A prediction method of line galloping based on IPSO-BP neural network
To ensure the normal operation and maintenance of transmission lines under meteorological conditions prone to galloping, according to the complex mapping relationship between line galloping and meteorological conditions, the improved particle swarm optimization (IPSO) is used to optimize the BP neural network, and a line galloping prediction method based on the improved particle swarm optimization BP (IPSO-BP) neural network is proposed. Text mining technology is used to analyze the influencing factors of line galloping, and an IPSO-BP neural network model with characteristic as inputs of span, ice thickness, temperature, wind speed, wind direction, relative humidity, and the angle between wind direction and line direction is determined. The model is trained through historical line galloping data to achieve the prediction function. Comparing the accuracy and stability of the IPSO-BP neural network model with other algorithm models, the results show that this method has certain advantages. Finally, this method is used to predict the line galloping in Xiezhuang area of Henan Province, which verifies the accuracy and practicability of the method.
万方数据
维普
