Icing thickness prediction method and device for power transmission line

Abstract

The invention relates to an icing thickness prediction method and device for a power transmission line. The method comprises the steps of obtaining a meteorological data prediction value of the powertransmission line at a future moment; inputting the future moment into an auto-regressive integral moving average model, and obtaining an icing thickness initial value output by the auto-regressive integral moving average model; inputting the meteorological data prediction value of the power transmission line at the future moment into a support vector regression model, and obtaining an icing thickness error value output by the support vector regression model; according to the icing thickness initial value and the icing thickness error value, determining an icing thickness final prediction value of the power transmission line at a future moment; according to the technical scheme of the invention, the prediction accuracy of the icing thickness of the power transmission line is improved through the combined prediction model.

Description

technical field [0001] The invention relates to the technical field of intelligent operation and maintenance of electric power systems, in particular to a method and device for predicting the thickness of ice coating on transmission lines. Background technique [0002] With the development of the economy and the progress of the society, people have increasingly strong requirements for the stability and safety of the power system. The key to the intelligent operation and maintenance of the power system is to ensure the safety and reliability of the power facilities, as well as related accurate prediction and maintenance. one of the problems. Scholars at home and abroad have established many icing prediction models on the issue of icing on transmission lines. From the perspective of the principles of the models, they can be roughly divided into mechanism models, traditional statistical models, and intelligent computing models. Mechanism models focus on the generation process o...

AI Technical Summary

Problems solved by technology

Scholars at home and abroad have established many icing prediction models on the issue of icing on transmission lines. From the perspective of the principles of the models, they can be roughly divided into mechanism models, traditional statistical models, and intelligent computing models. Mechanism models focus on the generation process of icing , using the relevant theories of fluid mechanics, thermodynamics and meteorology to model and analyze the ice-coated conductor from the perspective of internal mechanism. It is difficult to promote in practical applications; the traditional statistical model does not need to consider the actual icing process and physical mechanism, but predicts through statistical analysis of past data; the traditional statistical model is only a simple analysis of the data processing, the generalization performance is not strong; intelligent computing models use artificial intelligence algorithms to find ways, but in fact many intelligent computing models ignore the effect of time accumulation, and do not dig out the characteristic quantities contained in historical data well, thus Affected the accuracy of prediction of transmission line icing thickness

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