Prediction of smart substations´ network traffic based on improved particle swarm wavelet neural networks

Compared with traditional substation, smart substations has process layer network, which functions as the secondary circuit of traditional substation protection an d is actually equivalent to relay protection and automatic safety devices. Once an exception occurs in the network traffic of process layer, the reliability, rapidity and agility of relay protection action will be affected instantly. According to the characteristics of network traffic of smart substations, a network traffic prediction model, which is based on improved particle swarm wavelet neural network, is proposed in this paper to assist decision-making for the network performance analysis and prediction, network failures and virus invasion warning of smart substations. Experiments have been carried out and validated the high accuracy and fast convergence of the prediction model, which could improve the accuracy and rapidity of smart substation network traffic prediction and ensure the safe operation of grid.