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Method for Real-Time Abnormal State Detection of a Distribution Network Based on Maximum and Minimum Eigenvalues

机译：基于最大和最小特征值的配电网实时异常状态检测方法

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摘要

The state analysis method of a traditional distribution network operation is strictly dependent on the physical model of itself, but it varies as the geography changes, and it is difficult to find the abnormal state of a district network on real-time, especially the sudden change caused by the distributed energy and EV load. So, a method of the abnormal state detecting for the distribution network is proposed based on the maximum and minimum eigenvalues. Firstly, a high-dimensional random matrix is established by the big data from the distribution network management system to take abnormal state detection through a real-time sliding window. Then, themaximum and minimum eigenvalues of the distribution network are gained by calculating the sample covariancematrix of the random matrix and determining the maximum and minimum eigenvalues of the latter matrix. Finally, an 1177-node testing system was taken as an example, and the simulation results showed that the proposed method could detect the abnormal state in real-time without depending on the physical model and fault type of the grid.
机译：传统配电网运行的状态分析方法严格依赖于其自身的物理模型，但随着地理位置的变化而变化，难以实时发现区域网络的异常状态，尤其是突变情况。由分布式能源和电动汽车负载引起。因此，提出了一种基于最大和最小特征值的配电网异常状态检测方法。首先，利用配电网管理系统中的大数据建立高维随机矩阵，通过实时滑动窗口进行异常状态检测。然后，通过计算随机矩阵的样本协方差矩阵并确定后者矩阵的最大和最小特征值，获得配电网的最大和最小特征值。最后，以一个1177节点的测试系统为例，仿真结果表明，该方法可以实时检测异常状态，而不必依赖电网的物理模型和故障类型。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第6期|4092701.1-4092701.8|共8页
  • 作者

    Liu Keyan; He Kaiyuan; Niu Huanna; Wang Yuzhu; Zhao Jingxiang;

  • 作者单位

    China Elect Power Res Inst, Beijing, Peoples R China;

    China Elect Power Res Inst, Beijing, Peoples R China;

    China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China;

    China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China;

    China Agr Univ, Coll Informat & Elect Engn, Beijing, Peoples R China;

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