| 引用本文: | 李洪乾,韩松,周忠强.利用Rayleigh熵和并行计算的大规模电网异常负荷快速识别[J].电力系统保护与控制,2019,47(23):37-43.[点击复制] |
| LI Hongqian,HAN Song,ZHOU Zhongqiang.Efficient abnormal load identification in large-scale power system employing Rayleigh quotient and parallel computing technology[J].Power System Protection and Control,2019,47(23):37-43[点击复制] |
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| 摘要: |
| 为提升大规模电网应用场景下的计算效率和适应性，提出了一种利用Rayleigh熵和并行计算技术的异常负荷识别方法。首先分别构造大规模电网中各分区在负荷随机波动及噪声干扰情况下的数据源矩阵，继而构建其窗口矩阵和标准矩阵，进而形成各分区的样本协方差矩阵。其次，利用并行计算技术，采用Rayleigh熵同步快速估计各分区的MESCM指标。最后，通过对该指标进行越限判别，实现对大规模电网异常负荷的快速识别。借助Matlab R2014a和PST软件，案例分析在一个IEEE54机118母线标准系统和一个420机2736母线波兰系统中展开。与传统随机矩阵理论计算方法的计算结果比较表明了所提方法的有效性和高效性。 |
| 关键词: 大规模电网 MESCM Rayleigh熵 并行计算 异常负荷识别 |
| DOI：10.19783/j.cnki.pspc.181621 |
| 投稿时间：2018-12-31修订日期：2019-05-06 |
| 基金项目:国家自然科学基金项目资助(51567006);贵州省普通高等学校科技拔尖人才支持计划(2018036);贵州省科学技术基金(黔科合基础[2019]1100) |
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| Efficient abnormal load identification in large-scale power system employing Rayleigh quotient and parallel computing technology |
| LI Hongqian,HAN Song,ZHOU Zhongqiang |
| (School of Electrical Engineering, Guizhou University, Guiyang 550025, China) |
| Abstract: |
| An efficient abnormal load identification method in large-scale power system employing Rayleigh quotient and parallel computing technology is proposed for improving the efficiency of abnormal load identification and the adaptability in large-scale power system. It firstly constructs a group of data source matrices with random load fluctuation and noise interference for each area, respectively. Then their standard matrices would be obtained through using a moving window matrix. Consequently, the sample covariance matrices of area can be formed. Secondly, the MESCM of area could be estimated rapidly with the help of the Rayleigh quotient and the parallel computing technology. In this way, the rapid identification of abnormal load in large-scale power system may be achieved by the violation checking of MESCM. The case studies have been carried on an IEEE 54-machine and 118-bus system and a Polish 420-machine and 2736-bus system utilizing Matlab R2014a and PST software. The results in comparison with those results from the traditional mean spectral radius based method show that the proposed methodology is valid and efficient. 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 Science and Technology Fund of Guizhou Province (No. [2019]1100). |
| Key words: large-scale power system MESCM Rayleigh quotient parallel computing abnormal load identification |
