安军;艾士琪;刘道伟;李柏青;邵广惠;徐兴伟;王震宇;

• 1:现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学)
• 2:中国电力科学研究院有限公司
• 3:国家电网公司东北分部

摘要(Abstract)：

针对实时评估电网暂态稳定性的快速性和准确性的需求,提出一种基于故障清除后短时受扰轨迹和卷积神经网络的电力系统暂态稳定性的评估方法。该方法无需人工计算轨迹特征作为输入量,直接基于量测时序数据,利用深度学习模型的自主学习能力逐层提取隐含在短时轨迹的局部特征,构建短时受扰轨迹与稳定类别间的非线性映射关系,并引入考虑故障初期受扰程度的暂态稳定信息矩阵样本构建,以增强提取的局部特征的鲁棒性,提升模型的泛化能力,有效减少误判样本数,达到进一步提高准确率的目的。IEEE-39节点系统的仿真结果验证所提方法的有效性,并且所提方法的评估准确率比传统的暂态稳定浅层评估模型更加优越。

关键词(KeyWords)： 受扰轨迹;暂态稳定评估;深度学习;卷积神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家电网公司资助科技项目(数据驱动的大电网暂态稳定态势评估与自适应控制方法服务)~~

作者(Author): 安军;艾士琪;刘道伟;李柏青;邵广惠;徐兴伟;王震宇;

Email:

DOI: 10.13335/j.1000-3673.pst.2018.1947

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