基于改进深度残差网络的电力系统暂态电压稳定评估
摘要：传统的电力系统暂态电压稳定评估模型存在2方面问题：故障过程中的关键信息难以捕捉、暂态稳定样本与失稳样本不平衡导致模型对多数类样本存在倾向性。为此，提出了基于改进深度残差网络的电压稳定预警模型。首先，为了捕捉故障过程中的关键信息，在残差网络中嵌入卷积注意力模块，通过对时间通道与空间通道的双重注意力来挖掘电力系统动态轨迹中潜在的时空关系；其次，针对训练过程中模型倾向于多数类样本的问题，引入基于梯度平衡机制的损失函数来减小不平衡样本对评估结果的影响；第三，为了强化模型对数据特征的提取能力，将传统卷积核替换为非对称卷积模块。最后，通过在IEEE39节点系统上接入2种不同风电占比进行测试，进一步验证所提方法在暂态电压稳定评估中的优异性能。
Abstract:In traditional model to assess power system transient voltage stability there are defects in two aspects, i.e., difficult to capture the key information during the failure process and the imbalance between transient stability samples and unstability samples leads to the tendentiousness of the model for majority class samples. For this reason, a voltage stability forewarning model based on improved deep residual network was proposed. Firstly, to capture the key information during the failure process, a convolutional attention module was embedded in the residual network, and through the dual attention of the time channel and the space channel the potential spatiotemporal relationship in the dynamic trajectory of the power system was dug. Secondly, in allusion to problem that during the training process the model was tended to majority class samples, the gradient harmonizing mechanism-based loss function was led in to reduce the influence of imbalance samples on assessment results. Thirdly, to intensify the model’s ability of extracting data features, traditional convolution kernel was replaced with asymmetric convolution block. Finally, by means of connecting two different wind power ratios to IEEE 39-bus system, the performance of the proposed method in the assessment on transient voltage stability was verified.
标题：基于改进深度残差网络的电力系统暂态电压稳定评估
英文标题：Transient Voltage Stability Assessment of Power System Based on Improved Deep Residual Network
作者：刘浩然, 任惠, 郑至斌, 王威, 夏静, 杨金豪,
关键词：暂态电压稳定评估, 深度残差网络, 卷积注意力模块, 梯度平衡机制, 非对称卷积模块,
发表日期：2023-12-04
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