可再生能源2025，Vol.43Issue(3)：408-415,8.
基于改进卷积神经网络的新能源并网短路电流预测技术
Short circuit current prediction technology for new energy connected to the power grid based on improved convolutional neural network
摘要
Abstract
With the large-scale integration of distributed power sources,the short-circuit current characteristics of large power grids become more complex and difficult to predict.Based on this,this article proposes a new energy grid short-circuit current prediction technology based on improved convolutional neural networks.Firstly,analyze the characteristics of short-circuit current,perform variational mode decomposition on short-circuit current,and obtain the intrinsic mode function;Secondly,the convolutional neural network is improved by utilizing multi-scale feature extraction to maximize the features of current fault data,introducing attention mechanisms to extract important information,and using skip connections during the convolutional process to prevent information loss during forward transmission,which is beneficial for improving the accuracy of prediction.A short-circuit current prediction model based on the improved convolutional neural network is constructed;Finally,the PSCAD/EMTDC power grid model was validated,and the experimental results showed that the proposed method has high accuracy in predicting the peak short-circuit current.Compared with common limit learning machines and support vector machines,the average relative error decreased by 0.61%and 1.09%,respectively.This verified the effectiveness of the proposed method and laid the foundation for limiting short-circuit current in large power grids.关键词
新能源/改进卷积神经网络/短路电流预测/变分模态分解/注意力机制Key words
new energy/improving convolutional neural networks/short circuit current prediction/variational mode decomposition/attention mechanism分类
能源与动力引用本文复制引用
于琳琳,蒋小亮,贾鹏,孟高军,丁咚..基于改进卷积神经网络的新能源并网短路电流预测技术[J].可再生能源,2025,43(3):408-415,8.基金项目
江苏省重点研发计划(BE2021094). （BE2021094）
