Sag Prediction of High-voltage Transmission Lines based on PSO-SVM

Sag is an important variable in the design and operation and maintenance of transmission lines. The traditional method of calculating the sagging steps is cumbersome and poor in timeliness. This paper proposes a sag prediction method based on PSO-SVM. First, A support vector machine method was introduced to achieve the maximum mining of small sample data. The PSO algorithm is introduced to achieve the goal of optimizing SVM parameters and improving the accuracy and stability. Finally, the analysis of a numerical example indicates that the prediction error can be reduced effectively by the PSO-SVM.