Voltage sag sensitive equipment fault level evaluation method based on data mining

Abstract

The invention discloses a voltage sag sensitive equipment fault level evaluation method based on data mining, and the method comprises the steps: selecting a voltage sag characteristic attribute, calculating the fault rate of sensitive equipment according to monitoring data, and determining the fault level of the sensitive equipment according to the fault rate; converting the monitoring data intoa database consisting of a voltage sag characteristic attribute and a sensitive equipment fault level; mining an association rule between the voltage sag characteristic attribute and the fault level of the sensitive equipment, and setting two groups of support degree and confidence degree limit values; and matching the voltage sag characteristic attribute of the actual scene with the mined association rule by using an improved gray target theory to obtain the fault level of the sensitive equipment corresponding to the actual scene. According to a large number of voltage sag events, as long asa set of voltage sag characteristic attributes corresponding to the fault scene is constructed, the voltage sag characteristic attributes of the scene can be matched with the mined association rules,and the fault level of the sensitive equipment of the scene is obtained.

Description

technical field [0001] The invention relates to the technical field of fault level assessment of voltage sag sensitive equipment, in particular to a data mining-based fault level assessment method for voltage sag sensitive equipment. Background technique [0002] Voltage sag will bring huge economic losses to sensitive users, and has become a major problem of power quality. Evaluating the fault level of sensitive equipment caused by voltage sag can provide a theoretical basis for power companies and users to take reasonable measures to reduce the economic losses of sensitive users. [0003] However, the existing evaluation methods for sensitive equipment can be summarized as follows: 1) Obtain the tolerance curve of sensitive equipment through a large number of experimental tests; 2) Calculate the failure rate of sensitive equipment through the uncertainty evaluation method. These methods only aim at a node or some sensitive equipment, none of which evaluates the failure le...

AI Technical Summary

Problems solved by technology

Although the occurrence of a single voltage sag event in the power grid is random, according to the long-term sag monitoring data of different nodes, there is always a certain correlation between the fault level of sensitive equipment and the characteristic properties of voltage sag

Moreover, with the continuous expansion of the power quality monitoring scale of modern power grids, a large number of sag monitoring data are stored, but these data are still not fully utilized

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