Management line loss abnormity identification method based on data mining

Abstract

The invention relates to a management line loss abnormity identification method based on data mining. The technical points comprise: step 1, carrying out sub-sequence segmentation on preprocessed management line loss time sequence data by adopting a sliding window method; step 2, constructing a time sequence prediction model based on a neural network, obtaining a predicted value of a management line loss sub-sequence, and judging the sub-sequence of which the difference range between the predicted value and an actual measurement value is greater than a preset threshold value as an abnormal sub-sequence; step 3, extracting characteristic variables of the abnormal sub-sequences, establishing a management line loss characteristic sample set, and clustering by adopting three different algorithms; and step 4, performing cluster matching on the three clustering results, obtaining a final clustering result by adopting a majority voting clustering integration method, and comparing the difference between the number of objects in the cluster and a preset threshold value to obtain a specific classification condition of the management line loss abnormal sub-sequence. The abnormal condition ofline loss can be quickly and accurately identified and managed, and better stability and practicability are achieved.

Description

technical field [0001] The invention belongs to the technical field of abnormal line loss detection, and relates to an abnormal identification method for line loss management, in particular to an abnormal identification method for line loss management based on data mining. Background technique [0002] With the acceleration of industrialization and urbanization and the continuous upgrading of consumption structure, my country's energy demand, especially electricity demand, is showing a rigid growth trend. The power loss of the distribution network, especially the management line loss, has always been high, and the problem of line loss of public lines and low-voltage users in the station area is more significant, resulting in a considerable proportion of the line loss of the distribution network in the entire power grid, causing a lot of damage to the country. waste of resources. The cause of distribution network line loss is more complicated, and the management line loss is...

AI Technical Summary

Problems solved by technology

Although the K-means algorithm is simple in principle, easy to implement, and fast in convergence, the clustering results largely depend on the parameters and initialization, and a single clustering algorithm cannot accurately reveal the variety of data presented by various data sets. cluster structure

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