南方电网技术2026，Vol.20Issue(5)：59-70,12.DOI:10.13648/j.cnki.issn1674-0629.2026.05.007
基于双重注意力卷积双向长短期记忆神经网络的电力系统状态估计
Power System State Estimation Based on Dual Attention Convolutional Bidirectional Long and Short-Term Memory Neural Networks
摘要
Abstract
With the continuous expansion of modern power systems and the increasing complexity of their structures and operational modes,real-time and accurate state estimation of power systems is crucial.To address this,a power system state estimation method based on dual attention convolutional bidirectional long and short-term memory neural networks(DA-CNN-BiLSTM)is proposed.This model introduces a channel attention mechanism to adaptively adjust the weights of feature channels in convolutional neural networks(CNN),and incorporates a feature attention mechanism to dynamically allocate weights for individual features before input-ing them into the bidirectional long short-term memory neural network(BiLSTM).Important features are filtered from spatiotemporal characteristics based on acquired importance metrics and the correlation between measurements and state variables is dynamically explored.By constructing a measurement dataset from historical data,the DA-CNN-BiLSTM state estimation model is established.Real-time measurement data is then fed into this model to obtain real-time state estimation results.Case studies on IEEE standard systems demonstrate that compared to WLS,SE-CNN,and FA-BiLSTM state estimation methods,the proposed method achieves superior estimation accuracy,robustness,and computational efficiency.关键词
状态估计/注意力机制/神经网络/深度学习/鲁棒性Key words
state estimation/attention mechanism/neural network/deep learning/robustness分类
信息技术与安全科学引用本文复制引用
张程,林锦平..基于双重注意力卷积双向长短期记忆神经网络的电力系统状态估计[J].南方电网技术,2026,20(5):59-70,12.基金项目
国家自然科学基金资助项目(52377088) （52377088）
福建省财政厅专项资助项目(GY-Z220230) （GY-Z220230）
福建省自然科学基金资助项目(2023J01951)). Supported by the National Natural Science Foundation of China(52377088) （2023J01951）
the Special Fund of the Fujian Provincial Finance Department(GY-Z220230) （GY-Z220230）
the Natural Science Foundation of Fujian Province(2023J01951). （2023J01951）
