基于窥孔结构LSTM 的电力系统跳闸故障诊断
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1.国网河南省电力公司经济技术研究院, 郑州;2.河南九域腾龙信息工程有限公司;3.国网河南省电力公司经济技术研究院;4.南京信息工程大学江苏省大数据分析技术重点实验室;5.南京信息工程大学计算机学院

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国家自然科学基金项目(面上项目,重点项目,重大项目),国网河南省电力公司科技计划项目


Power System Tripping Fault Diagnosis Based on Peephole Structure LSTM
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1.Ping Zhang;2.Henan Jiuyu Tenglong Information Engineering Co., Ltd;3.ZHENG zheng;4.Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology;5.School of Computer Science, Nanjing University of Information Science &6.amp;7.Technology

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    摘要:

    跳闸是输电和配电电力系统中普遍存在的故障。近年来为了应对这种故障学术界提出了基于继电保护动作和电气元件动作的保护方法。然而,这些面向电气保护的方法在在跳闸故障处理方面存在滞后性。因此,提前预测跳闸故障对处理隐藏问题和电力恢复起着至关重要的作用。本文提出一种基于多源时序数据的电力系统跳闸故障预测方法,使用LSTM提取多元数据的时间特征,缓解了RNN在长时间序列上的梯度消失问题。模型在三层栅极上添加窥孔连接结构使得单个单元能够查看上一阶段的LSTM单元状态以此强化了网络时序记忆能力。其次,我们使用参数归一化等L2正则措施缓解故障预测中的过拟合问题对结果的影响。最后本文引入支持向量机分类器提高总体模型的泛化能力和鲁棒性。实验数据从中国国家电网的相关机构获得。实验证明与现有的数据挖掘方法相比,本文提出的方法具有分类准确性高的优点。本文在最后部分对实际应用进行了讨论,以证明其在实际场景中的可行性。

    Abstract:

    Tripping is a common fault in power transmission and distribution systems. In recent years, in order to deal with this kind of fault, academic circles have proposed protection methods based on relay protection action and electrical element action. However, these methods for electrical protection have hysteresis in handling tripping faults. Therefore, the prediction of tripping faults in advance plays a vital role in dealing with hidden problems and power recovery. In this paper, a method of power system trip fault prediction based on multi-source time series data is proposed. LSTM is used to extract the time characteristics of multivariate data, which alleviates the problem of RNN gradient disappearance on long time series. The model adds a peephole connection structure on the three-layer grid to enable a single unit to view the LSTM unit status in the previous stage, thereby strengthening the network timing memory capability. Secondly, we use L2 regularization measures such as parameter normalization to mitigate the impact of over fitting in fault prediction on the results. Finally, support vector machine classifier is introduced to improve the generalization ability and robustness of the overall model. The experimental data were obtained from relevant institutions of the State Grid of China. Experiments show that the proposed method has the advantage of high classification accuracy compared with existing data mining methods. In the last part of this paper, the practical application is discussed to prove its feasibility in the actual scene.

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张平,王鹏展,龚宁,郑征,高晶,张晓东,庄伟.基于窥孔结构LSTM 的电力系统跳闸故障诊断[J].南京信息工程大学学报,,():

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  • 收稿日期:2023-03-02
  • 最后修改日期:2023-04-20
  • 录用日期:2023-04-23
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