Application of Improved Wavelet Neural Network in Traffic Prediction of Smart Substation

Abstract

In recent years, as the structure of smart substation network changes, the prediction of high-quality network traffic becomes more and more important. Once the traffic is abnormal, it will affect the reliability and real-time relay protection device. In this paper, the wavelet transform and BP neural network algorithm are combined to construct and analyze the wavelet neural network model. Based on this, the improved particle swarm algorithm is used to replace the gradient descent training method of wavelet neural network to improve the wavelet neural network Easy to fall into the plight of the minimum, improve the convergence rate. Finally, taking the network traffic data of station-level switch in a smart substation as an example, the simulation is carried out based on the collected original frequency data. The experimental simulation shows that the improved particle swarm optimization wavelet neural network model prediction accuracy and convergence rate is better than the traditional model, thereby improving the accuracy and speed of smart substation network traffic forecasting.