Transmission Line Galloping Prediction Based on GA-BP-SVM Combined Method

Abstract

The paper proposes a method to construct a model for transmission line galloping prediction using machine learning algorithms to address the transmission line galloping problem, which can result in transmission line loss and pose a greater risk to the safety of electricity in society. First, to reduce sensor noise interference, a unilateral sliding time window is used for micro-meteorological data correction, and then gray correlation analysis method and the specific gravity method are used to obtain the influence weights of micro-meteorological elements. The galloping prediction models are constructed using six algorithms, with the GA-BP algorithm model and the SVM algorithm model having better prediction effects based on performance metrics. The GA-BP-SVM combined model is constructed on this basis, and all of its performance metrics are optimal. This model’s prediction accuracy in both galloping and no galloping states reaches 95.5%; the probability of correct prediction when predicted as galloping reaches 95.1%; the probability that actual galloping can be predicted reaches 92.5%. The F1-score of the combined model reaches 0.938, which indicates that it has the best prediction effect. The prediction method described in the paper is accurate and practical, and operation and maintenance personnel can flexibly develop inspection strategies and anti-galloping measures based on the prediction results to ensure the safe and stable operation of transmission lines.

Keywords

• Transmission line galloping
• combined prediction
• sliding time window
• SVM
• GA-BP neural network