A partial discharge signal clustering method under multi-source discharge and interference superposition conditions

Abstract

The invention belongs to the field of electrical equipment partial discharge signal state detection, and discloses a partial discharge signal clustering method under multi-source discharge and interference superposition conditions, which comprises the following steps of 1 inputting sample data to be clustered; 2 setting an initial clustering number c=1, and inputting the initial clustering number;3 under the initial clustering number, selecting an optimal clustering algorithm from the four clustering algorithms of a fuzzy C-means clustering algorithm, a Gaussian mixture model clustering algorithm, a GK fuzzy clustering algorithm and a fuzzy maximum likelihood clustering algorithm; 4 continuously setting the clustering number as c+1, repeating the step 2 and the step 3 until c+1 is greaterthan 5, and entering the step 5; and 5 selecting the clustering number and the clustering method with the optimal clustering effectiveness index, analyzing the sample data according to the optimal clustering number and the clustering method, and outputting a clustering result to realize the automatic optimization of the partial discharge signal clustering algorithm and the clustering number.

Description

technical field [0001] The invention belongs to the field of partial discharge signal state detection of electrical equipment, and discloses a partial discharge signal clustering method under the condition of multi-source discharge and interference superposition. Background technique [0002] Partial discharge is one of the important parameters to reflect the insulation state of large-scale power equipment, and it is also an important means to discover potential defects of high-voltage insulation equipment. It has the characteristics of high sensitivity, timely and effective. At present, partial discharge state detection technology has been widely used in substation sites. At the same time, due to the influence of the complex electromagnetic interference environment on site, the partial discharge detection has long faced the technical bottleneck of electromagnetic interference, and the problem of false positives and missed negatives is very serious. [0003] Some existing c...

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Problems solved by technology

For UHF partial discharge detection methods, since the signal frequency is as high as several GHz, it is difficult to directly sample the original UHF signal due to cost reasons, so it is also impossible to use time-frequency characteristics to separate multi-source partial discharge signals

[0004] At the same time, some existing conventional clustering methods have different adaptability to different problems, such as the number of clusters needs to be manually determined, and the optimal number of clusters cannot be automatically obtained; it is sensitive to the selection of initial cluster centers and cannot Insufficient clustering of arbitrary-shaped data clusters often leads to classification errors or failures, so it still cannot meet the needs of automatic and intelligent partial discharge state detection

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