Method for filtering online monitoring data of arrester leakage current based on morphological principle

Abstract

The existing method can achieve multilevel decomposition of online monitoring data by wavelet analysis to effectively suppress white noise and pulse noise, but has the disadvantage of complex modeling and large calculation amount. The invention provides a method for filtering online monitoring data of an arrester leakage current based on a morphological principle. An arrester online monitoring device collects the online monitoring data of the arrester leakage current, and transmits the collected data to a computer through an optical fiber; and the computer processes the data based on a morphological algorithm and outputs the processing result. The morphological algorithm comprises the following steps: sequentially executing primary erosion operation, primary expansion operation, secondary erosion operation and secondary expansion operation; and finally weighting the online monitoring data of the arrester subjected to secondary expansion operation. The method provided by the invention can be used for carrying out effective de-noising and smooth processing of the online leakage current monitoring data to ensure that the collected data can reflect the real work state of reaction equipment.

Description

technical field [0001] The invention relates to the processing of the on-line monitoring data of the leakage current of the arrester, in particular to a filtering method of the on-line monitoring data of the leakage current of the arrester based on the morphological principle. Background technique [0002] The data collected by the online monitoring device must have certain high-frequency interference signals, especially for some equipment state quantities with relatively small values, such as the leakage current of capacitive equipment and the leakage current of lightning arresters. Overwhelmed by noise. In order to truly reflect the insulation state of the equipment, it is necessary to analyze and process the data. [0003] Currently commonly used data signal processing methods mainly include 3δ criterion, median filter, 53H algorithm, and wavelet transform multi-resolution analysis (wavelet analysis for short). Although the 3δ criterion, median filter, and 53H algorithm...

AI Technical Summary

Problems solved by technology

Although the 3δ criterion, median filter, and 53H algorithm can effectively eliminate impulsive noise (picking points), the processing of white noise is not ideal, and the state quantities obtained by on-site state detection must contain rich white noise. If the above Method processing, the data still has jagged fluctuations, which cannot reflect the current status of the equipment

[0004] Using wavelet analysis to decompose the online monitoring data into multiple layers, this method can effectively suppress white noise and impulse noise, but the wavelet analysis modeling is complex and the amount of calculation is large

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