基于随机矩阵理论与熵理论的电网薄弱环节辨识方法
发布时间:2019-01-27 20:24
【摘要】:电网薄弱环节辨识对保证电力系统的安全性有重要的意义。为了分析辨识电网薄弱环节,提出一种随机矩阵理论与熵理论相结合的辨识方法。首先介绍随机矩阵理论基本原理和薄弱环节特征。然后利用电压数据和相角数据构建矩阵,结合随机矩阵理论分析矩阵的统计特性,并将统计特性与电网物理特性对比分析。再结合熵理论建立薄弱节点辨识模型,利用变异系数量化分析数据波动特征,构建薄弱支路辨识模型。最后,利用IEEE39节点系统模型验证方法的正确性。
[Abstract]:It is very important to identify the weak link of power system to ensure the security of power system. In order to analyze the weak links of power system, a new identification method combining random matrix theory and entropy theory is proposed. Firstly, the basic principle and weak link characteristics of stochastic matrix theory are introduced. Then the matrix is constructed by using voltage data and phase angle data, and the statistical characteristics of the matrix are analyzed by combining the stochastic matrix theory, and the statistical characteristics are compared with the physical characteristics of the power network. Then the weak node identification model is established based on entropy theory, and the weak branch identification model is constructed by quantifying the variation coefficient to analyze the fluctuation characteristics of the data. Finally, the correctness of the method is verified by using the IEEE39 node system model.
【作者单位】: 中国电力科学研究院;
【基金】:国家电网公司科技项目(XT71-15-056)~~
【分类号】:TM711;TM732
本文编号:2416664
[Abstract]:It is very important to identify the weak link of power system to ensure the security of power system. In order to analyze the weak links of power system, a new identification method combining random matrix theory and entropy theory is proposed. Firstly, the basic principle and weak link characteristics of stochastic matrix theory are introduced. Then the matrix is constructed by using voltage data and phase angle data, and the statistical characteristics of the matrix are analyzed by combining the stochastic matrix theory, and the statistical characteristics are compared with the physical characteristics of the power network. Then the weak node identification model is established based on entropy theory, and the weak branch identification model is constructed by quantifying the variation coefficient to analyze the fluctuation characteristics of the data. Finally, the correctness of the method is verified by using the IEEE39 node system model.
【作者单位】: 中国电力科学研究院;
【基金】:国家电网公司科技项目(XT71-15-056)~~
【分类号】:TM711;TM732
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