Modeling method for power transmission line icing thickness prediction model based on PR-KELM

Abstract

The invention provides a modeling method for a power transmission line icing thickness prediction model based on PR-KELM, and the method comprises the steps: a first stage: converting image data intoLBP image data, carrying out the dimension reduction through employing a PCA algorithm, calculating the gray histogram cascade, and obtaining the extracted image data features; performing feature screening on the meteorological data and the mechanical data by adopting a ReliefF algorithm, and removing highly related redundant features to obtain extracted meteorological and mechanical feature data;and a second stage: forming sample data by using the feature data obtained in the first stage and the icing level in the original image data, training a PR-KELM model by using the training data, testing the trained PR-KELM model by using the test data, and finally obtaining a power transmission line icing thickness prediction model. The method has the advantages that the PR-KELM model is adoptedto predict the icing thickness, selection of the learning rate is not very sensitive, the method is not prone to falling into a local optimal solution, and therefore the accuracy of the prediction model is improved.

Description

technical field [0001] The invention relates to the technical field of electric power system disaster early warning, in particular to a modeling method of a PR-KELM-based transmission line ice thickness prediction model. Background technique [0002] At present, algorithms commonly used in transmission line icing thickness prediction models include neural networks, support vector machines, decision trees, and improved versions of these models, but these algorithms only consider a single category of monitoring data, that is, they do not consider Extract features from the data. In fact, image data is often rich in information, but the level and scale used in common current models are very low. In addition, mechanical data and meteorological data have the characteristics of high dimensionality, nonlinearity, and heterogeneity. The existing prediction models for transmission line ice thickness do not take these characteristics into consideration when extracting features, which ...

AI Technical Summary

Problems solved by technology

[0002] At present, algorithms commonly used in transmission line icing thickness prediction models include neural networks, support vector machines, decision trees, and improved versions of these models, but these algorithms only consider a single category of monitoring data, that is, they do not consider Extract features from the data

In fact, image data is often rich in information, but the level and scale used in common models are very low

In addition, mechanical data and meteorological data have the characteristics of high dimensionality, nonlinearity, and heterogeneity. The existing prediction models for transmission line ice thickness do not take these characteristics into consideration when extracting features, which leads to the accuracy of the final prediction model. rate is not high

Images