Abstract
This paper proposes an early warning mechanism of network traffic based on quantum adaptive particle swarm optimization-radial basis function (QAPSO-RBF) neural network. The QAPSO-RBF neural network prediction model predicts the range of traffic threshold in order to detect abnormal traffic firstly. Then an intelligent substation operating state evaluation model is proposed. By analyzing the real-time traffic, a stable state of the current intelligent substation communication network is obtained. The early warning mechanism of intelligent substation abnormal traffic can effectively detect abnormal traffic and warn in the intelligent substation communication network. The experimental results show the effectiveness of the warning mechanism in intelligent substation traffic and high accuracy of the QAPSO-RBF model.
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