【摘要】 在电气综合能源系统运行可靠性分析中，传统数值算法在求解天然气动态时计算量庞大，难以在运行的时间尺度内有效完成动态分析。提出了一种新的方法，基于神经网络结合多尺度膨胀卷积和注意力机制，替代传统数值算法。首先利用卷积神经网络(convolutional neural network, CNN)进行特征提取，结合长短期记忆网络(long short-term memory, LSTM)捕捉时间序列特征，并通过多尺度膨胀卷积扩展感受野以及引入注意力机制提升对关键状态变化的敏感度。然后通过序列到序列的学习过程，模型能够准确捕捉相邻时间步之间的复杂映射关系，构建气网动态代理模型。最后，将气网动态代理模型与电力系统潮流模型相结合，并利用蒙特卡洛法和多态模型完成了电气综合能源系统运行可靠性的全面分析。在配网级电-气综合能源系统的实际测试中，验证结果表明，所提出的CNN-LSTM组合模型不仅能够准确模拟气网动态的复杂特性，且显著提高了计算效率，满足了大规模综合能源系统运行可靠性评估的要求。更多还原

【Abstract】 In the reliability analysis of electrical integrated energy systems, traditional numerical algorithms struggle with the high computational demands of natural gas dynamics, making timely dynamic analysis difficult. This paper presents a novel approach that replaces these algorithms with a neural network-based method using multi-scale dilated convolution and attention mechanisms. The model utilizes convolutional neural networks(CNN) for feature extraction and long short-term memory(LSTM) networks to capture time series characteristics. Multi-scale dilated convolutions expand the receptive field, while attention mechanisms enhance sensitivity to critical changes. This sequence-to-sequence learning process accurately models complex relationships between time steps, resulting in a dynamic surrogate model for the gas network.The gas network model is integrated with the power system flow model, allowing for a comprehensive reliability analysis using Monte Carlo methods and multi-state models. Tests on a distribution-level electric-gas integrated energy system show that the CNN-LSTM model not only accurately simulates gas dynamics but also significantly improves computational efficiency, meeting the reliability assessment needs of large-scale integrated energy systems.更多还原