Method for extracting waveform feature of local discharge pulse current

Abstract

The invention relates to a method for extracting the waveform feature of a local discharge pulse current, comprising the following steps of: acquiring data of a local discharge signal of a transformer; automatically extracting a pulse waveform signal of the local discharge signal; calculating each microcosmic characteristic parameter of the extracted single discharge pulse waveform; and carrying out characteristic space dimension reduction on the microcosmic characteristic parameter of the local pulse waveform. By using the method, the microcosmic characteristics can be effectively extracted from continuous sampled waveform signals; the defect that the obtained local discharge data cannot be sufficiently utilized because most of current digital local discharge instruments carry out statistic analysis treatment by only utilizing the microcosmic characteristic of local discharge data is overcome; single discharge pulse waveforms of various discharge types can be adaptively extracted from the acquired data, and effective dimension reduction of the waveform microcosmic characteristic can be carried out through an improved manifold learning algorithm, so that the low-dimension and effective discharge pulse waveform characteristic can be extracted.

Description

technical field [0001] The invention relates to a partial discharge pulse current waveform feature extraction method, which is suitable for a digital partial discharge instrument based on high-speed sampling and belongs to the technical field of transformer partial discharge detection and pattern recognition. Background technique [0002] The power transformer is one of the most important and expensive equipment in the power system, and its safe operation is of great significance. In field operation, partial discharge is one of the important reasons leading to the insulation deterioration of power transformers, especially as the capacity and voltage level of power equipment continue to increase, this problem becomes more serious. The detection and pattern recognition of partial discharge are important means to detect the insulation state of power transformers. [0003] Partial discharge detection is based on the phenomenon of electricity and light generated when partial dis...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for extracting waveform features of partial discharge pulse currents, which can effectively extract its microscopic features in continuous sampling waveform signals; overcome the problem that most of the current digital partial discharge instruments only use the macroscopic features of partial discharge data. Statistical analysis and processing cannot make full use of the lack of partial discharge data obtained; it can adaptively extract individual discharge pulse waveforms of various discharge types from the collected data, and effectively reduce the microscopic characteristics of the waveform through the improved manifold learning algorithm. Dimensions, extracting low-dimensional and effective discharge pulse waveform features

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