Monitoring system and monitoring method for secondary equipment of power system

Abstract

The invention relates to the field of secondary equipment monitoring, and particularly discloses a monitoring system and a monitoring method for secondary equipment of a power system. The method comprises the following steps of: performing global high-dimensional correlation feature extraction on various measurement value data acquired by secondary equipment deployed at a single power grid node and various data of a plurality of power grid nodes through a context-based encoder model, and calculating the distance between feature vectors to serve as a parameter topological feature matrix; further extracting associated information of the data sample due to the feature information and the irregular topological structure information by using a graph neural network, and calculating a label category evolution factor between the measured value topological feature vector and the measured value feature vector to serve as a loss function value training model; the accuracy of classification is improved. Therefore, each piece of power grid node data can be compressed, so that the monitoring accuracy and timeliness of the secondary equipment are ensured.

Description

technical field [0001] The present application relates to the field of secondary equipment monitoring, and more particularly, to a monitoring system and a monitoring method for secondary equipment of a power system. Background technique [0002] Secondary equipment is the equipment that monitors, controls and protects the primary equipment in the power system, and does not have a direct connection with electrical energy. Secondary equipment includes voltmeter, ammeter, power meter and electric energy meter, mainly to obtain relevant parameters of power equipment to judge whether the power grid system is running stably. [0003] However, with the increase in the number of secondary equipment, the related monitoring data soars, which reduces the monitoring accuracy of the secondary equipment and prolongs the monitoring time of the equipment. Therefore, in order to compress the monitored data to ensure the monitoring accuracy and timeliness of the secondary equipment, a monito...

AI Technical Summary

Problems solved by technology

[0003] However, as the number of secondary equipment increases, the relevant monitoring data soars, which reduces the monitoring accuracy of secondary equipment and prolongs the monitoring time of equipment

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