基于5G与CNN的智能电网稳定性预测

随着5G通信技术的研究以及新型基础设施的建设, 智能电网得到了快速发展. 同时, 在大数据时代, 万物互联导致海量的设备接入电力网络, 也给智能电网带来了较大的负担, 电力网络的稳定性问题亟待解决. 因此, 本文提出了一种基于CNN的智能电网稳定性预测算法, 通过收集电力网络产生的数据, 经过CNN模型的处理, 最后输出智能电网稳定性的判别结果. 经过仿真验证, 该算法与SVM、AdaBoost, 随机森林相比, 具有较高的准确率; 同时, 本文采用了4种不同的优化算法去改善CNN模型, 带有动量的SGD算法可以达到98.13％预测准确度, 利用该模型可以有效帮助电力系统对未知的问题提前预警, 降低了安全隐患并减少了电力事故的发生.

Stability Prediction of Smart Grid Based on 5G and CNN

The research on 5G communication technology and the construction of new infrastructure has witnessed the rapid development of the smart grid. In the era of big data, the Internet of everything leads to the access of massive equipment to the power network, which also brings a great burden to the smart grid, and the stability problem of the power network is urgent to be solved. Therefore, we propose a prediction algorithm for smart grid stability based on Convolutional Neural Network (CNN). It collects the data generated by the power network, processes them in the CNN model, and finally outputs the judgment results of smart grid stability. The simulation results show that the algorithm has higher accuracy than SVM, AdaBoost, and random forest. Furthermore, four different optimization algorithms are used to improve the CNN model. SGD algorithm with momentum can achieve a prediction accuracy of 98.13%. The proposed model can effectively help the power system to pre-warn the unknown problems, reducing the security risks and power accidents.