电力系统保护与控制2025，Vol.53Issue(24)：52-64,13.DOI:10.19783/j.cnki.pspc.250075
基于深度残差网络和改进时序卷积神经网络的宽频振荡监测
Wideband oscillation monitoring based on deep residual network and improved temporal convolutional neural network
摘要
Abstract
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.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Key words
wideband oscillation/deep residual network/improved temporal convolutional neural network/attention mechanism/sliding-window monitoring引用本文复制引用
赵妍,吴昊鑫,赵宗罗,陈运,周波,李强强..基于深度残差网络和改进时序卷积神经网络的宽频振荡监测[J].电力系统保护与控制,2025,53(24):52-64,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.U24B2084). 国家自然科学基金项目资助(U24B2084) （No.U24B2084）
国网浙江省电力有限公司科技项目资助(5211HZ240001) （5211HZ240001）
