Differential hash algorithm-based voltage sag source classification method

Abstract

The invention provides a differential hash algorithm-based voltage sag source classification method, which specifically comprises the following steps of S1, reading a three-phase voltage change waveform of a monitoring point when voltage sag occurs at a certain position in a power distribution network in a time period T, exporting a voltage waveform P1, and performing picture processing on the voltage waveform P1 to obtain an integer matrix; s2, carrying out difference value calculation and processing on the integer matrix obtained by processing in the step S1 to obtain an oscillogram hash value of the voltage waveform P1, i.e., a fingerprint of the image; and S3, comparing the fingerprint similarity degree of the voltage waveform P1 and the voltage waveform P2 caused by different sourcesthrough the Hamming distance in the difference hash algorithm so as to judge the voltage sag source. Compared with an existing voltage sag source identification method, the method solves the problem of theoretical insufficiency of the composite voltage sag source, can identify difference of voltage waveforms caused by different sag sources through extraction of oscillogram features, and has the advantages of high accuracy and fine classification.

Description

technical field [0001] The invention relates to the technical field of power quality, in particular to a voltage sag source classification method based on a difference hash algorithm. Background technique [0002] The Institute of Electrical and Electronics Engineers (IEEE) defines a voltage sag as an instantaneous drop of the root mean square value of the voltage at a certain point in the power supply system to 0.1-0.9p.u., and returns to normal after a period of half a cycle to 1min. In the actual power grid, the causes of voltage sags are complex, and the waveforms of various voltage sags are also different. Usually, when events such as lightning strikes or high-power equipment start-up occur in the power grid, voltage sags can be caused. Voltage sag sources can be divided into single voltage sag sources and compound voltage sag sources. Identifying the source of voltage sag is of great significance for timely removal of faults, distribution of responsibilities, and gov...

AI Technical Summary

Problems solved by technology

However, the signal processing method has shortcomings such as artificially setting thresholds, strong subjective factors, and unintuitive recognition results, which will affect the recognition accuracy.

[0005] 2) The calculation method is too complicated and the amount of calculation is large

The above pattern recognition methods all have high recognition accuracy. However, the pattern recognition methods have typical characteristics such as large amount of calculation, long calculation time, difficulty in related parameter setting and model debugging, complex models, or abnormal matching, and related methods are not suitable. directly applied to the monitoring terminal

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