低秩矩阵分解在母线坏数据辨识与修复中的应用
发布时间：2018-12-08 14:37
【摘要】：母线负荷分析与预测对电力系统的安全稳定具有重要意义。目前我国采集到的母线负荷数据中含有较多且类型不同的坏数据,给母线负荷的分析的准确性与预测的精确性带来较大影响。文中提出了一种基于低秩矩阵分解的母线坏数据辨识与修复方法。从母线数据本身出发,首先分析了母线数据的低秩特性,研究不同类型坏数据产生的原因;然后建立了一种基于低秩矩阵分解的母线坏数据辨识与修复的模型,并给出了基于阈值迭代法(iterative thresholding,IT)的模型求解方法;最后,利用广东省母线负荷实际算例进行了分析,并利用修复前后的母线数据进行虚拟预测对比,结果实现了坏数据的有效恢复和预测精度的提高。
[Abstract]:Busbar load analysis and forecasting is of great significance to the safety and stability of power system. At present, the busbar load data collected in our country contain more bad data of different types, which has a great impact on the accuracy of bus load analysis and prediction accuracy. In this paper, a method for identifying and repairing bad busbar data based on low rank matrix decomposition is proposed. Starting from the bus data itself, this paper first analyzes the low rank characteristic of the bus data, and studies the causes of different types of bad data. Then a model of bad busbar data identification and repair based on low rank matrix decomposition is established, and the method of solving the model based on threshold iteration (iterative thresholding,IT) is given. Finally, the actual calculation example of bus load in Guangdong Province is used to analyze, and the virtual prediction comparison is made by using the bus data before and after repair. The result realizes the effective recovery of bad data and the improvement of prediction accuracy.
【作者单位】： 电力系统及发电设备控制和仿真国家重点实验室(清华大学电机系);
【基金】：国家重点研发计划(2016YFB0900101) 国家杰出青年基金项目(51325702)~~
【分类号】：TM714;TM715
本文编号：2368523
[Abstract]:Busbar load analysis and forecasting is of great significance to the safety and stability of power system. At present, the busbar load data collected in our country contain more bad data of different types, which has a great impact on the accuracy of bus load analysis and prediction accuracy. In this paper, a method for identifying and repairing bad busbar data based on low rank matrix decomposition is proposed. Starting from the bus data itself, this paper first analyzes the low rank characteristic of the bus data, and studies the causes of different types of bad data. Then a model of bad busbar data identification and repair based on low rank matrix decomposition is established, and the method of solving the model based on threshold iteration (iterative thresholding,IT) is given. Finally, the actual calculation example of bus load in Guangdong Province is used to analyze, and the virtual prediction comparison is made by using the bus data before and after repair. The result realizes the effective recovery of bad data and the improvement of prediction accuracy.
【作者单位】： 电力系统及发电设备控制和仿真国家重点实验室(清华大学电机系);
【基金】：国家重点研发计划(2016YFB0900101) 国家杰出青年基金项目(51325702)~~
【分类号】：TM714;TM715
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1 冯平;具有分解形式的高维非线性电路唯一稳态研究[J];安徽工业大学学报;2001年02期
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