System and method for predicting insulator surface non-soluble deposit density

Abstract

The invention discloses a system and a method for predicting the insulator surface non-soluble deposit density. The system comprises an original data acquisition unit, a series gray neural network prediction unit, a parallel gray neural network prediction unit, an embedded gray neural network prediction unit, a NSDD (Non Soluble Deposit Density) predicted value output unit and an NSDD early warning unit. Insulator surface non-soluble deposit density data and local meteorological data are inputted into the series gray neural network prediction unit, the parallel gray neural network prediction unit and the embedded gray neural network prediction unit, and prediction is performed on an insulator NSDD value through the three prediction units; then judgment is performed on the prediction accuracy of the three prediction unit by using a test sample, output of the prediction unit with high prediction accuracy is enabled to act as an insulator NSDD predicted value; and an early warning is given out through the NSDD early warning unit according to whether predicted values outputted by two or more of the prediction units reach a preset graded early warning threshold or not.

Description

technical field [0001] The invention belongs to the technical field of external insulation of power systems, and more specifically relates to a system and method for predicting the density of insoluble deposits on the surface of an insulator. Background technique [0002] Due to the accumulation of dirt on the surface of insulators under normal working voltage, pollution flashover accidents are prone to occur in rainy, foggy and other bad weather, which poses a serious threat to the safe and stable operation of the power system. It is very necessary to predict the pollution degree of insulators on transmission lines in order to prevent pollution flashover accidents in time. Non-Soluble Deposit Density (NSDD) is usually used to evaluate the degree of pollution on the surface of insulators. [0003] The gray model has been widely used in the prediction of the degree of pollution on the surface of insulators because of the advantages of less sample data required for modeling, ...

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Problems solved by technology

[0003] The gray model has been widely used in the prediction of the degree of pollution on the surface of insulators because of the advantages of less sample data required for modeling, no need to consider the distribution law and change trend, simple modeling and convenient operation. However, due to the lack of self-learning and self-organizing and self-adaptive ability, the ability to process information is weak, and cannot independently complete the prediction task

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