Abnormality detection method and device for transformer monitoring data

Abstract

The embodiment of the invention discloses an anomaly detection method and device for transformer monitoring data. The method comprises the following steps: acquiring a to-be-detected sequence of on-line monitoring data of a transformer; constructing an abnormal data identification model by adopting time sequence modeling and an isolated forest algorithm; constructing an abnormal type recognition mode based on an improved multi-dimensional SAX vector representation method; adopting the abnormal data identification model to identify abnormal data of the to-be-detected sequence; determining the abnormal type of the abnormal data by adopting the abnormal type identification mode, wherein the abnormal type comprises an invalid abnormal mode and an effective abnormal mode; and when the exception type is the invalid exception mode, carrying out relevance verification on the exception type by adopting sequence relevance analysis. According to the scheme, on the basis of effectively recognizing abnormal data information, abnormal modes can be deeply analyzed, and effective abnormal points and invalid abnormal points can be accurately distinguished.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to an abnormal detection method and device for transformer monitoring data. Background technique [0002] With the wide application of big data and Internet of Things technology in power transformer status perception, operation and maintenance, the scale of transformer monitoring data shows an exponential growth trend, providing an important data basis for comprehensive status evaluation and prediction of equipment. However, affected by various emergencies, the equipment online monitoring system will inevitably generate some abnormal data. Reliable identification of abnormal data and effective differentiation of its patterns are important foundations for efficient cleaning of online monitoring data and accurate grasp of equipment operating status. Existing anomaly detection research involves methods based on clustering, classification, and statistics. [0003] Exis...

AI Technical Summary

Problems solved by technology

[0003] Existing anomaly recognition algorithms such as the selection of initial cluster centers in clustering algorithms will have a greater impact on clustering convergence; classification algorithms are suitable for data sets with a large number of abnormal samples, and in most scenarios, abnormal data are Very few parts; the statistical method will reduce the recognition effect due to the large interference of the selected sample set, and when the sample data fluctuates seriously

Moreover, the current existing methods are not deep enough for how to effectively distinguish different types of anomaly patterns, and further research is needed

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