摘要
针对实测的元胞负荷数据中存在随机波动现象而使空间负荷预测精度降低的问题,提出一种利用互补集合经验模态分解(CEEMD)和游程检验技术确定元胞负荷合理最大值的方法。该方法通过互补集合经验模态分解技术将各Ⅰ类元胞负荷序列分别进行分解,每个Ⅰ类元胞得到一组本征模态函数,采用游程检验技术对每个本征模态函数进行随机性检验,建立识别其中高频分量的判据,剔除刻画元胞负荷随机波动性的高频本征模态函数,对余下表征元胞负荷规律性与趋势性的本征模态函数进行重构得到主体分量,将其中最大值作为Ⅰ类元胞负荷合理最大值,最后利用该合理最大值进行基于Ⅰ类元胞和Ⅱ类元胞的空间负荷预测。工程实例表明了该方法正确有效。
Aiming at the problem that the spatial load forecasting accuracy is reduced due to the random fluctuation in the measured cellular load data, a method for ascertaining the reasonable maximum value of cellular load by using complementary ensemble empirical mode decomposition and runs test technique is proposed. The method decomposes each class Ⅰ cellular load sequence by complementary ensemble empirical mode decomposition technique. Each class Ⅰ cell obtains a set of intrinsic mode functions.Random test of each intrinsic mode function is carried out by using runs test technique, and the criterion for identifying the highfrequency component is established. The high-frequency intrinsic mode function that characterizes the random fluctuation of the cellular load is removed, and the remaining intrinsic mode functions that characterize the regularity and trend of the cellular load are reconstructed to obtain the main component, and the maximum value is taken as the reasonable maximum value of the class Ⅰcellular load. Finally, the reasonable maximum value is used to predict the space load based on class Ⅰ cells and class Ⅱ cells. The engineering example shows that the method is correct and effective.
作者
肖白
梁雪峰
姜卓
牛湘智
牛强
李介夫
XIAO Bai;LIANG Xuefeng;JIANG Zhuo;NIU Xiangzhi;NIU Qiang;LI Jiefu(School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,China;College of Computer Science and Technology,Beihua University,Jilin 13202,China;Jilin Power Supply Company of State Grid Jilin Electric Power Supply Company,Jilin 132001,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2020年第6期194-199,共6页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51177009)
吉林省产业创新专项基金资助项目(2019C058-7)
吉林省教育厅科技项目(JJKH20180442KJ)。
关键词
空间负荷预测
互补集合经验模态分解
游程检验
随机波动
本征模态函数
spatial load forecasting
complementary ensemble empirical mode decomposition
runs test
random fluctuation
intrinsic mode function