating state within a preset time period; extracting the characteristic parameters of the new data samples based on wavelet packet analysis; completing the learning of the new data samples based on a fast convex hull algorithm, training and updating a single-class abnormalitydetector model; and calling the updated single-class abnormality detector model to perform online abnormality detection of the current transformer vibration. According to the online abnormality detection method for transformer vibration, since the incremental learning algorithm is used to learn only the new data samples within the preset time period, the new data can be processed in time and effectively, the new training samples can be learned online in real time, and rapid upgrade of detection models can be achieved. At the same time, time and space requirements of the model update can be reduced.

Description

technical field [0001] The invention relates to the field of electric power, in particular to an online abnormal detection method for transformer vibration. Background technique [0002] The power transformer is an important equipment in the power system, and its operating status has an important impact on the safety and economic benefits of the power system. The surface vibration of the transformer mainly comes from the vibration of the winding and iron core under current and voltage excitation. Theoretical analysis and practical experience show that the working state of the winding and iron core can be analyzed and fault diagnosis can be carried out through the transformer vibration signal. The vibration method is used to analyze the operating status and fault diagnosis of transformers. Scholars at home and abroad have done a lot of research work and achieved considerable results. Because the transformer vibration signal feature extraction is the premise and basis of the ...

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Problems solved by technology

However, most of the data of the existing research results come from laboratory or test transformer simulation. Since the surface vibration of the transformer is affected by multiple factors, the surface vibration signal of the transformer in actual operation is different from the theoretical analysis and the transformer surface obtained under the laboratory test conditions. Significant difference in vibration signal

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