A method and system for predicting the communication state of an electric power metering automation terminal

Abstract

The invention provides a communication state prediction method of an electric power measurement automation terminal. The communication state prediction method comprises the following steps: determining prediction terminals and an analysis range; selecting samples from a terminal of which the communication state is determined, and obtaining the historical communication data of each sample; setting each variable of a feature vector, screening each sample feature vector value from each piece of historical communication data, obtaining an expert sample library formed by each sample feature vector value, and screening the feature vector value of each prediction terminal in the analysis range; and carrying out Euclidean distance calculation on the feature vector value of each prediction terminal and the expert sample library, obtaining the sample and a sample communication state in the corresponding expert sample library when a calculation value of each prediction terminal meets a certain condition, calculating the amounts of the fault and normal sample communication states obtained by each prediction terminal, and taking the communication state corresponding to a largest amount value as the communication state predicted by each prediction terminal. The communication state prediction method is high in practicality and can improve fault prediction accuracy.

Description

Technical field [0001] The present invention relates to the technical field of power metering automation terminals, in particular to a method and system for predicting the communication state of a power metering automation terminal. Background technique [0002] With the continuous expansion of the scale of the power system and the increasing complexity of the structure, more and more automatic devices are applied to the power system. Once the power grid fails, a large amount of alarm information generated by automatic devices at all levels will quickly flood into the power system dispatching center, making it impossible for dispatchers to quickly determine the cause of the fault in a short time, which will cause serious problems to the normal operation of the power grid. Potential safety hazards and huge economic losses, so timely troubleshooting is of great significance for the safe and economic operation of the power grid. [0003] In the prior art, the power metering automatio...

AI Technical Summary

Problems solved by technology

At present, widely used fault diagnosis methods include: expert system, artificial neural network, Bayesian network, optimization technology, support vector machine, Petri network, fuzzy set theory, rough set theory and multi-agent technology, etc., but in actual In the application, due to the difficulties in the application of the above methods and the lack of accuracy, it is still common to find faults manually and then investigate and solve them on the spot.

Images