摘要
宽频振荡严重威胁电网安全稳定运行。为此,提出一种基于深度残差网络(residual network,ResNet)和改进的时序卷积神经网络(improved temporal convolutional network,ITCN)的宽频振荡监测方法。首先,利用Res Net结构对宽频振荡信号在时间上进行卷积,滑动捕捉时间序列的相邻局部特征,通过残差块的堆叠实现振荡信号多尺度特征的压缩提取。然后,利用ITCN结构通过膨胀因果卷积对压缩特征进行扩展,在保证计算效率的同时逐层引入较大的感受野,进一步提取时间序列中蕴含的中长期依赖特性,两者结合实现了对全局特征的提取。最后,在TCN结构中嵌入注意力机制(Attention),对信号中重要特征进行加权分配,更好地捕捉全局模式和长期依赖特性。仿真和实测结果验证了Res Net-ITCN模型可以出色地完成宽频振荡参数检测任务并且对振荡类型进行识别,实现了对宽频振荡的监测。
Wideband oscillations pose severe threat to the safe and stable operation of power systems.To address this issue,a wideband oscillation monitoring method based on deep residual network(ResNet)and improved temporal convolutional neural network(ITCN)is proposed.First,the ResNet structure is used to convolve wideband oscillation signals,capturing adjacent local features of the time series through sliding windows.The multi-scale features of the oscillation signals are extracted and compressed by stacking the residual blocks.Then,the ITCN structure applies dilated causal convolutions to expand the compressed features,introducing progressively larger receptive fields while maintaining computational efficiency.This enables further extraction of medium-and long-term dependencies in the time series,and the combination of both networks facilitates comprehensive global feature extraction.Finally,an attention mechanism is embedded into the TCN structure to assign adaptive weights to important signal features,thereby improving the capture of global patterns and long-term dependencies.Simulation and real-world measurements verify that the ResNet-ITCN model can successfully detect wideband oscillation parameters and identify oscillation types,achieving effective wideband oscillation monitoring.
作者
赵妍
吴昊鑫
赵宗罗
陈运
周波
李强强
ZHAO Yan;WU Haoxin;ZHAO Zongluo;CHEN Yun;ZHOU Bo;LI Qiangqiang(School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,China;State Grid Zhejiang Electric Power Co.,Ltd.,Hangzhou Fuyang District Power Supply Company,Hangzhou 310000,China)
出处
《电力系统保护与控制》
北大核心
2025年第24期52-64,共13页
Power System Protection and Control
基金
国家自然科学基金项目资助(U24B2084)
国网浙江省电力有限公司科技项目资助(5211HZ240001)。
关键词
宽频振荡
深度残差网络
改进时序卷积神经网络
注意力机制
滑窗监测
wideband oscillation
deep residual network
improved temporal convolutional neural network
attention mechanism
sliding-window monitoring
