Model updating approach for icing forecast of transmission lines by using particle swarm optimization

Abstract

Abstract. With the purpose of predicting icing of transmission lines, a model updating approach is presented in this study. The changes of structural dynamic response of transmission lines that is caused by icing is studied firstly by using finite element method. Then, model updating method and particle swarm optimization is implemented to indentify the thickness of icing according to the alternation of natural frequencies. The results show that the proposed methodology is meaningful to monitor line icing. IntroductionIcing of transmission lines would cause line galloping, insulator flashover and even tower collapse which is a great threat for the operation of power grid [1-2]. Thus, effective monitoring and condition assessment of line icing is the key issue which needs to be solved, and the establishment of icing forecast plays an important role in the operation of power grid.It's very difficult to evaluate the condition of lines manually considering that transmission lines are located sparsely. Hence, the on-line monitoring technique has attracted increasing attentions in engineering application. The current monitoring strategy are mainly based on measuring the weight, angle of insulator, wind speed, temperature and humidity. The equivalent thickness of line icing can be estimated according to measured data [3][4][5], based on that, related staff will be alerted as long as the thickness evaluated beyond a predefined value. However, the actual icing condition is usually distributed non-uniformly along the length of line, thus, the monitoring technique based on the evaluation of equivalent thickness is unable to get more information of icing distribution in details. In this work, a methodology for icing forecasting based on model updating method is presented. The modal frequencies are introduced to identify the icing condition of transmission lines with the help of artificial intelligence technique, and the results show that the proposed approach is able to identify and predict the distribution of icing.

The General Regression Neural Network Based on the Fruit Fly Optimization Algorithm and the Data Inconsistency Rate for Transmission Line Icing Prediction

“…The transmission line ice coating can cause many types of accidents, including those related to flashover performance of the ice-covered insulator, breakage of the ground line, and the collapse of the tower [1]. They seriously affect the stability and security of power system operation.…”