基于优化时谱图神经网络的电力系统多元混沌时间序列预测

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (11) : 156-162.

论文

卢英东,韦笃取

作者信息 +

Chaotic time series prediction of power system by using optimized time spectrum neural network

LU Yingdong, WEI Duqu

Author information +

文章历史 +

摘要

电力系统是强耦合、多变量系统，对其多元混沌时间序列预测是当前研究难点。本文提出了一种基于优化的时谱图神经网络，用于电力系统的混沌预测。首先利用潜在相关层挖掘多元时间序列之间的相关性，然后通过序列转换单元将时间序列转换为频域信号并学习其特征，最后结合多种算法优化模型实现更好的预测效果。实验表明经优化后的时谱图神经网络不仅能对电力系统的多状态变量进行混沌预测，而且比其他参考模型具有更高的预测精度和稳定性。

Abstract

Power system is a strong coupling and multivariable system, and the prediction of its multivariate chaotic time series is a difficult problem at present. In this paper, a time spectrum neural network based on optimization is proposed for chaos prediction of power system. Firstly, the potential correlation layer is used to mine the potential correlation between multivariate time series, and then the time series are converted into frequency domain signals through the sequence conversion unit to learn their characteristics. Finally, a variety of algorithms are combined to optimize the model to achieve better prediction effect. Experimental results illustrated that the optimized time spectrum neural network can not only predict the multivariable chaos of power system, but also has higher prediction accuracy and stability than other baseline models.

导出引用

卢英东,韦笃取. 基于优化时谱图神经网络的电力系统多元混沌时间序列预测[J]. 振动与冲击, 2023, 42(11): 156-162

LU Yingdong, WEI Duqu. Chaotic time series prediction of power system by using optimized time spectrum neural network[J]. Journal of Vibration and Shock, 2023, 42(11): 156-162

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