随着现代电力系统规模不断扩大，电力系统的结构与运行方式日趋复杂，对电力系统进行实时、准确的状态估计至关重要。对此提出了一种基于双重注意力卷积双向长短期记忆神经网络（dual attention convolutional bidirectional long and short-term memory neural networks，DA-CNN-BiLSTM）的电力系统状态估计方法。该模型引入通道注意力机制自适应调整卷积神经网络（convolutional neural network，CNN）特征通道的权重，通过引入特征注意力机制动态分配单一特征权重输入双向长短期记忆神经网络（bi-long short-term memory neural network，BiLSTM），从时空特征中根据获取的重要性量度筛选重要特征，动态挖掘量测量与状态量之间的相关性。通过获得的历史数据构建量测数据集建立了DA-CNN-BiLSTM状态估计模型，将获得的实时量测数据输入建立的状态估计模型中，即可得到实时的状态估计结果。通过IEEE标准系统的算例分析表明，相比WLS、SE-CNN与FA-BiLSTM状态估计方法，所提方法的状态估计结果具有更优的估计精度、鲁棒性和计算效率。

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 inputing 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.