| 引用本文: | 周忠强,韩松.基于样本协方差矩阵最大特征值的低信噪比环境电网异常状态检测[J].电力系统保护与控制,2019,47(8):113-119.[点击复制] |
| ZHOU Zhongqiang,HAN Song.MESCM based abnormal state detection of power system in low SNR environment[J].Power System Protection and Control,2019,47(8):113-119[点击复制] |
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| 摘要: |
| 为发展基于数据驱动的电网态势感知理论与方法，基于样本协方差矩阵的最大特征值(Maximum Eigenvalue of Sample Covariance Matrix, MESCM)，提出了一种适用于低信噪比场景的电网异常状态检测方法。该方法源于随机矩阵理论，通过数据源矩阵的构造，窗口数据矩阵及其标准矩阵的构建，进而形成其样本协方差矩阵。通过该矩阵的最大特征值计算与越限判别，实现电网态势感知与预警。借助PSS/E软件，案例分析在一个IEEE 39节点系统及一个南方电网规划系统展开，涉及负荷异常跃变及三相短路接地故障。与传统平均谱半径分析法的计算结果比较表明该方法具有抗噪性能高，计算耗时少的优点，同时对于非完整性信息有一定的鲁棒性。 |
| 关键词: 随机矩阵理论 样本协方差矩阵 最大特征值 异常状态检测 信噪比 非完整信息 |
| DOI：10.7667/PSPC180537 |
| 投稿时间：2018-05-08修订日期：2018-08-02 |
| 基金项目:国家自然科学基金项目资助(51567006); 贵州省普通高等高校科技拔尖人才支持计划资助(2018036);贵州省科学技术基金(黔科合基础[2019]1100) |
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| MESCM based abnormal state detection of power system in low SNR environment |
| ZHOU Zhongqiang,HAN Song |
| (School of Electrical Engineering, Guizhou University, Guiyang 550025, China) |
| Abstract: |
| This paper proposes a novel method for abnormal state detection in low SNR environment by employing Maximum Eigenvalue of Sample Covariance Matrix (MESCM) for developing the theory and method of data-driven power grid situation awareness. Inspired by the random matrix theory, it firstly constructs a data source matrix, and obtains a moving window matrix and its standard matrix, then acquires the sample covariance matrix. In this way, the situation awareness and early warning for interconnected power systems could be achieved by MESCM calculation and its violation check. Utilizing PSS/E? software, the case studies have been carried on an IEEE 39-bus system and a planning system of China Southern Power Grid, involving two main working conditions such as abnormal load change and short circuit fault. The results show that the proposed methodology has the advantage of higher noise resistance and less computing time in comparison with the traditional mean spectral radius based method and preliminarily verifies that it would be robust under incomplete information. This work is supported by National Natural Science Foundation of China (No. 51567006), Program for Top Science & Technology Talents in Universities of Guizhou Province (No. 2018036) and Guizhou Province Science and Technology Fund (No. [2019]1100). |
| Key words: random matrix theory sample covariance matrix maximum eigenvalue abnormal state detection signal-to-noise ratio incomplete information |
