Short-term wind speed forecast for the early warning of conductor flashover accident

The flashover is one of the common incidents in transmission line systems. The wind-induced swing angle of the suspension insulator string is the key critical index for flashover incident limit state function. Based on the equivalent static wind load obtained from Gust Loading Envelope method, the wind-induced swing angle of suspension insulator string could be explicitly expressed by 10-min mean wind speed and line parameters, with consideration of wind speed correlation along conductor span. Therefore, the short-term forecast of the 10-min mean wind speed trend will be of great significance to the early warning of flashover incidents. This study proposes an improved hybrid prediction model based on the secondary data decomposition technology and neural network optimized by Bat algorithm for short-term multi-step-ahead wind speed prediction. In the improved hybrid prediction model, the high-frequency components of original wind speed data will be secondary decomposed because of greater prediction error. Then, the Bat algorithm is used further to optimize the initial weight and threshold parameters of the neural network to improve prediction accuracy. The accuracy and superiority of the proposed prediction model are verified by the application example. The obtained prediction results of wind speed will be substituted into the limit state function of flashover incidents to assess the flashover risk. The results show that the improved hybrid model has better performance in the multi-step-ahead forecast and can be used for flashover incident early warning.