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OA

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.