Research on correlation factor analysis and prediction method of overhead transmission line defect state based on association rule mining and RBF-SVM

The effective assessment and prediction of the defect state of transmission lines can provide important technical support for the maintenance management of transmission lines. This paper proposes a method of correlation factors analysis and prediction for transmission line defect state based on association rule mining and RBF-SVM since the single operation parameter is often used in the analysis and prediction of transmission line defect state, and ignoring the influence of internal and external factors such as the meteorological conditions, operating conditions, etc. Firstly, according to the defect state assessment of transmission lines, based on the existing data, a characteristic library of the defect state and correlation factors is constructed by considering various relevant influencing factors. Then FP-Growth algorithm is introduced into the association rules mining, which can find the internal and external factors that have a strong association with defect, and the association rules can be used as the input feature of the prediction model, so as to avoid the influence of low association factors on the accuracy of defect state prediction. Finally, RBF-SVM was used to predict the defect state, and have a better prediction accuracy compared with three commonly used methods of the linear SVM, ANN and the decision tree. The proposed approach is illustrated by predicting the defect state of an overhead transmission line in a certain area. The results verify the effectiveness of the method and provide a certain reference for the maintenance of the transmission line. (C) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license