Identification method for cable partial discharge insulation defects

Abstract

The invention discloses an identification method for cable partial discharge insulation defects. The identification method comprises the following steps of: (1) acquiring a plurality of insulation defect models; (2) applying a voltage to the insulation defect models to acquire a partial discharge signal to form a Phi-Q-n signal diagram, wherein Phi represents the power frequency phase, Q represents the discharge amount, and n represents the number of times of partial discharge occurring within each of a plurality of small intervals into which Phi-Q plane is divided; (3) decomposing the Phi-Q-nsignal diagram by adopting a two-dimensional Littlewood-Paley empirical wavelet transformation to obtain an empirical wavelet coefficient sub-graph; (4) extracting a Tamura feature, a moment featureand a entropy feature of the empirical wavelet coefficient sub-graph to obtain a feature vector space; (5) performing dimension reduction processing on the feature vector space to select an effectivecharacteristic parameter; (6) inputting the effective characteristic parameter into a classifier for training and testing; and (7) inputting the partial discharge signal to be identified into the classifier trained and tested to output an identification result from the classifier.

Description

technical field [0001] The invention relates to an identification method, in particular to an identification method for identifying defects of cables. Background technique [0002] The power cable line is an important part of the urban power grid, and its safe and reliable operation is of great significance to the urban power grid. However, under the influence of the harsh environment and its own local defects, the cable insulation may be seriously aged, which may lead to line and equipment failures. Partial discharge is an important indicator to reflect the insulation performance of power cables. Partial discharge signals produced by different defects have different characteristics. By analyzing them, we can effectively judge the insulation aging status of power cables and prevent further expansion of faults. [0003] Therefore, it is of great significance to accurately grasp the types and characteristics of insulation defects of cables for the safe and reliable operation ...

AI Technical Summary

Problems solved by technology

However, as far as the current existing technology is concerned, in order to grasp the types and characteristics of cable insulation defects, it is first necessary to perform pattern recognition on the obtained partial discharge signals, and how to extract effective characteristic parameters in this process has become the focus and difficulty of research

Currently common feature extraction methods include time-domain analysis and statistical analysis. Among them, the results of time-domain analysis are seriously affected by electromagnetic interference and the recognition rate is low.

However, the statistical analysis method may have invalid information due to the small number of discharges and other reasons, and the recognition rate is low when the number of samples is small.

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