Transformer partial discharge identification method of affinity propagation algorithm based on manifold distance (AP-MD)

Abstract

The invention discloses a transformer partial discharge identification method of affinity propagation algorithm based on manifold distance (AP-MD). The method comprises the steps of constructing threetransformer internal partial discharge models of corona discharge in oil, creeping discharge in oil and air gap discharge; using box dimension and information dimension for the extraction of the feature quantity of a gray-scale map; obtaining the definition of the manifold distance and its calculation formula; performing the method steps of the AP-MD; and obtaining the setting principle of the initial value of the k-nearest neighbor k. The method of the invention improves the shortcoming of difficulty in accurately identifying structural complex data by the traditional AP, and at the same time, is applied to the identification of the three discharge modes of the corona discharge in oil inside a transformer, the creeping discharge in oil and the air gap discharge. The experimental resultsshow that the results of the AP-MD are better than the results of the traditional AP, K-means clustering and fuzzy C-means clustering, and the accuracy of identification is increased.

Description

technical field [0001] The present invention relates to the field of electrotechnical technology, in particular to a transformer partial discharge pattern recognition method based on manifold distance neighbor propagation clustering, wherein the neighbor propagation clustering algorithm (Affinity Propagation, referred to as AP) is based on the manifold distance neighbor propagation clustering algorithm (Affinity Propagation Algorithm Based on Manifold Distance, referred to as AP-MD). Background technique [0002] Power transformers are one of the most important devices in the power grid. If a fault occurs, the power transformer will cause partial or even large-scale power outages in the power grid, causing huge economic losses. A large number of fault statistics show that most accidents of transformers are caused by insulation aging and damage, and partial discharge is one of the important reasons for insulation aging and damage. Effective identification of partial dischar...

AI Technical Summary

Problems solved by technology

However, when the traditional neighbor propagation clustering algorithm is used to identify the partial discharge pattern of transformers, the similarity matrix of the neighbor propagation clustering algorithm is constructed based on the Euclidean distance between data points, which causes the local consistency of the clustering results and cannot reflect the clustering results. Global consistency of classes and potentially complex structure of data

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