The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA‐WNN. A comparative analysis of the experimental results shows that the performance of the IGSA‐WNN‐based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution; thus, it is a better smart substation network traffic prediction model.

中文翻译：

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基于IGSA-WNN的智能变电站网络流量预测模型

智能变电站的网络流量预测是加强其系统安全保护的关键。为了提高流量预测的性能，本文提出了一种改进的引力搜索算法（IGSA），然后将IGSA引入小波神经网络（WNN），对初始连接权重，可伸缩性因子和移位因子进行迭代优化。 ，并基于IGSA-WNN建立智能变电站网络流量预测模型。对实验结果的比较分析表明，基于IGSA-WNN的预测模型的性能进一步提高了收敛速度和预测精度，并且该模型解决了原始WNN的不足问题，例如收敛速度慢和易用性陷入局部最优解；从而，